Handbook of Neuroscience for the Behavioral Sciences, 2 Volume Set

Preface

in the neurosciences and behavioral sciences and informs about relevant theory, methods, and research in these two increasingly synergistic disciplines. The Handbook is designed to make neuroscience accessible to psychologists and other behavioral scientists with minimal background in biology or neuroscience, while at the same time offering information, constructs and approaches that will enhance the knowledge, teaching and research of active investigators at the intersection of psychology and neuroscience. In addition, the Handbook is designed to provide an accessible background in and to highlight currently active areas within the behavioral sciences for neuroscientists, who may have a minimal background in behavioral sciences. To accomplish the dual purposes of exposing behavioral scientists to neuroscience and neuroscientists to psychology, we have adopted a unique organization in this two volume Handbook. The first volume includes a Foundations section that features a set of chapters that provide brief introductions to major questions and approaches at distinct levels of analysis. These include topics such as the logical basis of integrative neuroscience, developmental processes, comparative approaches, biological rhythms, neuropharmacology, neuroendocrinology, neuroimmunology, neuroanatomy, neuropsychology, and functional neuroimaging. These chapters are overviews that provide the reader with a basic conceptual orientation to the types of approaches and measures available and how they can be applied and interpreted. These chapters provide the basic background to allow the reader to comprehend and evaluate the subsequent chapters of the handbook, as well as the broader neuroscience literature. The subsequent sections are comprised of groups of chapters organized around major psychological themes. In Volume 1, these include: Sensation and Perception, Attention and Cognition, and Learning and Memory. This organization carries over to Volume 2, with major sections being: Motivation and Emotion, Social Processes, Psychological Disorders and Health and Aging.

The notion that 100 billion neurons give rise to human behavior proved daunting up through the twentieth century because neuroscientists were limited by existing technologies to studying the properties of single neurons or small groups of neurons. Characterizing simple neural circuits has led to an understanding of a variety of sensory processes, such as the initial stages in vision, and relatively simple motor processes, such as the generation of locomotion patterns. However, unraveling the neural substrates of more complex behaviors, such as the ability of an animal to navigate in its environment, to pay attention to relevant events in its surroundings, to perceive and communicate mental states including the beliefs and desires of others and to form and maintain interpersonal and group relationships remains one of the major challenges for the neurosciences in the twenty-first century. In contrast to more elementary behaviors, these complex behavioral processes depend on interactions within elaborate networks extending across distinct brain structures. Elucidating the neural bases of complex behaviors, therefore, may require sophisticated approaches and methods that have only recently, or have yet to be, developed. These include the ability to record electrical brain activity with multi-electrode arrays in freely behaving animals or humans, neuroimaging methods that can noninvasively monitor brain activity, and an increasing cornucopia of technologies from molecular biology and genetics that allow investigators to analyze the cellular bases of behaviors. These approaches are not only revealing the underlying neurobiology of behavior, but are establishing the foundation for an understanding for the biological bases of a variety of physical and mental health problems. As a result of these developments, the neurosciences are reshaping the landscape of the behavioral sciences, and the behavioral sciences are of increasing importance to the neurosciences, especially for the rapidly expanding investigations into the highest level functions of the brain. The Handbook of Neurosciences for the Behavioral Sciences provides an introduction to graduate students and scholars xi

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xii

Preface

Throughout the Handbook, the goal has been to integrate information across disciplines and levels of organization/ analyses, from the cellular/molecular to systems to behavioral/social levels. The organization of the Handbook thus avoids artificial dichotomies such as lower level vs. higher level processes, or neuroscience vs. behavioral science sections. Coverage of motor systems, for example, is included in the section on Attention and Cognition and discussion of somatovisceral function is included in the context of Motivation and Emotion, providing the reader with the broadest and most interdisciplinary perspectives on a given topic. Cross referencing across chapters has been emphasized, to underscore the fact that a neuroscientific perspective may illuminate connections across areas of study in psychology, where none may have traditionally been recognized.

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The intended audience for the Handbook is broad, including graduate students, psychologists and other behavioral scientists who seek knowledge and understanding about neuroscience. It is a resource for active behavioral neuroscientists as well as those with minimal background in the biological sciences. It should also be of interest to neuroscientists who want an introduction to contemporary psychological issues, presented in a neuroscientific context. None of this would be possible without the tremendous efforts and high quality of the chapter authors. We thank them all for their contributions and we hope you will find this Handbook of value in understanding the behavioral neurosciences. GARY G. BERNTSON JOHN T. CACIOPPO

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Chapter 1

Integrative Neuroscience for the Behavioral Sciences: Implications for Inductive Inference JOHN T. CACIOPPO AND GARY G. BERNTSON

Charles Darwin (1873) concluded his seminal treatise on the expression of emotions by noting the adaptive value of the structures and behaviors that are observed in humans and the utility of comparative studies to discern the origin of these structures and behaviors. Darwin’s emphasis on the biological and functional underpinnings of behavior influenced William James (1890), who opined as follows:

is associated with the behavioral element, but it does not address whether the behavioral element is caused by the neural element (Cacioppo & Tassinary, 1990). The second approach is illustrated by the case of a patient who had suffered a neurosyphilitic lesion in the front part of his brain and was being attended by the physician Paul Pierre Broca. The patient was known as “ Tan” because this was the only word he was left able to speak, but in other regards his mental processes and behavior appeared relatively normal. In a postmortem autopsy, Broca determined that Tan’s lesion was in the posterior third of the inferior frontal gyrus. This region became known as Broca’s area and was surmised to be the speech center of the brain based on the changes in behavior associated with damage to this region. This case illustrates the methodological approach of comparing differences in or manipulating neural elements to investigate their effects on cognition, emotion, or behavior—that is, the study of psychological or behavioral processes () as a function of neural processes (). In Bayesian terms, this can be specified as P(/). Stating it in this way highlights a limitation of this approach: It provides evidence that a neural element is sufficient to influence a behavioral element, but it does not address whether the neural element is necessary. Thus, these two approaches provide unique nonredundant information (Sarter, Bernston, & Cacioppo, 1996). For much of the twentieth century, the notion that 100 billion neurons gave rise to the human mind and behavior proved daunting, especially when one tried to say anything specific about this feat. To make this problem tractable, neuroscientists initially studied simple circuits and behaviors. The notion, illustrated for instance by Sherrington’s (1906) work on the integrative action of the nervous system based on his studies of spinal cord reflexes, was that fundamental principles governing the operation of neural circuits and how such mechanisms relate to behavior could be understood as well in simpler systems,

A science of the mind must reduce such complex manifestations {of behavior} to their elements. A science of the brain must point out the functions of its elements. A science of the relations of the mind and brain must show how the elementary ingredients of the former correspond to the elementary functions of the latter. (p. 28)

Several celebrated clinical cases of the nineteenth century illustrate, at least at a gross level, two distinct approaches to investigation of these elements and relationships. An Italian named Bertino suffered a head injury that left his frontal lobes partially exposed (Raichle, 2000). Angelo Mosso (1881), an Italian physiologist, observed a sudden increase in the magnitude of pulsations over the frontal lobes with the ringing of local church bells and the chiming of a clock signaling the time for required prayer. Based on these observations, Mosso posited that changes in blood flow were associated with changes in cognition. To test this hypothesis, Mosso asked Bertino to multiply 8 by 12, a task that was accompanied by an increase in brain pulsation. These observations set the stage for contemporary functional brain mapping using hemodynamic measurements (Raichle, 2000), and illustrate the general approach of manipulating cognition, emotion, or behavior to determine their effects on neural functions—() as a function of psychological or behavioral processes (). In Bayesian terms, this can be specified as P(/). Stating it in this way highlights a limitation of this approach: It provides evidence that a neural element or mechanism 3

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Integrative Neuroscience for the Behavioral Sciences: Implications for Inductive Inference

assuming that the difference between simple and complex systems could be extrapolated from an established set of theories. The organization and function of simpler circuits can inform and be informed by the study of the behavior of the more complex circuits and organisms of which they are a part, but complex circuits cannot be explained on the basis of simpler ones alone. For instance, the phenotypic expression (e.g., behavior) of strains of mice with specific genes inactivated (i.e., knockout mice) has been known to depend on the genetic background (e.g., Gerlai, 1996); the effects of the social context, in contrast, were thought to be unimportant. Crabbe, Wahlsten, and Dudek (1999) demonstrated that the specific behavioral effects associated with a given knockout could vary dramatically across environmental contexts (e.g., experimenters, testing environments, laboratories). When these authors expanded the traditional approach to include multilevel integrative analyses of genetics, neural processes, and behavior, they observed new patterns of data that were not predictable based on what was known about the component elements. Stephen Jay Gould (1985) noted, “We often think, naively, that missing data are the primary impediments to intellectual progress—just find the right facts and all problems will dissipate. But barriers are often deeper and more abstract in thought” (p. 151). This observation is especially true in the investigation of complex systems, where patterns of data rather than single data points are important to grasp. Human behavior (including mental behavior) is the most complex system science has investigated. In this endeavor, as in all complex sciences, theory and empirical investigation must proceed together, each informing the other. As William James (1890) suggested, the need for theoretical models applies to psychological and behavioral elements (), biological and neural elements (), and the relations between the former and the latter. Whether manipulating or measuring neural mechanisms, or both, mapping these changes to behavior requires that tasks be well designed, which is to say the functional component processes must be well specified by some behavioral theory and body of empirical work. Theory and research in the neurosciences, however, are just as crucial to discerning patterns of integrative relations. Being inattentive to the properties, constraints, and operating possibilities of the neural mechanisms that underlie cognition, emotion, and behavior is inefficient, at best, because one misses the opportunity to build more plausible theories and to focus efforts more precisely on the constructs and interpretations that need to be considered. The resulting theories within and bridging across the behavioral sciences and the neurosciences are themselves only evolving approximations, but they provide an important means for advancing

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understanding of the complex neural and behavioral mechanisms at work. The evolution of these theories is promoted by the open theoretical dialogue between the neurosciences and behavioral sciences. This Handbook is designed to make theory and research in the neurosciences accessible to psychologists and other behavioral scientists while also highlighting for neuroscientists currently active areas within the behavioral sciences and discussing information, constructs, and approaches at the intersection of the behavioral sciences and the neurosciences. Complex states tend to be multiply determined, and human behavior is certainly no exception. Nineteenthcentury neurologist John Hughlings Jackson emphasized the hierarchical structure of the central nervous system and the re-representation of functions at multiple levels of this neuraxis (Jackson, 1884/1958). Subsequent neuroanatomical investigations have revealed this structure to be more heterarchical than hierarchical (see Berntson et al., this volume), but Jackson’s point remains that information is processed concurrently at multiple levels of the organization within the nervous system. Primitive protective responses to aversive stimuli, for instance, exist at the level of the spinal cord, as in stereotypic flexor withdrawal responses to nociceptive stimulation. These protective reactions are expanded and embellished at higher levels of the nervous system (Berntson, Boysen, & Cacioppo, 1993). The evolutionary development of higher neural systems, such as the limbic system, endowed organisms with an expanded behavioral repertoire including escape reactions, aggressive responses, and even the ability to anticipate and avoid aversive encounters. Humans were not the first bipedal creatures or the first to use tools, but humans, apparently uniquely, contemplate the history of the earth, the reach of the universe, the origin of the species, the genetic blueprint of life, and the physical basis of their own unique mental existence. These feats are the result of evolution endowing humans not only with primitive, lower level adaptive reactions but also with the unmatched information-processing capacities of the cerebrum. At progressively higher levels of neural organization, there is a general expansion in the range and relational complexity of representational operations and contextual controls and in the breadth and flexibility of discriminative and adaptive responses (Berntson et al., 1993). The adaptive flexibility of higher level neural systems comes at a cost, however, given the finite information-processing capacity of neural circuits. Greater flexibility means a less rigid relationship between inputs and outputs; a greater range of information that must be integrated; and a slower, more serial-like mode of processing. Consequently, the evolutionary layering of higher processing levels onto lower substrates has adaptive advantage in that lower

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Mapping across Levels of Organization 5

and more efficient processing levels may continue to be expressed. The processes expressed at lower and higher levels of the neuraxis can also interact in myriad ways, as illustrated by the diabetic who can overcome the withdrawal reflex when self-injecting insulin and the professional golfer who chokes when putting on the final green with a major championship at stake. Statistical models now exist that include stochastic error terms at various hierarchical levels of aggregation that are applicable to the data matrices that span biological and behavioral levels of organization. Our goal here is not to review these statistical models (cf. Weinstein, Vaupel, & Wachter, 2008) but to provide a more generic discussion of the conceptual issues that arise when mapping between the neural and behavioral domains. The mappings of elements across levels of organization may begin as associations but must move to mechanisms to explicate the neural basis of psychological and behavioral processes. This progression can be hindered by inattention to the logic underlying inferences about the functional import of neural structures or processes simply because one is dealing with observable biological events, as doing so can yield simple and restricted descriptions of empirical relationships or erroneous interpretations of these relationships. In the remainder of this chapter, therefore, we review logical issues involved in mapping constructs across levels of organization.

MAPPING ACROSS LEVELS OF ORGANIZATION One of the simplest methods of mapping across neural () and behavioral () levels of organization is the correlative approach. There are notable success stories to illustrate this approach. For instance, Suomi and colleagues found that peer-raised monkeys, compared to monkeys raised by their biological mothers, exhibit more aggression and less grooming behaviors, and typically remain at the bottom of the social hierarchy (Suomi, 1999). These monkeys are further characterized by lower cerebrospinal fluid concentrations of 5-hydroxyindoleacetic acid (5-HIAA, a serotonin metabolite) than their mother-reared counterparts. Impulsive rhesus monkeys in the wild are also characterized by high cerebrospinal fluid 5-HIAA concentration levels and the behavioral tripartite of aggressiveness, infrequent grooming, and low standing in the social hierarchy (cf. Suomi, 1999). These correlational studies have contributed to productive research on serotonin transporter gene (5-HTT) polymorphisms, environmental influences, and behavior in monkeys and humans. Of course, not every association identified in a correlative approach proves robust or informative. The associations

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uncovered in correlative research, especially atheoretical correlative investigations, run the risk of yielding false discoveries (i.e., nonreplicable associations), and this risk increases with the number of possible associations that are examined. False discovery rate techniques have been developed to help mitigate this problem, but these techniques do not eliminate the problem (cf. Munafo et al., 2003). The development and adoption of false discovery rate methods represents an advance in dealing with Type I error rates, however, because the cost of near-zero false discovery rates is a high false-negative rate. The ratio of the number of missed small discoveries to false discoveries can be substantially greater than 1 in studies of complex behavioral outcomes, and small associations can carry large theoretical ramifications. Therefore, the cost of missing important but small associations (Type II errors) can sometimes be greater than the cost of a Type I error. Independent replication, therefore, is of paramount importance. In addition, bioinformatics tools and multivariate techniques permit a reduction in the number of measures to more meaningful functional sets of measures. For instance, microarray studies can now be performed on hundreds of thousands of gene transcripts, but the upstream transcription control pathways are typically of greater interest than the individual gene expressions in these studies. Cole, Yan, Galic, Arevalo, and Zack (2005) introduced the Transcription Element Listening System (TELiS), which combines sequence-based analysis of gene regulatory regions with statistical prevalence analyses to identify transcription factor binding motifs that are overrepresented among the promoters of up- or downregulated genes. Cortisol can regulate a wide variety of physiological processes via nuclear hormone receptor–mediated control of gene transcription. Cortisol activation of the glucocorticoid receptor exerts broad anti-inflammatory effects by inhibiting pro-inflammatory signaling pathways. In longitudinal research on middle-aged and older adults, we found that perceptions of social isolation predict higher morning rises in cortisol the following day (Adam, Hawkley, Kudielko, & Cacioppo, 2006). Social isolation is also associated with increased risk of inflammation-mediated diseases. One possible explanation for inflammation-related disease in individuals with high cortisol levels involves impaired glucocorticoid receptor–mediated signal transduction that prevents the cellular genome from effectively “hearing” the anti-inflammatory signal sent by circulating glucocorticoids (Cole et al., 2007). Consistent with this hypothesis, a systematic examination of genome-wide transcriptional alterations in circulating leukocytes using TELiS showed increased expression of genes carrying pro-inflammatory elements and decreased expression of genes carrying anti-inflammatory glucocorticoid response

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Integrative Neuroscience for the Behavioral Sciences: Implications for Inductive Inference

elements in lonely relative to nonlonely middle-aged adults (Cole et al., 2007). Impaired transcription of glucocorticoid response genes and increased activity of pro-inflammatory transcription control pathways provide a functional genomic explanation for elevated risk of inflammatory disease in individuals who chronically perceive high levels of social isolation. In sum, a strength of the correlative approach is often in identifying associations that might be replicable and worthy of further study rather than in post hoc hypothesis testing. An important goal of scientific theory is to describe the causal interrelationships among factors, thereby explicating the mechanism responsible for an association. The correlative approach may generate elements (e.g., genes, neurophysiological circuits, cognitive processes) or contextual moderators that are candidates for this causal mechanism. The correlative approach may not indicate the nature of the specificity of the association across levels of organization. For convenience, consider the constructs or measures at each level of organization as elements within a domain or set, as William James (1890) suggested. The mapping between elements across such sets can take one of the following forms (see Figure 1.1): • A one-to-one relation, such that an element in one set or level of organization is associated with one and only one element in another set, and vice versa. An example of a one-to-one relation is neurons in V1 tuned to a given stimulus feature such as line orientation (see Hubel & Wiesel, 2005). • A one-to-many relation, meaning that an element of interest in one set is associated with multiple elements in another set. An example is visual perception, which proceeds along ventral and dorsal streams (see Chapter 11). • A many-to-one relation, meaning that two or more elements in one set are associated with one element in another set. (This differs from a one-to-many relation only when the order of the mapping across levels of organization—e.g., behavioral [e.g., cognitive] to biological—is specified.) An example is the finding that a particular movement or the observation of that particular movement activates the same neurons (termed mirror neurons) in area F5 of the monkey brain (see Chapter 16). • A many-to-many relation, meaning two or more elements in one set are associated with the same (or an overlapping) subset of elements in another set. Faces and objects may differ in terms of the region of maximal response in functional magnetic resonance imaging studies, but both faces and objects are associated with activation of multiple regions in the ventral temporal lobe (e.g., fusiform gyrus, parahippocampal gyrus; Haxby et al., 2001).

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• A null relation, meaning there is no association between the specified element in a neural set and those observed in a behavioral set. Even though all elements in  map into one or more elements in , not all elements in  map into elements in , and a particular element of interest in  may not map into the set of elements known or measured in . Association studies involving elements with a one-toone relation (absent confoundings and measurement error) produce high correlations, whereas association studies involving elements characterized by a null relation yield an essentially zero correlation. The strength of the association between elements across levels of organization can vary a great deal, however, for one-to-one, one-to-many, and many-to-one mappings, and a many-to-many mapping between two elements across levels of organization can produce correlation coefficients that are quite small, making them difficult to distinguish from a null relation unless the sample size is large or one or more elements are manipulated. Thus, the initial establishment of an association between elements across levels of organization through a correlative approach is typically not sufficient to determine the specificity of the mapping.

Psychological

Biological

One-to-One

One-to-Many

Many-to-One

Many-to-Many

Null

Figure 1.1 Possible relationships between elements in two adjacent levels of organization (domains). For illustrative purposes, these domains have been labeled Psychological and Biological.

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Toward Stronger Inferences in the Interpretation of Brain–Behavior Relationships 7

Why might it be important to go beyond thinking of associations to considering the nature of the relationship between elements at different levels of organization? First, it is important if one is to move efficiently from association to the specification of mechanisms. Second, the nature of the mappings between elements at different levels of organization determines the limits of interpretation one can draw about an association. Consider research in which a biological measure (e.g., activation of the anterior cingulate cortex as measured by fMRI) is shown to covary with a behavioral task (e.g., lying; Langleben et al., 2002). This established association may then be used to justify an interpretation of differences in the neural element (i.e., activation of the anterior cingulate) as evidence of differences in the behavioral element (i.e., lying). This form of inference can be problematic, however. Even if one knew that variations in lying were associated with corresponding variations in anterior cingulate activity, inferring lying based on anterior cingulate activity ignores the possibility that other antecedent conditions could also produce variations in anterior cingulate activity. That is, it ignores the specificity of the association or mapping to the construct about which one would like to draw the inference. Such errors, in turn, can slow theoretical development. It is tempting to suggest that these issues do not apply to genetics (or brain processes) because there is no doubt that they play a causal role in the production of complex behaviors. To say that genes are causal is not equivalent, however, to specifying which gene or set of genes is associated with and causal in a particular phenotypic expression or, for that matter, to specifying the mechanism by which associated genes might influence a particular phenotype. Gottesman and Gould (2003) suggested that the number of genes involved in a phenotype is directly related to both the complexity of the phenotype and the difficulty of genetic analysis (see also Butcher, Kennedy, & Plomin, 2006). Although difficult to discern, such causal linkages will be more easily resolved if attention is paid to the implications of the many-to-many mapping problem. The mapping between elements across levels of organization may become more complex (e.g., many-to-many) as the number of intervening levels of organization increases. The exception to this statement is when mappings among elements across adjacent levels of organization is one-to-one, but such mappings are atypical. Accordingly, the likelihood of complex and potentially obscure mappings increases as one fails to consider intervening levels of organization. Admittedly, it is not always obvious which of several levels of organization might be “adjacent,” except perhaps when level of organization refers to a temporal rather than

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spatial scope. This caveat that mapping across levels of organization may be fostered by the incremental mapping of elements between proximal levels nevertheless may have heuristic value. For instance, endophenotypes such as neurocognitive deficits have proven to be valuable explanatory constructs between genes and psychiatric diseases (e.g., Gottesman & Gould, 2003; Nuechterlein, Robbins, & Einat, 2005), and in theory the same situation should apply to any mapping that goes from neural elements to complex behaviors. For this reason, we focus here on the mappings between two adjacent levels of organization. The issues raised about the mappings between adjacent levels of organization can be extended to any number of adjacent levels of organization.

TOWARD STRONGER INFERENCES IN THE INTERPRETATION OF BRAIN–BEHAVIOR RELATIONSHIPS There is an intuitive appeal to the view that a proper understanding of the neural substrates of cognition and behavior may be couched in terms of the selective activation of regions of the brain during particular behavioral tasks. Although progress has been made in this regard, the manner in which many inferences are drawn about the behavioral significance of localized brain activity is more complex than is sometimes assumed. A major goal of neuroscientific studies of behavior can be expressed as   f(). That is, ultimately one wishes to specify the biological mechanisms responsible for various behavioral (including mental) phenomena. Contrasting tasks that are thought to differ in only one or more cognitive or behavioral operations () between the associated neural events () are sometimes interpreted as showing that neural structure (or process)  is associated with behavioral operation . These data are also sometimes treated as revealing much the same information that would have been obtained had neural structure (or process)  been stimulated or ablated and a consequent change in behavioral function  observed. This form of interpretation reflects the explicit assumption that there is a fundamental localizability of specific behavioral operations and the implicit assumption that there is an isomorphism between  and . Such observations may represent a starting point for a series of internally consistent propositions that lead to a general conclusion, but problems with the logic of this approach can lead this conclusion astray (Cacioppo & Tassinary, 1990). In particular, research has shown that when a behavioral element varies when a neural element is manipulated (or vice versa), this does not necessarily imply the existence of an isomorphism between these elements (Sarter, et al., 1996).

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Integrative Neuroscience for the Behavioral Sciences: Implications for Inductive Inference

For instance, causal hypotheses regarding a specific neural structure or process () underlying a cognitive or behavioral operation () are of the form   f(). They necessarily imply that  is always followed by  but do not necessarily imply that  is always preceded by . This is what it means for  to have multiple (parallel) determinants. Furthermore, brain events () may be of interest to the extent that they index a cognitive operation or state so that the inferences are of the form   f() rather than not   (not ). Thus, one important aim of neural measurements can be specified by the conditional probability of  given , or P(/)  1. The typical structure of investigations in which neural measurements are made, in contrast, can be written as the conditional probability of  given , or P(/)  x. For example, brain imaging techniques provide information about  as a function of , but the conditional probabilities P(/) and P(/) are not equivalent unless there is a 1:1 relationship between brain structure  and cognitive function . Most of the early discoveries of functional locationism that have had lasting impact involved the ablation or direct electrical stimulation of specific nuclei (i.e., P[/]), whereas the evidence for Gall’s localization theory was based primarily on relating cranial features to extreme behaviors (i.e., P[/]), which was interpreted to mean that individuals with these cranial features were destined toward these behaviors (i.e., P[/]). This latter interpretation does not follow from the form of the data on which it was based because P(/) does not equal, or even approximate, P(/) unless there is an isomorphism between  and . Careful attention to the structure of inference, therefore, may contribute to a more productive dialogue between neuroscientists and behavioral scientists. Approaches such as stimulation and ablation studies and brain imaging research provide complementary rather than redundant information about the relationship between brain structures (or events) and cognitive functions. This is because stimulation and ablation studies bear on the relationship P(/), whereas brain imaging studies provide information about P(/). Despite the formal parallelism between these expressions, there is a fundamental asymmetry in the heuristic power of studies aimed at the demonstration of P(/) versus P(/). The causal role of  in process  can be examined in a straightforward fashion by direct experimental manipulation of . The loss of cognitive function by inactivation of neural processes can serve to establish  as necessary for function . Moreover, addressing a more complex avenue of research, facilitation of cognitive functions by electrical or neurochemical brain activation can further establish that  is a sufficient condition for function .

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THE IMPORTANCE OF SPECIFYING CONTEXT: TAXONOMY OF MAPPINGS Tests, assays, or measured biological responses (i.e., a physiological event that exceeds some decision threshold) have two different but related sets of characteristics. Analytic sensitivity is the ability to consistently detect very low levels of the target analyte. Stated another way, sensitivity is the true-positive rate. Considering all true (T) and false (F) outcomes of the test, for both positive (P; analyte present) and negative (N; analyte absent) conditions, sensitivity  TP/(TP  FN). In contrast, analytic specificity refers to the ability of a test to selectively detect only the target analyte and not others; specificity  TN/(TN  FP). For instance, blood sugar levels will vary in a predictable fashion for several hours after one ingests a dosage of glucose. Deviations from the normative values in blood sugar level across time mark a possible problem in metabolism because the blood glucose tolerance test (a procedure for mapping the glucose–blood sugar association) is sensitive and specific as long as the appropriate testing procedures are followed (e.g., fasting prior to the test) to eliminate the other known influences on the observed blood sugar excursions over the course of the test. This illustrates how a mapping between elements in  and  can be simplified by paying attention to potential confounding and contextual factors, by which we mean specifically P(not /) and P(not /). Furthermore, the diagnostic value of any given  (i.e., a measured response, as defined by some decision criteria such as corrected p  .05) as a measure of  depends not only on the sensitivity and specificity of the measured response but on the base rates for true positives and for true negatives. The sensitivity, defined quantitatively above, represents the true detection probability, and the specificity represents 1 minus the false detection probability. The chance that the measured response correctly indexes the targeted state is called the positive predictive value (PPV) and equals the fraction of detections that are true hits, that is, PPV  TP/(TP  FP). In contrast, the chance that the absence of this measured response (termed a negative screen in medicine) is correct is called the negative predictive value (NPV), where NPV  TN/(TN  FN). The properties of sensitivity and specificity and the PPV and NPV, of course, depend in part on the elements involved in the mapping. For instance, the adrenocortical hormone cortisol is released by the adrenal cortex under conditions of stress and hence is considered a stress hormone and is often used as a marker of stress. Assays with high sensitivity and specificity for cortisol are available and can be used to measure this hormone in plasma, urine, or saliva; these assays may provide an accurate measure

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The Importance of Specifying Context: Taxonomy of Mappings

The scientist is usually looking for invariance whether he knows it or not. Whenever he discovers a functional relation between two variables his next question follows naturally: under what conditions does it hold? In other words, under what transformation is the relation invariant? The quest for invariant relations is essentially the aspiration toward generality, and in psychology, as in physics, the principles that have wide application are those we prize. (p. 20)

Is the mapping between two elements across levels of organization universally generalizable, or is it moderated by other factors? If it is generalizable without qualification, then the association requires no attention to characteristics of the context or sample population; that is, the mapping has external validity. If external validity is absent, then the reason for this becomes a theoretically interesting question regarding P(not-/) or P(not-/). Invariant associations were once assumed, but statistical methods are now sufficiently developed to test for potential moderators (e.g., Baron & Kenny, 1986), and increasing attention is being paid to the operation of moderator variables. A taxonomy of associations between elements across levels of organization is summarized in Figure 1.2. The initial step is often to establish that variations in an element in

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Context-Bound

Context-Free

Maker

Invariant

Outcome

Concomitant

Specificity

One-to-One

Generality

Many-to-One Many-to-Many

of adrenocortical activity. This represents a proximal mapping, as the adrenal cortex is the primary source of cortisol, although other factors (e.g., clearance) can also affect the measure. When cortisol is used as a marker of stress, however, the sensitivity, specificity, PPV, and NPV may all be quite different. In this case the mapping is more distal, as there are several mediating links between stress and cortisol secretion. Although potent stressors generally yield a cortisol response, the sensitivity of a cortisol assay for stress is considerably lower, as minor stresses may not trigger a measurable cortisol response. That is, there may be many more false negatives in the equation sensitivity  TP/(TP  FN). Moreover, the selectivity of a cortisol assay for stress is also considerably lower, as other variables may also impact cortisol release. Cortisol levels vary across the day and with activity, among other variables, so there may be more false positives in the equation specificity  TN/(TN  FP). The PPV and NPV depend on the threshold used to define a response, but it should be obvious that these values, too, will be lowered by poor sensitivity and specificity. Consequently, the utility of a cortisol assay for stress may be limited to more significant stressors, and to enhance specificity, extraneous variables that can impact cortisol must be taken into account statistically. Another dimension is the generality of the mapping. In his influential Handbook of Experimental Psychology, S. S. Stevens (1951) advised the following:

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Figure 1.2 Taxonomy of mappings among elements between adjacent levels of organization.

one domain are associated with variations in an element in another, thereby establishing an association. An outcome is defined as a mapping in which multiple elements at one level of organization (e.g., biological) are related to an element at another level of organization (e.g., behavioral), and this many-to-one mapping may change across contexts. Initial association studies typically do not address issues of specificity or generality, and the treatment of such associations as invariants is premature. An invariant relationship refers to a universal isomorphic (one-to-one) mapping between elements across levels or organization (see Figure 1.2). Invariant mappings permit the inference of an element at one level of organization based on the measurement of its isomorphic element at another. A marker is defined as a one-to-one, nonuniversal (e.g., context-dependent) relationship between elements across levels of organization (see Figure 1.2). Many medical diagnostic tests that have sensitivity and specificity only if explicit procedures are followed to eliminate other influences are examples of markers. Inferences based on markers are similar to those for invariants as long as all other elements involved in the mapping are either experimentally or statistically controlled. Finally, a concomitant refers to a many-to-one but universal association between elements across levels of organization and is similar to outcomes, except that the latter is not universal. Outcome and concomitant mappings enable strong inferences to be drawn about theoretical constructs based only on hypothetico-deductive logic (Platt, 1964). Specifically, when two theoretical models differ in predictions regarding one or more outcomes or concomitants, then the logic of the experimental design allows theoretical

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10

Integrative Neuroscience for the Behavioral Sciences: Implications for Inductive Inference

inferences to be drawn about elements at one level of organization based on the measured elements in another. When a new effect or association is found not to generalize to specific contexts or individuals, concerns are typically expressed about the methodological differences between the studies. Such a finding raises several important questions, including whether the original association is replicable; and, if replicable, whether the diminution in effect size attributable to measurement issues (e.g., reliability, construct validity) or to the operation of a moderator variable. The latter is an important theoretical question. It is for this reason that careful attention to the psychometric properties of all measures, regardless of their level of organization, to ensure their reliability and validity (including construct validity) is especially important in the design and analysis of neuroscientific studies of behavioral processes.

SUMMARY Given the complexity of neural and behavioral processes, attention to the form of scientific reasoning can be especially important. Many behavioral processes are multiply determined. To the extent that this is the case, investigators who assume rather than establish an invariant relationship between elements in the behavioral and neural domains are at risk for predictably faulty interpretations. That is, the sensitivity and specificity of the mapping of neural elements into behavioral levels of organization may be context dependent, and paying attention to these issues improves the quality of inductive inferences. Interdisciplinary research that crosses neural and behavioral levels of organization raises issues about how might one productively think about concepts, hypotheses, theories, theoretical conflicts, and theoretical tests across levels of organization. Abstract constructs such as those developed by behavioral scientists provide a means of understanding highly complex activity without needing to specify each individual action of the simplest components, thereby providing an efficient means of describing the behavior of a complex system (e.g., working memory). Chemists who work with the periodic table on a daily basis nevertheless use recipes rather than the periodic table to cook, not because food preparation cannot be reduced to chemical expressions but because it is not cognitively efficient to do so. Reductionism, in fact, is one of several approaches for bettering science based on the value of data derived from distinct levels of organization to constrain and inspire the interpretation of data derived from other levels of organization. In reductionism, the whole is as important to study as are the parts, for only in examining the interplay across levels of organization can the underlying principles and mechanisms be ascertained.

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Our goal in this chapter has been to outline a simple model to aid in thinking about elements from different levels of organization and the inferences drawn from observations of these elements. Contemporary work has demonstrated that theory and methods in the neurosciences can constrain and inspire behavioral hypotheses, foster experimental tests of otherwise indistinguishable theoretical explanations, and increase the comprehensiveness and relevance of behavioral theories. Several principles further suggest that comprehensive theories of behavior will be advanced by the joint consideration of multilevel integrative analyses that span neural and behavioral processes. One that we have discussed is the principle of multiple determinism, which specifies that a target event at one level of organization, but especially at molar or abstract (e.g., cognitive or behavioral) levels of organization, can have multiple antecedents within or across levels of organization. A corollary to this principle, the corollary of proximity, is that the mapping between elements across levels of organization becomes more complex (e.g., many-to-many) as the number of intervening levels of organization increases. An important implication of this corollary is that the likelihood of complex and potentially obscure mappings increases as one skips levels of organization. The principle of nonadditive determinism specifies that properties of the whole are not always readily predictable from the properties of the parts. For instance, the behavior of nonhuman primates following the administration of amphetamine or placebo can appear similar unless each primate’s position in the social hierarchy is considered. When this behavioral factor is taken into account, amphetamine is found to increase dominant behavior in primates high in the social hierarchy and to increase submissive behavior in primates low in the social hierarchy. Thus, the effects of physiological changes on behavior can appear unreliable until the analysis is extended across levels of organization. A strictly physiological (or behavioral) analysis, regardless of the sophistication of the measurement technology, may not reveal the orderly relationship that exists. The emergence of an orderly pattern of data when spanning levels of organization is one of the unique opportunities of neuroscientific investigations of behavior. Finally, the principle of reciprocal determinism specifies that there can be mutual influences between microscopic (e.g., biological) and macroscopic (e.g., social) factors in determining behavior. For example, not only has the level of testosterone in nonhuman male primates been shown to promote sexual behavior, but the availability of receptive females influences the level of testosterone in nonhuman primates. These principles illustrate that the mechanisms underlying mind and behavior may not be fully explained

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References 11

by a biological or a behavioral approach alone but rather may require a multilevel integrative analysis. The contributions to this volume are designed with this in mind.

Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001, September 28). Distributed and overlapping representations of face and objects in ventral temporal cortex. Science, 293, 2425–2430. Hubel, D. H., & Wiesel, T. N. (2005). Brain and visual perception: The story of a 25-year collaboration. Oxford, England: Oxford University Press.

REFERENCES Adam, E. K., Hawkley, L. C., Kudielka, B. M., & Cacioppo, J. T. (2006). Day-to-day dynamics of experience-cortisol associations in a population-based sample of older adults. Proceedings of the National Academy of Sciences, 103, 17058–17063. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. Berntson, G. G., Boysen, S. T., & Cacioppo, J. T. (1993). Neurobehavioral organization and the cardinal principle of evaluative bivalence. Annals of the New York Academy of Sciences, 702, 75–102. Butcher, L. M., Kennedy, J. K. J., & Plomin, R. (2006). Generalist genes and cognitive neuroscience. Current Opinion in Neurobiology, 16, 1–7. Cacioppo, J. T., & Tassinary, L. G. (1990). Inferring psychological significance from physiological signals. American Psychologist, 45, 16–28. Cole, S. W., Hawkley, L. C., Arevalo, J. M., Sung, C. Y., Rose, R. M., & Cacioppo, J. T. (2007). Social regulation of gene expression in human leukocytes. Genome Biology, 8(9), R189.

James, W. (1890). The principles of psychology (Vol. 1). New York: Holt. Langleben, D. D., Schroeder, L., Maldjian, J. A., Gur, R. C., McDonald, S., Ragland, J. D., et al. (2002). Brain activity during simulated deception: An event-related functional magnetic resonance study. Neuroimage, 15, 727–732. Mosso, A. (1881). Ueber den Kreislauf des Blutes im menschlichen Gehirn [About the circulation of the blood in the human brain]. Leipzig, Germany: Veit. Munafo, M. R., Clark, T. G., Moore, L. R., Payne, E., Walton, R., & Fint, J. (2003). Genetic polymorphisms and personality in healthy adults: A systematic review and meta-analysis. Molecular Psychiatry, 8, 471–484. Nuechterlein, K. H., Robbins, T. W., & Einat, H. (2005). Distinguishing separable domains of cognition in human and animal studies: What separations are optimal for targeting interventions? Schizophrenia Bulletin, 31, 870–874. Platt, J. R. (1964, October 16). Strong inference. Science, 146, 347–353.

Cole, S. W., Yan, W., Galic, Z., Arevalo, J., & Zack, J. A. (2005). Expressionbased monitoring of transcription factor activity: The TELiS database. Bioinformatics, 21, 803–810.

Raichle, M. E. (2000). A brief history of human functional brain mapping. In A. W. Toga & J. C. Mazziotta (Eds.), Brain mapping: The systems (pp. 33–77). San Diego, CA: Academic Press.

Crabbe, J. C., Wahlsten, D., & Dudek, B. C. (1999, June 4). Genetics of mouse behavior: Interactions with laboratory environment. Science, 284, 1670–1672.

Sarter, M., Berntson, G. G., & Cacioppo, J. T. (1996). Brain imaging and cognitive neuroscience: Towards strong inference in attributing function to structure. American Psychologist, 51, 13–21.

Darwin, C. (1873). The expression of the emotions in man and animals. New York: Appleton.

Sherrington, C. S. (1906). The integrative action of the nervous system. New York: Scribner ’s.

Gerlai, R. (1996). Gene-targeting studies of mammalian behavior: Is it the mutation or the background genotype? Trends in Neurosciences, 19, 177–181.

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Gottesman, I. I., & Gould, T. D. (2003). The endophenotype concept in psychiatry: Etymology and strategic intentions. American Journal of Psychiatry, 160, 636–645. Gould, S. J. (1985). The flamingo’s smile: Reflections in natural history. New York: Norton.

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Jackson, J. H. (1958). Evolution and dissolution of the nervous system (Croonian Lectures). In J. Taylor (Ed.), Selected writings of John Hughlings Jackson. New York: Basic Books. (Original work published 1884.) Vol 2, pp. 3–92.

Suomi, S. (1999). Attachment in rhesus monkey. In J. Cassidy & P. Shaver (Eds.), Handbook of attachment: Theory, research, and clinical applications (pp. 181–197). New York: Guilford Press. Weinstein, M., Vaupel, J. W., & Wachter, K. W. (2008). Biosocial surveys. Washington, DC: The National Academies Press.

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Chapter 2

Developmental Neuroscience MYRON A. HOFER

revealed how changes in the expression patterns of genes provide a common basis for both developmental and evolutionary changes (S. Carroll, 2005). Developmental biology (as embryology) had long been isolated from evolutionary biology, and most scientists thought that differences between species were accounted for by differences between those species’ genes. But with the advent of rapid gene sequencing in the past few years, it is now appreciated that differences between species lie in how a common set of highly conserved genes is regulated during development to produce the extraordinary differences in animal body plans and behaviors ranging from those of flies and worms to humans. Evolution is now viewed as taking place through changes in the processes and course of development, and development, in turn, is now seen as a major source of novelty and of variation between individuals upon which selection can act in the course of evolution. Rapid progress in the molecular genetic mechanisms of early development in the past few years has revealed an unexpected plasticity in the regulation of gene expression that enables a relatively few conserved cellular processes to be linked together in a variety of potentially adaptive patterns in response to genetic mutation or to environmental change. This “facilitated” variation (Kirschner & Gerhart, 2005) is capable of generating the useful novelties that random mutation and selection by themselves have seemed far less capable of producing in the course of evolution. New findings have also shown that the plasticity of behavior development in response to environmental interactions and genetic change (mutation, recombination) is capable of generating novel forms of adaptive variation upon which selection can act. By exposing previously hidden (genomically silenced) genes to selection and by genetic “accommodation” through selection acting at multiple genetic sites over generations, novelties that were at first environmentally induced can gradually become independent of their initiating environments (West-Eberhard, 2003). This focus on changes in gene regulation as a central mechanism of development has led to a wave of new

The development of an adult organism from a single cell is one of the most familiar processes of nature. It has been studied by scientists for more than two centuries without the emergence of a generally accepted explanatory theory. Ironically, evolution, the other great historical process in biology, has been far more difficult to study, yet Darwin’s simple but powerful theory has guided scientists for 150 years. One reflection of the lack of an agreed-on set of developmental principles has been the persistence, in scientific as well as lay circles, of the seemingly endless nature versus nurture debate. Another is the gap that exists between the language, methods, and concepts used by scientists studying development at the psychological, behavioral, and the cellular/molecular levels. Less discussed than these issues, but more fundamental, has been the uncertainty about how evolution and development are related. The topic was opened with Ernest Haeckel’s (1892) resilient nineteenth-century formulation that “ontogeny recapitulates phylogeny” (pp. 422–544) that was finally laid to rest in 1977 with the publication of Ontogeny and Phylogeny by Stephen Jay Gould. In the past few decades, developmental psychobiology and, more recently, developmental neuroscience have made considerable progress in advancing an interdisciplinary approach to the study of development. New methods for the study of early behavior in animal model systems and in human fetuses have begun to build connections between events and concepts at the cellular/molecular, physiological, behavioral, and psychological levels. For it is in early development that one can best see how each of these levels of biological function emerge in sequence and come to work together.

EVOLUTION AND DEVELOPMENT In the past few years a new field, evolutionary developmental biology, or evo-devo, has emerged as new methods of functional genetic analysis and manipulation have 12

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Evolution and Development 13

discoveries in the area of “epigenetic” processes involving the remodeling of chromatin, the protein matrix surrounding, supporting, and controlling the access of molecular regulatory signals to the DNA strands within chromosomes (Baylin & Schuebel, 2007). These molecular rearrangements, which take place through simple chemical processes such as methylation and acetylation, provide, for the first time, a direct cellular/molecular link between the outside environment and the genes of the organism during development, a concrete and specifiable locus for the much-sought-after gene–environment interaction. Developmental Selection: The Evolution of Development The emphasis in evo-devo is on the proposed role of development in evolution, but what implications does this new evolutionary role have for the concept of development? What competitive advantages did the evolution of multicellular development convey? What selective pressures have shaped development during its own evolution, and what can these tell scientists about the adaptive functions that they should expect modern-day developmental processes to carry out? The question of how and when development evolved is essentially the question of the origins of multicellularity. It leads to one of the last remaining mysteries in the fossil record of evolution, the Cambrian explosion (Gould, 1989; Knoll, 2003). In this abrupt change in the number and variety of marine fossils, examples of all major animal groups or phyla that exist today (e.g., vertebrates, mollusks,

Cell division

Sexual recombination

arthropods, worms) appeared abruptly approximately 500 million years ago. For the more than 3 billion years before that, only fossils of single-celled organisms have been found. Modern-day representatives of these ancient organisms, such as algae and amoebae, seem little changed in form since that time, but they have been found to possess all the characteristics of developing cells in multicellular animals (see Figure 2.1) such as rapid multiplication, migration, adhesion, the capacity for sexual as well as asexual reproduction, and the capacity for differentiation (e.g., from an amoeba to a flagellate form). These adaptive cellular/molecular mechanisms evolved in response to “signal” molecules in their environments that accompanied or produced changing conditions. What is missing in these protozoa is the organization of these cellular/molecular processes into a linear series of events involving many such cells so as to produce structures that grow in size, shape, and complexity of form and function (see Figure 2.2). This is called development, and it must have emerged very rapidly (in geologic time), as it was crucial for the appearance of multicellular life. The prerequisite for this emergence seems to have been a gradual increase in the complexity of the genome of unicellular animals. Evidence for this lies in the progressive appearance of unicellular lineages whose living members showed larger and larger numbers of introns (the noncoding segments of DNA that facilitate “shuffling” of gene positions along the DNA strand), a multiplication of promoters of gene activation, and the appearance of novel regulatory elements leading to a greater degree of interaction between genes. These genomic changes allowed for

Adhesion Migration

Sporulation

Apoptosis

Differentiation

Molecular signals

Environmental signals

Figure 2.1 Protozoan precursors of developmental processes. Note: Unicellular organisms evolved nearly all of the cellular/molecular mechanisms needed for multicellular development over the 3 billion years of life prior to the Cambrian explosion of diverse multicellular organisms

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500 million years ago. These processes were selected for their capacity to enhance the adaptive capacity of these single-cell organisms to changes in the surrounding environment.

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Developmental Neuroscience

Note: The rapid evolution of metazoan (multicellular) organisms took place through a reorganization of the various individual cellular/molecular mechanisms of unicellular organisms (represented on the left; see also Figure 2.1) into the integrated, linear processes of development, a process driven by the selective advantage of larger and more complex multicellular

organisms capable of creating and exploiting novel ecological niches. During evolution, cells continued to be regulated by signal molecules in the intercellular environment of the developing metazoan organism, as they had been in unicellular organisms. A return to single cells marks the beginning of the next multicellular generation of metazoans, depicted on the far right.

the rapid emergence of a novel system of genetic control for all multicellular animals in which only a limited set of genes within each cell is expressed at any one time while the vast majority are silenced. Genes in the fertilized egg, or zygote, express signal proteins that diffuse into the intercellular space and then into neighboring cells, where they act as transcription factors regulating the genetic control of growth and function as well as the expression of a further wave of different intercellular signal proteins. The timing, amount, and structure of these waves of signal proteins create a virtual cascade of intercellular communication, and with it a complex pattern of growth and function over time. These developmental cascades specify and organize the construction of a multicellular organism and its complex array of functions, including behavior. Signal molecules in the intercellular environment continue to act to regulate the timing and nature of cellular changes, as they had in unicellular organisms. The development of all multicellular organisms is strikingly similar in its early stages. An egg, fertilized by one other smaller cell, divides repeatedly, forming a ball of similarly appearing cells, the zygote. Gradually, the zygote becomes a blastocyst, with two layers of cells: the endoderm inside and the ectoderm outside. These two sheets of cells become bent and folded, developing a radial symmetry. The development of the most primitive multicellular animals, such as sponges, hydra, and jellyfish, stops here. Behaviorally, these simple creatures show primarily local cellular “irritability” and generalized “flowing” body movements coordinated by nerve nets. But in the metazoan animals of the Cambrian period, an invagination of the hollow blastocyst occurs and a third layer of

cells, the mesoderm, forms between the two other layers. These layers bend to form shapes with symmetry in three planes, and they expand to form groups and sheets of cells that interact with groups of different cells deriving from the other layers. As these cells change in form and location, they differentiate into radically different cell types in different regions to become organs with multiple different functions. One of those organs, the nervous system, becomes the basis for the evolving complexity of animal behavior that so interests neuroscientists. There are good reasons to think that the selective advantage responsible for the evolution of this developmental plan for multicellular life was that it made possible the construction of novel, much larger, and more complex animals. These animals not only were able to use the previously dominant unicellular species as food but were capable of finding and even creating new ecological niches: territories, food sources, and ways of life that bacteria and protozoa were incapable of inhabiting, defending, or utilizing. Bonner (1993) defined development as the growth phase of the life cycle and demonstrated that its duration (and complexity) is highly correlated with the size (and complexity) of any given life form, ranging from bacteria to whales, each enabled to exploit larger and more complex niches. This view of development as construction is widely accepted and has too often led to an assumption that all developmental mechanisms are organized to carry out a single function: the building of a successful adult. The rapid evolution of development, however, was shaped not only by the selective advantages provided by the size and functional complexity of the adults that were constructed. Other developmental processes and functions

Figure 2.2 The evolution of development.

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Evolution and Development 15

had to be selected in order to adapt immature forms to the changing environments they inhabited as embryos, larvae, fetuses, newborns, and weanlings on their path to adulthood. The unique characteristics of larval forms in amphibians, and of intrauterine/placental structures and early nursing interactions in mammals, are examples of the “ontogenetic adaptations” that play such a prominent role in the development of animals that one can study today (Oppenheim, 1984). Furthermore, as the processes mediating the developmental functions of construction and adaptation evolved, they were also being shaped and modified by selection for their “evolvability” (R. Carroll, 2002). That is, processes that created potentially useful variations in the course of development and others that promoted the replication of a successful variant developmental path in the next generation were differentially selected. In the same way that genetic mechanisms for variation (recombination and mutation) and for heritability (the copying of DNA during cell division) were shaped by selection in the evolution of single cells, so a set of developmental mechanisms for “facilitating” variation and for transmitting successful variant paths to the next generation was shaped by selection in the evolution of development in multicellular organisms. Thus, the developmental processes that scientists study today have been organized and shaped in their evolution by the four components of what I have called developmental selection: construction, adaptation, variation, and inheritance (Bateson, Hofer, Oppenheim, & Wiedenmayer, 2007; Hofer, 2005). As with many other features of organisms, developmental processes have been co-selected; that is, processes selected for their contribution to the construction and ontogenetic adaptation of young animals have also been shaped by selection for their heritability and their capacity for variation (see Figure 2.3). For example, the embedding of embryos and fetuses in the internal environments of their mothers, and the sustained close physical interactions of newborns and infants with their parents (Rosenblum & Moltz, 1983), gain a new significance and interpretation if one thinks of them in terms of developmental selection. These features of development can now be viewed as inherited, transgenerational environments that act as a protective scaffolding and matrix for the construction of a descendant in the next generation and as a source of potentially useful variation in the future. In this way, long and complex early developmental pathways can be organized in a linear fashion, regulated by interactions with the previous generation so as to be re-created, with further variations, in the next generation. The usefulness of the concept of developmental selection, it seems to me, is that it defines a set of functions that developmental processes are organized to carry out.

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Figure 2.3 Developmental selection. Note. During evolution, developmental processes are co-selected not only for (a) construction of an adult, but also for their capacity to (b) adapt the immature organism to its age-specific environments, (c) produce potentially useful variations in the course and/or outcome of development, and (d) facilitate the inheritance of successful variants in the next generation.

Knowing the ultimate (i.e., evolutionary) functions of developmental processes should be useful for forming new hypotheses about how a given process works. For example, in the field of sociobiology, the evolutionary principles of kin selection led to the discovery of new processes at work within the parent–offspring relationship, such as parent– offspring conflict. In the case of development, asking how specific events and processes contribute to age-specific adaptation, variation, and inheritance of a selected developmental path in the next generation should increase understanding of development considerably beyond the current approach of asking questions that are limited to the function of constructing an adult. Examples are given in the sections on attachment and the regulation of development. Levels of Organization Developmental neuroscience involves a journey in time across levels of organization from the molecular genetics of an embryonic cell to the mental experiences of a conscious mind (see Figure 2.4). The gap that exists between biology and psychology is slowly narrowing as new biological methods have become increasingly able to approach the brain systems underlying more and more complex cognitive and emotional processes. In the study of early behavior development, the conceptual models of psychologists and the working models of biologists converge, as they are being applied to the same behavioral phenomena. Observations and insights from these different levels of organization are beginning to contribute to one another ’s understanding, as I illustrate with regard to the area of early attachment in the next section. Evolutionary principles offer a conceptual common ground that can be shared by both neuroscience and psychology,

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Developmental Neuroscience

Size Complexity

Time/Age Figure 2.4 Levels of organization in developmental neuroscience. Note: During development, new properties emerge at each level that are not reducible to lower-level events. A full understanding that extends across levels requires repeated efforts at “translation” as well as extensive knowledge of events at each level. Early development affords significant simplification of this process at all levels.

providing answers to questions about how the human mind and brain have come into being and why they have their present forms. The historical nature of both development and evolution bridges the gap that exists between the reductionist emphasis of the molecular/cellular neurosciences and the holistic emphasis on inner experience and meaning that is the central focus of some branches of psychology. Early human development traverses a series of levels of scale and organization (as illustrated in Figure 2.4) from the molecular and intercellular interactions of the embryo, to the integrated systems and behavior of the fetus, to the emerging cognitive and affective capacities of the child, and finally to the inner experience that even language cannot fully describe. The biological, behavioral, and psychological processes at work at those levels of organization seem very different. But the new properties that emerge at higher levels arise from the combined operation of simpler component processes taking place at the lower level. Understanding those transitions, and the emergence of new properties at higher levels, is one of the central questions for research in early animal and human development as well as for attempts to integrate neuroscience and psychology in general. In early development, behavior provides a crucial link between the levels of brain systems and of psychological constructs, as is illustrated in the following sections.

THE EARLY DEVELOPMENT OF ATTACHMENT In this section I outline how a strategy of attempting to understand the component processes underlying the psychological constructs created in studying early human

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attachment can provide new and potentially useful ways of thinking in a more general way about development as it occurs at multiple levels of organization. The idea of “early attachment” exists in a number of forms in people’s ways of speaking and thinking. In its most general sense, the phrase refers to a set of behaviors observed in infants and the feelings and thought processes (conscious and/ or unconscious) that we suppose infants have with their mothers or caretakers, based on our own experiences and memories and the psychological concepts we have formed for ourselves or learned from others (see also Volume 2, Chapters 36–42 and Chapters 47–49). Within this range of use of the word attachment, several different schools of thought have coalesced within psychology. Common to all, however, are three themes: (1) some sort of emotional tie or bond that is inferred to develop between the infant and his or her caretaker that keeps the infant physically close, (2) a series of responses to separation that constitute the infant’s emotional response to interruption or rupture of that bond, and (3) the existence of different patterns or qualities of the interaction between infants and mothers that persist over time and lead to longterm effects of this experience on the social and emotional functioning of offspring throughout life, even extending to a repetition of specific patterns of mothering by daughters in the next generation. These observations and the psychological concepts of attachment have been extremely useful in human developmental psychology, but they leave a number of observations unexplained and questions unanswered. Furthermore, when my colleagues and I found close similarities in the behavior and separation responses of a far less evolved mammal, the laboratory rat pup, this suggested that we were missing a deeper layer of biological processes underlying the psychological concepts of attachment theory. The unanswered questions left open by developmental attachment theory are posed in the sections that follow. The answers that came from our laboratory research illustrate how evolutionary developmental theory and the concept of developmental selection help to better explain the nature and functions of early attachment at the psychological as well as the biological levels. Deconstructing the First Attachment Bond Infants of mammalian species that are born in an immature state, such as the human and the laboratory rat, face a daunting task. They must find a way to identify, remember, and prefer their own mother, and they must use these capacities to reorganize their simple motor repertoires, long adapted to the uterine environment, so as to be able to approach, remain close to, and orient themselves to their mothers.

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The Early Development of Attachment

Until recently it was assumed that these bonding processes were well beyond the capacities of newborn mammals (except in precocial species such as the sheep) and that the closeness of the relationship depended almost entirely on maternal behavior until well into the nursing period (Bowlby, 1982; Kraemer, 1992; Volume 2, Chapter 48). It was supposed that an attachment bond builds up slowly in the weeks or days after birth through repeated mother– infant interactions, starting with stereotyped reflexes in the newborn. But the past decade has produced a number of studies revealing evidence of earlier and earlier learning, extending even into the prenatal period, as is described here. In addition, coordinated motor acts have been demonstrated experimentally in fetuses in response to specific stimuli that will not be encountered until after birth. Thus, the solutions for the infant’s tasks appear to be found much earlier than previously thought and appear to take place through novel developmental processes that had not been imagined until recently. These developmental processes clearly function as age-specific adaptations to the unique environments of early development, playing only a supportive role in growth of size and complexity, for many of these behaviors (e.g., nursing) have been shown not to be precursors of later behaviors. Prenatal Origins The first strong evidence for fetal learning came from studies on early voice recognition in humans, in which it was found that babies recognize and prefer their own mother ’s voice, even when tested within hours after birth (De Casper & Fifer, 1980). Bill Fifer continued these studies in our department using an ingenious device through which newborns can choose between two tape-recorded voices by sucking at different rates on a pacifier rigged to control an audiotape player (Fifer & Moon, 1995). He has found that newborn infants, in the first hours after birth, prefer human voices to silence, female voices to male, their native language to another language, and their own mother to another mother reading the same Dr. Seuss story. In order to obtain more direct evidence for the prenatal origins of these preferences (rather than very rapid postnatal learning), Fifer filtered the high-frequency components from the tapes to make the mother ’s voice resemble recordings of maternal voice by hydrophone placed within the amniotic space of pregnant women. This altered recording, in which the words were virtually unrecognizable to adults, was preferred to the standard mother ’s voice by newborns in the first hours after birth, a preference that tended to wane in the second and third postnatal days. Furthermore, there is evidence that newborns prefer familiar rhythmic phrase sequences to which they have been repeatedly exposed prenatally when pregnant mothers daily read out loud a specific text in a quiet place (De Casper & Spence, 1988).

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In a striking interspecies similarity, rat pups were shown to discriminate and prefer their own dams’ amniotic fluid to that of another dam when offered a choice in a head-turning task (Hepper, 1987). Newborn pups were also shown to require amniotic fluid on a teat in order to find and attach to it for their first nursing attempt (Blass, 1990). Robinson and Smotherman (1995) directly tested the hypothesis that pups begin to learn about their mothers’ scent in utero. They were able to demonstrate one trial taste aversion learning and classical conditioning in late-term rat fetuses using intraoral cannula infusions and perioral stimulation. Taste aversions learned in utero were expressed in the free-feeding responses of weanling rats nearly 3 weeks later. The authors went on to determine that aversive responses to vibrissa stimulation were attenuated or blocked by intraoral milk infusion, a prenatal “comfort” effect they found to be mediated by a central kappa-opioid receptor system. These forms of fetal learning, involving maternal voice in humans and amniotic fluid in rodents, appear to play an adaptive role in preparing the infant for its first extrauterine encounter with its mother. They are thus the earliest origins as yet found for attachment to the mother. The spontaneous motor acts needed for an attachment system also appear to be developing prior to birth. Rat fetuses engage in a number of spontaneous behaviors in utero, including curls, stretches, and trunk and limb movements. These acts were observed to increase markedly in frequency with progressive removal of intrauterine space constraints as pups were observed first through the uterine wall, then through the thin amniotic sac, and finally unrestrained in a warm saline bath (Smotherman & Robinson, 1986). When newborn pups are observed prior to their first nursing bout they resemble exteriorized fetuses, until the mother lowers her ventrum over them. Their behavior then changes rapidly over the first few nursing bouts into a complex repertoire as described in the next section. How Newborns Approach and Orient to Their Mothers When newborn pups in their first extrauterine experience are stimulated gently by soft surfaces from above, as when the mother hovers over them, they show a surprisingly vigorous repertoire of behaviors (Polan & Hofer, 1999). These include the spontaneous curling and stretching seen prenatally but also locomotor movement toward the suspended surface, directed wriggling, audible vocalizations, and, most strikingly, turning upside down toward the surface above them. Evidently these behaviors propel the pup into close contact with the ventrum, maintaining it in proximity and keeping it oriented toward the surface. They thus appear to be very early attachment behaviors. In a series of experiments my colleague and I found that these are not

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stereotyped reflex acts but organized responses that are graded according to the number of maternal-like stimulus modalities present on the surface presented experimentally (e.g., texture, warmth, odor; Polan & Hofer, 1999). Furthermore, they were enhanced by periods of prior maternal deprivation, suggesting the rapid development of a motivational component. We found that by 2 days of age, pups discriminate their own mother ’s odor and prefer it to equally familiar nest odors (Polan & Hofer, 1998). Hepper (1987) showed that by the first postnatal week, pups discriminate and prefer their own mother, father, and siblings to other lactating females, males, or agemates. Recent work in humans inspired by these findings in lower animals has shown that human newborns, too, are capable of slowly locomoting across the bare surface of their mother ’s abdomen and locating the breast scented with amniotic fluid in preference to the untreated breast (Varendi, Porter, & Winberg, 1996). Although newborns are attracted to natural breast odors even before the first nursing bout (Makin & Porter, 1989), amniotic fluid can override this effect. Apparently, human newborns are not as helpless as previously thought but possess approach and orienting behaviors that anticipate the recognized onset of specific maternal comfort responses at 6 to 8 months. These events and processes can best be understood in terms of the four primary functions of development (construction, adaptation, inheritance, and variation). More and more complex behavior patterns are constructed, beginning with simple local movements and progressing through organized graded responses to specific stimuli. Then with a unique rapid learning process, behavior becomes organized into an adaptive repertoire of nursing-related behaviors. These developmental steps function in part as transient adaptations first to the confines of the uterus and then to the requirements of staying close, finding a nipple, and initiating sucking. One can also see how the inherited developmental environment of the uterus and the maternal nest supply a matrix and a template for the formation and organization of species-specific mother–infant interaction patterns. Thus, the developmental environments and events of the uterine cavity and maternal ventrum function to ensure that the same developmental path will be repeated in the next generation (i.e., inherited). As differences occur in the kinds and intensities of maternal stimulation of pups or in other aspects of the interaction, variation from one generation to the next will also take place. It is in the process of fulfilling these functions of development (adaptation, inheritance, and variation) that the organization and construction of more complex repertoires of behavior takes place. Looking for novel development processes that fulfill the functions of variation and inheritance should lead to a deeper understanding of how development works.

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A Novel Postnatal Learning System Bowlby (1969) was uncertain about exactly how a behavioral attachment system or bond developed in slowmaturing mammals and hypothesized that some learning mechanism must exist that is similar to the phenomenon of “imprinting” in birds, made famous by Konrad Lorenz (1996). Scientists are now beginning to gain an understanding of how such a specific proximity–maintenance system develops in animals and humans at the levels of basic learning mechanisms and the brain systems mediating them. Regina Sullivan and Steven Brake in our lab discovered that within 2 to 3 days of birth, neonatal rat pups were capable of learning to discriminate, prefer, approach, and maintain proximity to an odor that had been associated with forms of stimulation that naturally occurred within the early mother– infant interaction (e.g., milk or stroking; Sullivan, Hofer, & Brake, 1986). Random presentations of the two stimuli had no such effect, a control procedure that identified the change in behavior as being due to associative conditioning and not some nonspecific effect of repeated stimulation. Because the learning required only two or three paired presentations and because the preference was retained for many days, it seemed to qualify as the long-sought “imprinting-like process” that is likely central to attachment in slow-developing mammals. Indeed, a human analogue of this process was found by Sullivan, who showed that when human newborns were presented with a novel odor and were then rubbed repeatedly along their torsos to simulate maternal care, the next day they became activated and turned their head preferentially toward that odor (Sullivan et al., 1991). This suggests that rapid learning of orientation to olfactory cues is an evolutionarily conserved process in mammalian newborns. Early attachment-related odors appear to retain value into adulthood, although the role of the odor in modifying behavior appears to change with development. Work done independently in the labs of Celia Moore (Moore, Jordan, & Wong, 1996) and Elliot Blass (Fillion & Blass, 1986) has demonstrated that adult male rats showed evidence of enhanced sexual performance when exposed to females scented with the artificial odors with which their mothers had been scented during the male rats’ infancy. Aversive Learning of the Attachment Bond Clinical observations have taught that not only does attachment occur to supportive caretakers, but children can endure considerable pain and even injury while becoming strongly attached to an abusive caretaker. Although it may initially appear to be counterproductive from an evolutionary perspective to form and maintain an attachment to an abusive caretaker, it may be better for a slow-developing mammalian infant to have a bad caretaker than none at all.

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The Early Development of Attachment

This aspect of human attachment is also represented in the infant rat. We found that during the first postnatal week, a surprisingly broad spectrum of stimuli can function as reinforcers to produce an odor preference in rat pups (Sullivan, Brake, Hofer, & Williams, 1986; Sullivan, Hofer, et al., 1986). These stimuli range from apparently rewarding ones such as milk and access to the mother (Alberts & May, 1984; Brake, Sager, Sullivan, & Hofer, 1982; Pedersen, Williams, & Blass, 1982; Wilson & Sullivan, 1994) to apparently aversive ones such as moderate shock and tailpinch (Camp & Rudy, 1988; Sullivan, Hofer, et al., 1986), stimuli that elicit immediate escape responses from the pups. It should be noted that threshold to shock (Stehouwer & Campbell, 1978) and the pup’s behavioral response (Emerich, Scalzo, Enters, Spear, & Spear, 1985) do not change between the ages of 9 and 11 days. As pups mature and reach an age when leaving the nest becomes more likely, olfactory learning comes to more closely resemble learning in adults. Specifically, odor aversions are easily learned by 2-week-olds, and acquisition of odor preferences is limited to odors paired with stimuli of positive value (Sullivan & Wilson, 1995). Thus, the learning that underlies early attachment develops through a transiently adaptive “paradoxical” phase during which positive associations take place in response to a very broad range of contingent events (including painful stimulation) while pups are confined to the nest. It becomes more selective at a time in development when pups begin leaving the nest and encountering novel odors not associated with the mother. Brain Substrates for Two Kinds of Early Attachment Learning Early rapid aversive learning has been traced to focal odor-specific areas in the olfactory bulb by Sullivan and Donald Wilson. Certain cell types alter their firing rates in response to a specific odor as a result of learning experience (Sullivan, Wilson, Wong, Correa, & Leon, 1990; Wilson & Sullivan, 1994). This altered firing rate is the result of activation of norepinephrine pathways leading from the locus coeruleus. Indeed, behavioral learning can be driven by electrical stimulation of the locus or norepinephrine injection in the olfactory bulb in association with the novel odor, without the association of any maternal stimuli with the novel odor. The period during which aversive learning of position associations takes place ends about 10 days postnatal, but this “sensitive period” can be extended by a few repeated brief associations of odor with shock each day until weaning begins about a week later. The period from 12 to 15 days of age is an interesting one because during this time, if the mother is present during the association of odor and aversive stimulation, preference

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learning takes place, but in the mother ’s absence, the odor is subsequently avoided. Recent studies of neural and hormonal substrates has helped to explain this unusual developmental pattern (Moriceau, Wilson, Levine, & Sullivan, 2006). As illustrated in Figure 2.5, the primary brain substrate activated by the later developing avoidance learning in 12- to 15-day-old pups with the mother absent was the amygdala, not the locus coeruleus and olfactory bulb. However, in the mother ’s presence, there was no amygdala activation. Instead, positive association learning took place, mediated by the locus coeruleus. Corticosterone levels were previously known to be reduced by maternal presence at this age. Moriceau and Sullivan administered increased corticosterone to the pups learning in the mother ’s presence and less corticosterone to those learning in the mother ’s absence. The results showed that corticosterone levels mediated the maternal “switch” between the formation of positive and negative association learning and did so by switching the neural substrates mediating the response between the locus coeruleus/olfactory bulb and the amygdala. It is relatively easy to see how these seemingly complex paths of development in early systems for learning are likely to have evolved. Only outside the nest, and in the absence of the mother, is it crucial to learn an avoidance response to all painful stimuli. In interactions with the mother, learning to avoid her as a result of painful or uncomfortable associated conditions would likely lead to far more dangerous circumstances. The specialized environmental contingencies of early life have selected, during evolution, a sequence of the development of learning that would be hard to explain except within the concept of developmental selection. The next steps in this research will be to learn which genetic, experiential, and developmental processes were involved in the creation of these developmental paths during evolution and how they

Early

Corticosterone levels low

Late

Corticosterone levels high

Figure 2.5 Summary of transition between two learning systems and their neural substrates in the formation of early attachment in rat pups. Note. Early ⫽ birth to 10 days of age. Late ⫽ 11 to 15 days. Differences in properties and known mechanisms of the two forms of learning can be shifted in time across the 10-day transition point according to corticosterone levels and/or presence or absence of mother during learning.

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became integrated into the developmental sequences and mechanisms this work has revealed. Parallel Processes in Human Maternal Attachment Learning Successful mother–infant interactions require the reciprocal responding of both individuals in the mother–infant dyad. Human mothers rapidly learn about their baby’s characteristics and can identify their baby’s cry, odor, and facial features within hours of the baby’s birth (Eidelman & Kaitz, 1992; Kaitz, Lapidot, & Bronner, 1992; Porter, Cernoch, & McLaughlin, 1983). An animal model for this rapid learning by mothers has received considerable attention (Brennen & Kaverne, 1997; Fleming, O’Day, & Kraemer, 1999). Indeed, there are interesting parallels between the early attachment behavior of infants and the attachment behavior of the newly parturient mother. In rats and sheep, a temporally restricted period of postpartum olfactory learning in the mother involving norepinephrine facilitates the mother ’s learning about her young (Levy, Gervais, Kindermann, Orgeur, & Piketty, 1990; Moffat, Suh, & Fleming, 1993). It is possible that mammalian mothers and their young use similar neural circuitry to form their reciprocal attachments, both abusive and normal.

EARLY SEPARATION AS LOSS OF REGULATION In Bowlby (1969, 1982) and Harlow’s (1958) work, as well as in the clinical observations of Anna Freud and Dorothy Burlingham (1943) a generation before, it was maternal separation that revealed the existence of an “animal tie” between mother and infant and a deeper layer of processes beneath the apparently simple interactions of mother and infant. Bowlby (1969, 1982) viewed these processes as primarily psychological. The behavioral and physiological responses of the infant to separation, in their conception, were a consequence of the rupture of a psychological bond that was formed as part of an integrated psychophysiological organization that Bowlby called the attachment system. More recent research, however, has revealed a network of simple behavioral and biological processes that underlie this and other psychological concepts used to understand early human social relationships (see Volume 2, Chapter 47). The Separation Cry One of the best known responses to maternal separation is the infant’s separation cry, a behavior that occurs in a wide variety of species (Lester & Boukydis, 1985; Newman,

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1998). In the rat, this call is in the ultrasonic range (40 kHz) and appears in the first or second postnatal day. Pharmacological studies in a number of labs (reviewed in Hofer, 1996) have shown that the ultrasonic vocalization (USV) response to isolation is attenuated or blocked in a dose-dependent manner by clinically effective anxiolytics that act at benzodiazepine and serotonin receptors. Conversely, USV rates are increased by compounds known to be anxiogenic in humans, such as benzodiazepine receptor inverse agonists (beta-carboline, FG 1742) and GABA-A receptor ligands such as pentylenetetrazol (Brunelli & Hofer, 2001; Miczek, Tornatsky, & Vivian, 1991). Within serotonin and opioid systems, receptor subtypes known to have opposing effects on experimental anxiety in adult rats and humans also have opposing effects on infant USV calling rates (see Figure 2.6). Neuroanatomical studies in infant rats have shown that stimulation of the periaquaductal grey area produces USV calls, and chemical lesions of this area prevent calling (Goodwin & Barr, 1998). The more distal motor pathway is through nucleus ambiguus and both laryngeal branches of the vagus nerve. The engagement of higher centers known to be involved in cats and primates suggests a neural substrate for isolation calls involving primarily the hypothalamus, amygdala, thalamus, and hippocampus, and cingulate cortex, brain areas known to be involved in adult human and animal anxiety responses (Newman, 1998). This evidence strongly suggests that separation produces an early affective state resembling anxiety in rat pups, one that is expressed by the rate of infant calling. This calling behavior, and its inferred underlying affective state, develops as a communication system between mother and pup. The evolution of such a response is clarified by the finding that infant rat USV is a powerful stimulus for the lactating rat, capable of causing her to interrupt an ongoing nursing bout, initiate searching outside the nest, and direct her search toward the source of the calls (Smotherman, Bell, Hershberger, & Coover, 1978). The mother ’s retrieval response to the pup’s vocal signals then results in renewed contact between pup and mother. This contact, in turn, quiets the pup (as represented along the bottom right of Figure 2.6). This entire behavioral system fades out as infants cease to vocalize when isolated during the weaning period, showing that it represents a developmental ontogenetic adaptation. In the psychological concept of attachment, vocal separation and comfort responses are conceptualized as emotional expressions of interruption and reestablishment of a social bond. Such a formulation would predict that because pups recognize their own mothers by the mothers’ scents (as previously described), pups made acutely anosmic would fail to show a comfort response. But anosmic pups

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Early Separation as Loss of Regulation

Figure 2.6 The regulatory control system for infant ultrasonic vocalization. Note. BZi ⫽ benzodiazepine inverse agonist; CRH ⫽ corticotropinreleasing hormone; DA ⫽ dopamine; 5-HT ⫽ 5-hydroxytryptamine (serotonin); GABA ⫽ gamma-amino butyric acid; NA ⫽ noradrenaline; NMDA ⫽ N-methyl-D-aspartic acid; OT ⫽ oxytocin; VP ⫽ vasopressin. Moving counterclockwise from the far right in the diagram, interactions of a rat pup with its mother (proximity, warmth, etc.) act over multiple

show comfort responses that are virtually unaffected by the loss of their capacity to recognize their mothers in this way (Hofer & Shair, 1991). Instead Harry Shair, Susan Brunelli, and I have found multiple regulators of infant USV within the contact between mother and pup: warmth, tactile stimuli, and milk as well as her scent (Hofer, 1996). Provision of stimulation in these modalities separately (e.g., artificial fur lacking warmth or scent) and then progressively combining modalities elicited graded responses. The full “comfort” quieting response was elicited only when all modalities were presented together, and maximum calling rates occurred when all were withdrawn at once. In essence, we found parallel regulatory systems involving different sensory modalities (see Figure 2.6). These functioned in a cumulative or additive way, with the rate of infant calling reflecting the sum total of effective regulatory stimuli present at any given point in time. These processes at the behavioral/biological level underlie the psychological concept of separation anxiety. They do not supplant it. They operate at a different level of organization; the psychological concepts can be thought of as emerging from the lower-level component processes. Searching for What Was Lost Experiments in our laboratory have shown that infant rats also have more complex and lasting responses to maternal

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pathways (olfactory, thermal, etc.) to activate infant sensory systems (center top) and then brain neurotransmitter receptor systems (in box on the left). Those receptors in the (⫹) column, when activated, increase calling rate (ultrasonic vocalization), and those in the (–) column suppress calling. Maternal separation (lower center) results in a rapid burst of calling that gradually subsides. Continuing counterclockwise from the center of the diagram, ultrasonic vocalization by the pup stimulates maternal retrieval, licking, and so on, reinstating contact and maternal interactions (far right), thus closing the circle.

separation, similar to primates in a number of different physiological and behavioral systems. A number of years ago colleagues and I found slower developing changes following maternal separation, similar to those of Bowlby’s (1969, pp. 27–28) “despair” phase (see Figure 2.7A). This was not an integrated psychophysiological response as Bowlby had supposed but the result of a novel mechanism (see Figure 2.7B). As separation continued beyond the initial vocal response, each of the individual systems of the infant rat responded to the loss of one or another of the components of the infant’s previous interaction with its mother. Providing one of these components to a separated pup (e.g., maternal warmth) maintained the level of brain biogenic amine function underlying the pup’s general activity level for up to 3 days (Stone, Bonnet, & Hofer, 1976) but had no effect on other systems. For example, the pup’s cardiac rate continued to fall to 60% of its normal level over 24 hours regardless of whether supplemental heat was provided (Hofer, 1971). We found that the heart rate, normally maintained by sympathetic autonomic tone, was regulated by provision of milk acting on receptors in the lining of the pup’s stomach (Hofer & Weiner, 1975). With loss of the maternal milk supply, sympathetic tone fell and cardiac rate was reduced by 40% in 12 to 18 hours. By studying a number of additional systems—such as those controlling sleep–wake states, activity level, sucking pattern, and blood pressure (Brake et al., 1982; Hofer, 1975,

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Developmental Neuroscience

(A)

(B)

Growth Hormone

Activity Heart Rate REM Sleep

Figure 2.7 Two schematic representations of the dynamics of early separation. Note. A: as conceptualized in the framework of attachment theory by John Bowlby and B: as found to result from loss of regulatory interactions within the mother–infant relationship.

1976; Shear, Brunelli, & Hofer, 1983)—we found different components of the mother–infant interaction such as olfaction, taste, touch, warmth, and texture that normally either upregulated or downregulated each of these functions (or, in the case of sleep states, regulated rhythmic patterning). Thus, we concluded that in maternal separation, all of these regulatory components of the mother–infant interaction are withdrawn at once (see Figure 2.7). This widespread loss creates a pattern of increases or decreases in level of function of the infant’s systems, depending on whether the particular system had been up- or downregulated prior to separation by specific components of the previous mother– infant interaction. We called these components hidden regulators because they were not evident when one simply observed the ongoing mother–infant relationship. These studies revealed a novel mechanism for the infant response to separation, an event that previously could be

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conceptualized only at the psychological level, as an emotional response. In younger infants this was conceived to be the result of rupture of the bond between mother and infant, and in older children the stress resulting from perception of the loss of the mother ’s emotional, physical, and nutritional support. At the biological level of organization we found that separation resulted in the withdrawal or loss of a number of different physical interactions between mother and infant that normally regulated infant physiology and behavior on an ongoing basis. Thus, we found that infant and mother compose a partial fusion of two individual homeostatic units into a single common homeostatic organization. When separated, each returns to its individual set points at lower or higher levels of function. The regulation of maternal milk letdown by infant sucking is a wellknown example of a hidden regulatory system present in all adult female mammals. When the suckling interaction

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Early Separation as Loss of Regulation

is interrupted, the mother ’s milk production and periodic letdown become greatly reduced. The observations of psychologists on the evident changes in mental state of infants during separation and the psychological constructs used to explain them remain valid and useful. However, the processes at the biological level described here can enlarge psychological understanding and support newer psychological concepts of emotional and perceptual regulation. It is not that rat pups respond to loss of regulatory processes, whereas humans respond to emotions of love, sadness, anger, and grief. Human infants, as they mature, can respond at the level of complex affective responses and symbols as well as at the level of regulatory interactions. And rat pups respond at the level of an affective state expressed through separation calling as described previously. Even adult humans continue to respond in important ways at the sensorimotor– physiologic level in their social interactions (Hofer, 1984; Stern & McClintock, 1998). Examples include the role of social interactions in entraining sleep–wake and menstrual rhythms, the disorganizing effects of sensory deprivation, and the remarkable effects of social support on the course of medical illness. This extended homeostatic system of mother and infant represents an aspect of mammalian early development similar to the intertwined nature of the fetus and mother, essentially an extension of the symbiosis recognized in the intrauterine period of development. By applying the concept of developmental selection, it is evident that these evolved adaptations function to provide a living environment that is inherited from one generation to the next to serve as a guiding matrix, enabling a specific early developmental course to be repeated (see Figure 2.8). Variations in maternal behavior are capable of producing variations in infant physiology and behavior through the regulatory processes described. These variations can be immediately adaptive. For example, the major thermoregulatory, cardiovascular, and behavioral responses of pups separated for a period of hours slow metabolism, shunt blood away from the digestive tract to the heart and brain, and profoundly reduce spontaneous behavior, inducing a hibernation-like state that promotes survival until the mother returns. Furthermore, variations in infant development that are repeated over generations (e.g., because of increased maternal foraging demands during climate change creating long absences) become targets for selection and can become increasingly efficient and finally genetically fixed, producing an alternative infant phenotype. In this way one can better understand why these widespread and complex regulatory process evolved to become a feature of mammalian early development, serving the developmental functions of inheritance and variation as well as construction and adaptation.

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Hidden Regulators of Early Development Other investigators, using this approach, have since discovered other maternal regulatory systems of the same sort. For example, Saul Schanberg, Cynthia Kuhn, and colleagues found that separation of the dam from rat pups produced a rapid (30-min) fall in the pup’s growth hormone levels, and vigorous tactile stroking of maternally separated pups (mimicking maternal licking) prevented the fall in growth hormone (Kuhn & Schanberg, 1991). Brain substrates for this effect were then investigated, and it now appears that growth hormone levels are normally maintained by maternal licking, acting through serotonin (5HT) 2A and 2C receptor modulation of the balance between growthhormone-releasing factor and somatostatin that together act on the anterior pituitary release of growth hormone (Katz, Nathan, Kuhn, & Schanberg, 1996). The withdrawal of maternal licking by separation allows growth-hormonereleasing factor to fall and somatostatin to rise, resulting in a precipitous fall in growth hormone and cessation of growth processes generally for several days. However, a parallel process in prematurely born human infants showed longer-term regulation. There are several biological similarities between this maternal deprivation effect in rats and the growth retardation that occurs in some variants of human reactive attachment disorders of infancy. Applying this new knowledge about the regulation of growth hormone to lowbirth-weight, prematurely born babies, Tiffany Field and coworkers (1986) joined the Schanberg group. They used a combination of stroking and limb movement, administered 3 times a day for 15 min each time and continuing throughout the babies’ 2 weeks of hospitalization. This intervention increased weight gain, head circumference, and behavior development test scores in relation to a randomly chosen control group, with earlier discharge from the intensive care unit and other enhanced maturational effects discernible 6 months later. Clearly, early regulators are also effective in humans, and over time periods as long as weeks to months. As experimenters began to realize that infants’ separation responses revealed a network of individual regulatory processes within the mother–infant interaction, an important implication of this finding emerged: These ongoing regulatory interactions could, over the long term, act to shape the development of an infant’s brain and behavior throughout the preweaning period when mother and infant remained in close proximity. And when maternal behavior changed in response to changes in her environment, this could change the course of her offspring’s development. We could now think of the mother–infant interaction as a long-term regulator of development, with variations in

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Developmental Neuroscience

the intensity and patterning of mother–infant interactions gradually shaping the development of behavior and physiology. These processes go far beyond the adaptive evolutionary role of attachment as a protection against predators proposed by Bowlby (1969). They also go beyond the role described in the previous section of a flexible adaptive response of the infant to the environmental conditions that caused maternal separation. Here, development is regulated along a different path for a substantial period of time. With the mother absent for even longer time periods, perhaps never to return, and without another effective source of regular stimulation and caretaking, an overall slowing of growth occurs to the point that a smaller adolescent and adult with lower nutritional needs will develop. Subsequent research has shown long-term effects on offspring even from briefer and less-extreme mother–infant experiences.

LONG-TERM MATERNAL REGULATION OF DEVELOPMENT In clinical work and in attachment theory, the psychological construct of an enduring mental representation is generally used to denote an internal working model that is formed early in the infant’s developing mind, through his or her particular interaction with parents or consistent caretakers. This conceptual model helps to organize and explain how the nature of peoples’ later relationships and responses to stress seem to be shaped by their experiences as infants and children and even transmitted to their offspring in the next generation through particular patterns of mothering behavior expressive of the mother ’s mental representation of how mothers behave toward their infants. In research with a simple mammalian working model system, we found evidence of similar long-lasting and transgenerational effects that can be attributed to the processes of the maternal regulatory interactions described previously. For example, Sigurd Ackerman, Herbert Weiner, and I found that permanent maternal separation of juvenile rat pups early in the weaning period (early weaning) produced a greatly increased vulnerability to stress-produced gastric ulcer in adolescence and early adulthood (Skolnick, Ackerman, Hofer, & Weiner, 1980). To our great surprise, we found that this effect persisted in the next generation in the normally reared offspring of mothers that had been separated early as infants. We also found that, as adult mothers, early-weaned females spent less time and interacted less with their infants. However, a cross-fostering study revealed that this was not the result of the reduced maternal behaviors of the early weaned mothers. For if the pups of normally reared mothers were substituted for the

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early-weaned mother ’s own at birth, these cross-fostered pups were not vulnerable as adolescents. Instead, the pups born to early-weaned mothers but reared by normally reared mothers inherited the vulnerability. This suggested an unknown intrauterine or germ cell line transmission mechanism. In a different study on the development of hypertension in a strain of rat that had been selected over generations for high blood pressure (SHR), Michael Myers, Susan Brunelli, and I found that naturally occurring variation in 3 out of the 12 maternal behaviors we observed in the hypertensive and control strains (WKY) of inbred (genetically homogenous) rats did indeed appear to regulate the development of the physiological trait in the next generation: the levels of blood pressure in their offspring. Because the animals in each strain were genetically identical, and because the levels of maternal behaviors were significantly correlated with the magnitude of effect on adult offspring blood pressure both within and between strains, it seemed very likely that it was the variation in maternal behaviors acting on the pups that produced these long-term effects (Myers, Brunelli, Squire, Shindeldecker, & Hofer, 1989). The findings of these two studies led us to realize that both the hidden postnatal maternal regulators that we had discovered in our separation studies and a different class of early-weaning-induced prenatal or germ line influences could have long-term regulatory effects on later development, even into the next generation. In their implications for human development, they appeared to represent a level of biological developmental processes that underlie and coexist with the psychological processes inferred to mediate the lasting effects clinically observed between early relationships and their later mental representations in patients (Hofer, 1980, 2005). However, the biology of these effects was most unusual and difficult to explain in terms of the familiar processes studied in developmental psychobiology at the time. In addition, the evolutionary basis for such long-term developmental effects was unclear. Now, with a new understanding of the role of development in evolution, one can begin to make some sense of them (see Figure 2.8). The concept of developmental selection states that developmental processes have been selected, in part, to function as creators of potentially useful variation and to provide developmental mechanisms of inheritance necessary for selection of the most successful of these variations. The regulatory processes hidden within early mother–infant interactions have adaptive value for the infant within the unique environment of young mammals, but they also can function to regulate the early course of development. Because development consists of series of cascades of gene regulation patterns, an early diversion is capable of setting the pattern of downstream

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Long-Term Maternal Regulation of Development

Figure 2.8 Overlapping life cycles provide a template for inheritance and a matrix for creating variation throughout early development in humans and other mammals. Note. In a single generation, the origin of germ cells (lower left) takes place during the early embryonic period of the mother while she is in the grandmother ’s womb. A number of years later, these germ cells unite at

regulation onto different paths. This can extend the early effects of variations in maternal behavior on infants into adolescence and even adulthood. Moreover, because some of the neural substrates for adult behavior, such as maternal behavior, are present even in infancy, maternal regulation that affects early gene expression in these neural precursors can likewise be modified, with the result that when the systems mature, their patterns of function will be different. In the case of maternal behavior, this inherited environment can result in transmission (inheritance) of an early experience effect from one generation to the next. As evolution continues, if an environmental or social change persists over several generations and can be transmitted across generations by maternally driven developmental processes, then this developmental variation will gradually become genetically modified by repeated selection. Variants in the structure of genes (alleles) responsible for expression of the transcription factors that activate the cascades of gene expression patterns will be selected to the extent that survival and reproductive success is enhanced in these variant individuals. In this way, new and even more adaptive developmental paths will be gradually created over generations because of this “genetic accommodation”

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conception, and development continues within the mother ’s womb and throughout a long period of postnatal proximity and interactions with the mother, grandparents, and other caretakers. Development has been shown to be affected by such extended influences as the age of the father prior to conception and the proximity of grandparents.

of developmentally created novelties. Often an environmental or maternal behavior variable will be maintained by selection as a “trigger” for one or another developmental path, creating alternative phenotypes to be expressed by a singe genotype, greatly enhancing the range of adaptability of the evolving species. The plausibility of this developmental–evolutionary scenario is supported by the widespread existence of alternative developmental phenotypes (reviewed in WestEberhard, 2003) in organisms ranging from phage viruses to humans. In our experiments described above, we revealed the existence of such alternative developmental paths. In the early weaning experiments, we found that the susceptibility to gastric erosions was greatly increased in adolescent and young adults, but older adults were actually less vulnerable than normally reared animals. The whole life trajectory of the trait had been shifted by the social/ environmental trigger of early weaning. Physiologically, the response to early weaning may have been adaptive. Instead of sleeping throughout most of the 24-hr immobilization stress used to induce gastric ulcer as the normally weaned animals did, the early-weaned juveniles remained awake. This altered response could prevent them from

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being surprised by predators in the absence of protection by their mother. The physiological price paid was the risk of gastric ulceration if immobilization was prolonged. Early weaning seems to have also produced long-term changes that altered the uterine environment mothers provided to their young (possibly a hormonal or epigenetic change) that induced the alternative developmental phenotype in their fetuses, through a different mechanism, as a “predictive” response pre-adapting offspring for the expected environment in the next generation. Here, as in the section on maternal regulation of early development, the mother is revealed as a potential transducer of environmental change effects on future generations. Changes in the environment have long been demonstrated to affect maternal behavior, and it is highly adaptive if their young can begin to adapt to changed conditions early in their development (enabling them to become effectively preadapted as adults). Such preadaptations have been studied for several decades by ecologists and evolutionary biologists and are referred to simply as maternal effects (Mousseau & Fox, 1998). A well-known example in insects is that the location in which the female lays her eggs differs according to the season. Females lay their eggs in warmer locations in response to changes in the light in the fall. This reduces the time required before hatching and thus increases survival of the young before the onset of winter. Epigenetic Mechanisms of Long-Term Maternal Regulation as Preadaptation The work of Michael Meaney and his colleagues over the past decade has greatly increased understanding of the biological processes at work in these lasting effects of early relationships, as described in Cameron et al. (2005). These researchers discovered that normal variation among mothers in a colony in the same maternal behaviors implicated in our earlier studies (Myers et al, 1989) of offspring blood pressure development (the level of maternal licking and grooming of pups) systematically modified (regulated) the development of a number of different traits in adult offspring (i.e., adrenocortical stress response, behavioral fear response, measures of learning, and sexual and maternal behavior in adult offspring). Furthermore, these different phenotypes can be produced in offspring as a result of adverse environmental events occurring in the parental generation, such as repeated immobilization of pregnant females. This stress was shown to affect maternal behavior, resulting in physiological and behavioral changes in offspring that preadapted them to more challenging environments (Champagne & Meaney, 2006). The cellular/molecular mechanisms mediating these complex transgenerational effects of variations in maternal

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care have been analyzed in detail by the Meaney group (Weaver et al., 2007). These studies have revealed a novel layer of long-term epigenetic modification of gene expression in offspring caused by maternal care differences acting on the molecular configuration of the chromatin matrix surrounding certain specific genes in the offspring, permanently altering their expression levels and causing longterm effects. Revealing the Structure of Alternative Developmental Pathways In all of these studies just described, alternative developmental pathways appear to be potentially available within the genetic potential of a given strain of rat. One or another of these can be expressed, depending on certain specific social/environmental eliciting or triggering experiences. It occurred to Susan Brunelli and me that such alternative developmental paths or trajectories might also be revealed by repeated selection for high levels of a particular trait in infancy. If the trait were part of a genetically organized and extended developmental pathway, the later stages of that pathway, and any associated traits, should be revealed in adults of the selected line after a number of generations of repeated selection based on the level of the infantile trait. The trait we chose was the infant separation call described previously (see “The Separation Cry”), and we picked a differential selection procedure for the laboratory that was based on certain evolutionary considerations. It is known that sensory and perceptual adaptations to infant USV have evolved in female rodents, for example, an auditory frequency response threshold tuned to the exact frequency of infant USV (45 kHz) that enables the mother ’s sensitive and specific search, retrieval, and caregiving responses (Ehret, 1992). But the infant’s isolation call can also be used by predators to locate the infant. Not surprisingly, predator odors dramatically suppress USV in isolated pups, a specific fear response (Takahashi, 1992). The evolution of the infant separation cry has involved what is known as an evolutionary trade-off, a ratio of risk to benefit that is thought to have shaped many behaviors. In this case, the theory predicts that in environments with many predators, infants that show less separation-induced vocalization will gradually increase in the population. However, when nest disruption and scattering of pups occurs frequently (e.g., through flooding) and fewer predators exist, high rates of isolation calling would be advantageous. To explore some of these hypothetical evolutionary processes and the role of development in them, we have been conducting an experimental model of evolution in the laboratory. We selectively bred adult rats that had shown relatively high or relatively low rates of USV responses to

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27

separation as 10-day-old infants (Brunelli & Hofer, 2007). We found that in as few as five generations, two distinct lines emerged that differed widely on this infantile trait. Cross-fostering showed no evidence that being reared from birth by a mother from the other line changed the level of isolation calling (Brunelli, Vinocur, Soo-Hoo, & Hofer, 1997). Clearly, the selected trait has a strong genetic basis. But because there have been no systematic studies of selective breeding for an infant behavior trait (that we have been able to discover), we did not know how repeated selection might affect the developmental course of separation calling rates in descendents. Furthermore, we wondered whether other traits related to vocalization or to an underlying early anxiety state might also be affected through their genetic, physiological, or behavioral links to the systems controlling the infants’ vocal response to separation. We found that infants’ calling during isolation was elevated in high-line pups over a randomly bred control line starting as early as the response develops at 3 days of age, whereas low-line pups had already decreased their calling rates below controls at this early age (Hofer, Brunelli, & Shair, 2001). The greatest difference between high and low lines was at the age of repeated selection, 10 days postnatal. Response differences were much less evident at 14 days, and the three lines converged as the response ceased to occur in all weanling pups at 18 to 20 days postnatal. Thus, selection resulted in high-line pups showing a marked increase in the already high rates of newborn pups and maintaining this level up to and including the age of selection. However, the low-line pups showed the opposite, a more rapid decline than normal from 3 to 10 days of age. In short, selection at 10 days of age appeared to be acting on the whole developmental trajectory of the vocal response to separation, shifting it in time: either delaying

or hastening the normal gradual decline in isolation calling with age. In more recent studies, as summarized in Figure 2.9, Brunelli found a number of other behaviors and physiological responses at different ages in the high and low lines that had been altered by selection at 10 days of age (Brunelli & Hofer, 2007). Those differences appeared to form two coherent groups of traits. In high-line juveniles tested in isolation at 18 days (when isolation calling no longer occurs), both defecation/urination and (sympathetically mediated) heart rate acceleration were greater than in controls. As adolescents, rough-and-tumble play behavior and the short high-frequency vocalizations that accompany these interactions were reduced in the high-line compared to randomly bred controls in the first few play bouts. Highline adults were significantly slower to emerge into an open test arena and avoided the center region more completely than the low lines. In addition, high-line adults showed a much more passive response to the Porsolt swim test, a pattern associated with depression-like states in rodents. The low-line rats, as they developed into juveniles, showed the greatest heart rate acceleration of the three lines during isolation testing and a much-delayed return to baseline due to major vagal withdrawal. Low-line adolescents were deficient on all play behaviors on all days of testing and emitted the fewest play calls. As adults, the lowline animals were quicker to emerge into the open area, explored its center more than the highs, and were more active in the swim test. When confronted with an unfamiliar male, 70% of low-line males engaged in aggressive behavior compared to 30% of randomly bred controls. These groups of traits suggest a characterization of the high line as anxious and passive, whereas the lows were exploratory, active, and aggressive. Apparently, selection

Figure 2.9 Summary of developmental effects created in two lines of rats by repeated selection, over more than 25 generations, for an infant anxiety trait: either high or low levels of isolationinduced ultrasonic vocalization (USV).

are widely used as representing close equivalents in animals to these two human affective states. Plus signs (⫹) denote increased levels of behaviors, and minus signs (–) denote decreased levels.

Note.“Depression-like” and “anxiety-like” refer to behavioral responses observed in tests that have been validated pharmacologically and that

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for high and low rates of separation calling in infancy, as would occur during evolution under different ecological conditions, selects for a lifelong developmental path involving associated traits. Two alternative phenotypes, or temperaments (as illustrated in Figure 2.9; see also Volume 1, Chapter 15), were created by repeated selection acting at a single time point early in development, suggesting that a genetic potential exists for more than one organized developmental path. A pup could be set on one or another path when alleles for associated traits that enhanced one or another level of the infantile separation calling trait are gradually accumulated by repeated selection. These results resemble the studies from the Meaney group (reviewed in Cameron et al., 2005) in which early environmental change and different maternal behavior patterns had long-term effects on specific patterns of associated traits in adult offspring. Thus, the developmental structures for two or more behavioral phenotypes with adaptive potential are present and can be realized either through selection over 15 or 20 generations, or in a more rapid but shorter-lived transgenerational response to certain types of stress from the parental to the offspring generations.

SUMMARY Scientists’ understanding of development has reached a new phase with the rapid advances in molecular genetics that for the first time allow them to see in detail how development takes place through changes in gene expression over time. These advances have led to an integration of evolutionary and developmental biologies (evo-devo) based in large part on the insight that novel forms are generated in evolution as much through variation in the regulation of genes during development as by changes in gene frequency in populations through natural selection. It is generally agreed that there is still no general theory of development comparable to evolutionary theory (Bateson et al., 2007), but scientists do have a new understanding of the relationship between those two great historical processes of biology. Multicellular development has its origin in the Cambrian explosion of major animal groups (phyla) appearing suddenly in the fossil record 500 million years ago. During and after this major transition in evolution, developmental processes were selected that promoted the construction of larger and more complex organisms capable of inhabiting and exploiting new ecological niches. But these construction processes were also shaped by selection for their capacity to create ontogenetic adaptations that enabled survival of early developmental forms in their own unique and transient environments. Furthermore, developmental

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processes that enabled novel and potentially useful variations in developmental paths that enabled the re-creation of successful paths in the next generation were also selected for their capacity to facilitate the evolution of successful multicellular forms. Identifying these evolutionary selection pressures for construction, ontogenetic adaptation, variation, and inheritance (developmental selection) provides an understanding of the functions being carried out by developmental processes, similar to the understanding gained through identifying the evolutionary functions of stress responses or of social behaviors. In this chapter, I have used the principles of developmental selection in discussing what has been learned recently about the development of early attachment: its prenatal origins, approach and contact maintenance systems, learning of specific maternal discrimination and preference, novel mechanisms for separation responses, and hidden regulators of early development. Finally, I have described the evolution and development of long-term effects of early attachment and the resulting role of mother–infant interaction in the shaping of alternative pathways of development and in the formation of temperament. Another theme of this chapter has been the concept of levels of organization, a way of thinking that is extremely useful in trying to understand how events and processes that are studied by neuroscientists in cells and in brain circuits are related to the observed behavior and inner experiences studied by psychologists. I have tried to illustrate how psychological constructs such as the bond, emotional responses to separation, mental representations and long-term developmental effects deriving from parent–infant interactions can be better understood by learning more about the component neurobiological processes that underlie and have given rise to the psychological constructs and theory. Both the evolutionary developmental approach and the research revealing the component developmental processes of attachment are in the early stages of their own development; there is a great deal still to learn about them. Perhaps the most important gap in this understanding of development is in the area of the self-organizing processes that hold together and guide the seemingly endless series of cascades and networks of gene regulation that underlie development at its deepest level. This “construction” component of development not only embodies as-yet unknown principles of self-organization but is clearly supported, directed, and organized also from the “outside,” by the environment inherited from the previous generation, in ways scientists are only beginning to understand. This environmental matrix begins with the early life of germ cells developing within the parents when they are still embryos, and with the signal proteins and transcription

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References 29

factors formed in the maternal egg prior to fertilization. It continues through the long-term maternal effects exerted by the symbiotic relationship of the embryonic, fetal, and infant stages of development all the way to the “given” properties of the outside environment within which the young must grow up. In addition to these general areas in which scientists know far too little, specific new biological processes are being discovered that experts never knew existed, such as the controls over gene regulation maintained within the genome itself (e.g., the intricate regulation of DNA transcription and translation by the protein “skeleton” of chromatin). Lastly, many mechanisms for variation have evolved within all of these developmental processes as a result of their having enhanced the opportunities for selection. These processes for facilitating variation are just beginning to be studied. The perspective I see is that there is no end of exciting opportunities for research, that the approaches for investigating developmental neuroscience suggested in this chapter are only a beginning, and that these approaches will soon be modified in interesting ways, perhaps by some of the readers of this chapter.

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Chapter 3

Comparative Cognition and Neuroscience CHARLES T. SNOWDON AND KATHERINE A. CRONIN

all, insects, fish, birds, and mammals. Presumably, in environmental conditions where color vision would benefit the reproductive success of individuals, individuals possessing mutations leading to color vision would be more likely to survive and would leave more surviving offspring with the color vision mutation. Many cognitive and behavioral phenomena appear as examples of convergent evolution: families as the basic reproductive unit in many birds, marmosets, tamarins, titi monkeys, and humans; enhanced spatial memory in seed-caching birds and rodents; vocal learning in birds, marine mammals, and humans, and so forth. In this chapter, we first discuss the methods and cautions of comparative studies and then focus on a selection of cognitive phenomena for which there are good comparative data and at least some information on the neural bases. We include social processes among the phenomena that we

Developmental processes are typically viewed from the perspective of an individual’s history, but these processes can also be viewed from the broader historical context of evolution by natural selection. The study of the evolution of cognition and its neural correlates involves the use of a comparative method: the study of multiple species that differ in one factor that may influence the selective pressures on the trait of interest. Factors investigated may include phylogeny (e.g., birds vs. mammals, or apes vs. monkeys), social structure or social organization (e.g., multimale, multifemale groups vs. families), mating system (e.g., monogamy vs. polygamy), group size (e.g., pair with offspring vs. many individuals), foraging behavior (e.g., generalist vs. specialist, or clumped vs. distributed food resources), or ranging behavior (e.g., territorial vs. migratory). Thus, someone interested in the evolutionary origins of language might compare the abilities of humans with those of other great apes. Someone interested in spatial memory might compare two closely related species, one that stores food in many locations and recovers the food later and another that does not store food. The typical view of evolutionary processes is that they diverge. As new species form and become reproductively isolated, they diverge from one another. From this perspective, those species that have shared ancestry and are similar on most variables but differ on some critical one (e.g., monogamous vs. polygamous mating systems in some closely related rodent species) are the comparisons of greatest interest. However, an additional view of evolution is one of convergence (see Figure 3.1). Two quite distantly related species may face similar ecological problems and may independently reach similar solutions. One example is color vision, which has independently appeared in some, but not

Trait variation B

C

D

E

Time

A

Figure 3.1 Convergent and divergent evolution. Letters represent species on this hypothetical phylogenetic tree. The less the horizontal distance between species, the more similar the species are on the trait of interest. Species D and species E are more closely related to each other than are species B and species C. However, species D and species E are less similar on the trait of interest and are an example of divergent evolution, whereas species B and species C are more similar on the trait of interest and are an example of convergent evolution.

We thank Bridget Pieper and Carla Boe-Nesbit for critical comments on the manuscript and Andrew Fox for critical feedback on neural mechanisms of cooperation. Our research was supported by U.S. Public Health Service Grant MH035215 and a Hilldale Professorship from the University of Wisconsin, Madison. KAC was supported by a National Science Foundation Graduate Fellowship. 32

Handbook of Neuroscience for the Behavioral Science, edited by Gary G. Berntson and John T. Cacioppo. Copyright # 2009 John Wiley & Sons, Inc. c03.indd 32

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Methods and Cautions 33

call cognition. Thus, we review cooperation and prosocial behavior, social learning, spatial memory, and pair bonding. An obvious omission is comparative work on bird song learning and its neural mechanisms, which is covered in Chapter 45.

METHODS AND CAUTIONS Choosing Species for Comparison The selection of appropriate species for comparison depends on the nature of the question being addressed. However, often species are chosen on the basis of phylogenetic similarity alone. For example, chimpanzees (Pan troglodytes) and bonobos (Pan paniscus) are the closest relatives of humans, but these apes diverged from humans more than 4.5 million years ago. Behaviorally, chimpanzees differ significantly from bonobos by being more competitive and aggressive, and both species differ from humans in significant ways. Nonetheless, because both species have relatively large brains that are similar in form to those of humans, these species may be good models for comparisons with humans involving some cognitive abilities. However, they may not be good models for comparisons with some human social and emotional processes because the social environments of these species differ greatly from our own. Comparative models might also be chosen from species that are quite different but share some interesting commonality. For example, human fathers often appear to display competence in caring for and bonding with their infants, something rarely, if ever, observed in other great apes. However, fathers in several New World primate species and in some rodents and many birds exhibit spontaneous care of infants and form long-lasting bonds, making these species potentially better candidates than great apes and Old World primates for studying paternal behavior (Snowdon & Ziegler, 2007). As we show here, species with extensive coordination of infant care between different group members are more likely to exhibit social learning (Coussi-Korbel & Fragaszy, 1995) and cooperative behavior (Cronin, Kurian, & Snowdon, 2005). Ecological factors affecting foraging behavior can also lead to differences in rapidity of social learning (Lefebvre & Palameta, 1988). As we also show, social factors or mating systems can lead closely related species, such as monogamous versus polygynous mice and voles, to have very different behavior, hormones, and neural organizations than we would predict from phylogeny alone. Important comparisons may also be made within species. Call and Tomasello (1996) and Thompson, Oden, and Boysen (1997) have shown differences in performance on social learning and second-order analogical reasoning tasks between so-called enculturated chimpanzees that have had extensive

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human interactions during development compared with chimpanzees that have been reared naturally by their own species. It is not clear what aspects of enculturation are important for high-level cognitive function, but the ability to interact comfortably with the humans who are testing the animals may lead these chimps to exhibit better performance than those with less of a history of interaction with humans. Differences within a single species can also inform researchers of developmental influences on cognition. Seasonal differences may also produce behavioral and neural variation within a species or even an individual. Male birds sing under the influence of testosterone stimulated by increasing day length. Furthermore, changes in the size and neural complexity of brain areas track seasonal differences, with consequent changes in rates of singing or song structure and complexity (Alger & Riters, 2006; Nottebohm, 1981; G. T. Smith, Brenowitz, Beecher, & Wingfield, 1997; see Chapter 4 for more details). No Scala Naturae Hodos and Campbell (1969) argued against the concept of scala naturae, a popular misconception that behavioral changes follow a natural phylogenetic scale from earlier evolved species exhibiting simpler behavior to more complex and more recently evolved species necessarily exhibiting more complex behavior. You still can find psychology texts talking about invertebrates having less complex behavior than fish, which in turn are simpler than birds, which are simpler than mammals. The reality of evolution is much messier than a simple linear hierarchy implies. Honeybees have a complex communication system to indicate the location of nectar (Von Frisch, 1950). Salmon remember the odors of their home streams over several years of ocean living and can return to their natal streams to spawn (Hasler, 1966). Clark’s nutcrackers (Nucifraga columbiana) of the U.S. Southwest can store up to 30,000 seeds each fall and can remember the location of enough of these seeds to survive the winter (Balda & Kamil, 1992). Cotton-top tamarins (Saguinus oedipus; Cronin & Snowdon, 2008; Hauser, Chen, Chen, & Chuang, 2003) and common marmosets (Callithrix jacchus; Burkart, Fehr, Efferson, & van Schaik, 2007) spontaneously display cooperative and reciprocal behaviors that a human parent would admire in his or her children, yet chimpanzees display little evidence of similar positive social behavior (Jensen, Hare, Call, & Tomasello, 2006; Silk et al., 2005; Vonk et al., 2008; see following discussion). Vonk and Povinelli (2006) argued that evolution creates no presumption about the phylogenetic distribution of psychological systems among closely related species, and yet, even among presumably sophisticated primatologists who might be expected to have a more subtle appreciation of the

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processes of evolution, we often encounter the idea that primates are somehow more special than other mammals and that apes have cognitive skills surpassing all others. This leads to the frequent dismissal of nonprimate species with complex social structures such as hyenas, wolves, and other social carnivores as well as species more difficult to study, such as marine mammals. In these species one can find evidence of group-specific differences in communication and behavior that might be called culture if seen in apes, as well as coordination of complex social behavior, such as teaching in meerkats (Thornton & McAuliffe, 2006) and communal hunting in hyenas (Drea & Frank, 2003). There is often evidence of clear relationships between aspects of body size, brain size, and cognition. The patterns of allometric relationships may often illuminate interesting exceptions from a strict phylogenetic progression. For example, Figure 3.2 plots body size and encephalization quotient (EQ) for several primate species grouped by phylogeny. There is little variation across New World monkeys, Old World monkeys, apes, and extinct hominoids in brain size as a proportion of body size. However, the two genera with the highest EQs are squirrel monkeys and capuchin monkeys, both New World primates, and one of the genera with the lowest EQ is the gorilla. Note also that modern humans have a significantly greater EQ than any other primate species. A modern reincarnation of the scala naturae is research that uses allometry of brain size to explain the evolution of language. Dunbar (reviewed in Dunbar, 2003) has shown that among terrestrial Old World primates there is a correlation between group size and neocortex size. This suggests that the cognitive demands of social complexity were driving forces in the evolution of brain size (the social

brain hypothesis). Dunbar ’s measure of social complexity was the number of grooming relationships that are possible within a group, as grooming is an essential component of maintaining social relationships in many primates. As group size increases linearly, the number of potential dyadic and higher order relationships increases exponentially. According to Dunbar, typical human groups average 150 in a social cohort, too large to allow individual relationships to be maintained by grooming. Thus, humans have evolved language, using gossip as a proxy for social grooming. In fact, there does appear to be a relationship between typical group size and neocortex volume among the species selected. But is the description of the relationship between group size, social complexity, and brain size adequate? Figure 3.3A shows New World primates that diverged from the ape lineage earlier than Old World monkeys. The monkeys with the largest brains and the largest groups (muriquis, Brachyteles spp.) almost never groom one another (Strier, 1997), whereas the primates with brains with less neocortical volume and the smallest group sizes (marmosets and tamarins) groom extensively. One field study of common marmosets reported adult pairs grooming each other more than 20% of the day (Lazaro-Perea, de Fatima Arruda, & Snowdon, 2004). Figure 3.3B shows that for both New World primates and apes there is a linear relationship between EQ and group size; so the basic relationship between group size and brain size still holds, but not for grooming time. These findings argue against a clear relationship between group size, grooming, social complexity, and the evolution of the neocortex. One lesson from this is that limiting the selection and range of species studied can potentially lead to different

8 New World primates

Encephalization Quotient

7

Old World primates Apes

6

Extinct hominoids 5

Modern humans

4 3 2 1 0 0.1

1

10 Body Weight

Figure 3.2 Encephalization quotient (EQ) plotted against log body weight for several genera of primates. Note the variation within each group of primates (Saimiri and Cebus as New World primates have higher EQ’s than all other

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100

1000

nonhuman primates. Gorillas, furthest to the right, have a small EQ relative to all great apes and most monkeys. Only modern humans have an EQ that is not in the range of other species. (Drawn from data in Aiello & Dean, 1990)

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Methods and Cautions 35 (A)

25

Time Grooming (%)

20

15

10

5

0 0

(B)

20

40

60 Group Size

80

60 Group Size

80

100

120

3.5

Encephalization Quotient

3 2.5 2 1.5 1 0.5 0 0

20

40

conclusions. Another lesson is that brain size is not necessarily synonymous with complexity. As a nonprimate corollary of this, G. T. Smith et al. (1997) found that although the sizes of brain areas related to song production changed with the seasons in song sparrows, some of the change was due to changes in average neuron size and neuron density rather than in the number of neurons. Complexity must be carefully defined, and we should not assume that differences in size must necessarily be related to differences in complexity. Anthropomorphism and Anthropocentrism Many of the problems with the scala naturae view among scientists otherwise quite sophisticated about natural selection and evolution may be due to both anthropomorphism (ascribing human traits to nonhuman animals) and anthropocentrism (seeing cognition in other species through the lens of human abilities). When species are more similar to humans, it may be difficult to avoid ascribing humanlike cognition to these species. Even when we try hard to

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100

120

Figure 3.3 The linear regression from New World primates is shown by a solid line, for Old World primates by a dotted line and for Apes by a dashed line. A: Grooming time as a function of group size in selected genera of New World primates, Old World primates and apes. Regression lines for Old World primates and apes have a positive slope, whereas New World primates show a negative relationship between grooming time and group size. (Data from Dunbar, 1991) B: Encephalization quotient as a function of group size in the same genera showing a positive relationship between brain size and group size for New World primates and apes and no clear trend for Old World primates. EQ data from Aiello and Dean (1990) and group size data from Dunbar (1991).

avoid being anthropomorphic, it is nearly impossible to avoid being anthropocentric. This has led to the idea that increasingly complex social life (as measured by mean group size) must be accompanied by increasingly complex cognitive and social skills (Barrett, Henzi, & Rendall, 2007). As mentioned, social group size may not be a good index of brain complexity, and many species far removed from humans have displayed complex cognition on one or more dimensions in the context of their environment. Furthermore, it may be a mistake to assume that all of human behavior is governed by complex cognitive processes. Many of us engage in automatic behaviors (routines in cooking, driving to work, semantically meaningless phrases used in greetings, etc.). We may be overrating our own cognitive ability simply because we are the species doing the rating. Cognitive complexity must be seen through the lens of the species being studied. None of this is to deny the very real abilities of humans but instead to urge scientists to be more modest when evaluating human abilities (and those of other great apes) compared with those of other species.

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Problem of Plasticity Comparing species on different aspects of cognition is one way to gain a deeper understanding of cognitive processes and cognitive evolution, yet animals, human and nonhuman, are notoriously plastic and are able to modify behavior. For example, ecological differences between captive and wild environments may lead to different behavior within the same species. Leavens, Hopkins, and Bard (2005) have shown that pointing behavior is observed readily in captive chimpanzees interacting with humans to obtain food, whereas pointing has never been observed in the wild. Unlike wild tamarins, captive-born cotton-top tamarins do not exhibit fear when exposed to snakes, a natural predator (Campbell & Snowdon, in press; Hayes & Snowdon, 1990). Captive-born tamarins responded with equal arousal to playbacks of calls of some natural predators and vegetarian howler monkeys, suggesting that initial responses to predator calls may be flexible and may be related to certain acoustic properties rather than innate predator recognition (Friant, Campbell, & Snowdon, 2008). Captive tamarins spontaneously produced alarm calls when fed a familiar food that had been adulterated with pepper, although they had never alarm called at that food previously (Snowdon & Boe, 2003). They also gave mobbing vocalizations toward a caretaker acting as though coming to catch a monkey, despite their lack of arousal toward natural predators (Campbell & Snowdon, 2007). These results on captive chimpanzees and tamarins suggest that captivity represents an ecological niche to which animals may adapt, leading to changes in behavioral contexts different from those in which their wild conspecifics would show similar behavior. Withinspecies comparisons of individuals living under different social or environmental conditions can identify the environmental (as opposed to phylogenetic and genetic) variables that can affect cognitive processing and behavioral expression. This contextual flexibility may paradoxically utilize simpler neural processes than those hypothesized for contextually inflexible cognitive processes. For example, with respect to communication between animals, Owren and Rendall (1997) have argued for flexibility in the contexts in which signals are used and how they are responded to by others. They offered a simple model of affective conditioning by which individuals learn the relationships between signals and affective state. As environments change, organisms can acquire new signal–meaning correspondences. Saffran, Aslin, and Newport (with human infants; 1996) and Hauser, Newport, and Aslin (with cotton-top tamarins; 2001) have demonstrated that human babies and monkeys can learn about statistical regularities in auditory input, a simple but rapid

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way in which organisms can acquire contingencies between sounds. Thus, an animal in a novel context (e.g., captivity) can quickly learn to associate alarm calls with caretakers or veterinarians rather than leopards and snakes, or can learn to eat foods different from those found in the wild and still give appropriate food calls. These basic processes of conditioning provide a general way to learn rapidly about associations of signals and events in the environment without reliance on a host of hypothetical modality and context-specific modules that have been shaped by evolution (Tooby & Cosmides, 1990). Successful adaptation is more likely to occur when organisms can respond rapidly to changes in environment than rely on hard-wired species-specific stimulus–response connections. Two striking examples of plasticity come from work on rodents. For the first, Marler and colleagues studied two species of mice (Table 3.1). The California mouse (Peromyscus californicus) is one of the few species known from field studies to be both socially and genetically monogamous (Ribble, 1991). Males defend their mates and play an important role in infant care, with infant survival being affected by fathers (Gubernick & Tefari, 2000). The white-footed mouse (P. leucopus) is a close relative but is polygamous. These mice do not form pair bonds, and individual males and females may mate with many others. Males provide little, if any, infant care and do not show territorial defense. Bester-Meredith and Marler (2001) successfully cross-fostered these mice and showed that California mice reared by white-footed mice exhibited reduced paternal care and reduced territorial defense behavior, becoming more similar to their foster species. Furthermore, the patterns of distribution of vasopressin (a neuropeptide hormone related to aggression) staining in the brains of cross-fostered California mice more closely approximated the distribution patterns seen in white-footed mice than those seen in their own species. Within California mice, sires that were retrieved less as young retrieved their pups less often when they became adults (Bester-Meredith & Marler, 2001). An experimental increase in the amount of retrievals experienced by California mice offspring led to decreased attack latencies in both males and females and greater vasopressin immunoreactivity in the dorsal bed nucleus of the stria terminalis (Frazier, Trainor, Cravens, Whitney, & Marler, 2006). Cross-fostering (CF) to another species can change not only behavior but also brain organization and brain function. The specific nature of the crossfostering experience that leads to behavioral and neural changes is fairly minimal. In the second example, the work of Meaney and collaborators has shown that variations in maternal licking and grooming in rats (Rattus norvegicus) are related to variations in stress reactivity, with greater amounts of licking

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Social Learning 37 TABLE 3.1 Effects of cross-fostering (CF) on behavior and arginine vasopressin (AVP) brain staining in mice. Species CrossFostered

Territorial Aggression

Neutral Arena Aggression

Paternal Care

AVPImmuno Staining

Whitefooted mouse

No effect

Control < CF

No effect

No effect

California mouse

Control ⬎ CF

No effect

Control ⬎ CF

Control ⬎ CF

Note. From “Paternal Behavior and Aggression: Endocrine Mechanisms and Non-Genomic Transmission of Behavior,” by C. A. Marler, J. K. Bester-Meredith, and B. C. Trainor, 2003, Advances in the Study of Behavior, 32, pp. 263–323. Adapted with permission. Elsevier Science 2003.

and grooming reducing stress reactivity of the pups when they are adults. Furthermore, when offspring become adults they show similar licking and grooming patterns as their mothers. Cross-fostering studies showed these effects to be nongenomic but purely a result of the early grooming and licking received (Francis, Diorio, Liu, & Meaney, 1999). High levels of maternal licking and grooming translated into differences in hippocampal glucocorticoid receptor messenger RNA expression (Liu et al., 1997) as well as increased synapse formation in the hippocampus, increased expression of N-methyl-D-aspartic acid receptors, increased cholinergic innervation of the hippocampus, and enhanced spatial learning and memory (Liu, Diorio, Day, Francis, & Meaney, 2000). Furthermore, high levels of early maternal care are associated with differences in responsiveness of oxytocin receptors to stimulation by estrogen (Champagne, Diorio, Sharma, & Meaney, 2001). That such small but significant differences in early rearing can have profound effects on behavior and brain function in several species should caution us about thinking about behavior as being determined solely by the direct effects of natural selection. Organisms are not static entities but respond flexibly and dynamically to their environments.

SOCIAL LEARNING Social learning (or socially mediated learning) has been studied in a wide range of species. Social learning occurs when the acquisition of behavior is influenced by the activities of other individuals, either directly or indirectly (Box, 1984). Much research has focused on the mechanisms that enable social learning, such as an observer ’s attention being drawn to a particular object as a result of another individual interacting with that object (stimulus enhancement;

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Spence, 1937), or the increased likelihood of performing a familiar motor action after seeing it performed by another individual (response facilitation; Byrne, 1994). More than seven mechanisms that enable social learning have been identified (Whiten & Ham, 1992). One mechanism, imitation, has received the most attention and has been categorized as the most challenging and unique form of social learning (Byrne, 1999). Some have argued that imitation requires theory of mind, or the ability to conceive of the intentions of others (Premack & Woodruff, 1978; see also Stone, this volume). Because theory of mind is widely thought to be an ability restricted to humans and potentially other great apes (Byrne, 1999), it has been argued that imitation is impossible for other taxa (Whiten & Ham, 1992). Miklosi (1999) reported that although some studies have provided data in support of imitative abilities in other species, others have quickly criticized and reinterpreted the results. The prevalence of imitative ability across taxa, and the cognitive mechanisms that underlie imitation, will likely be a hotly debated issue for some time, especially because some researchers with beliefs in scala naturae will cling to the uniqueness of imitation for humans and great apes. In order to evaluate the social learning capability or tendency of a group or species, researchers typically employ one of three methodologies. The first is simply exposing an individual trained to perform a skill to one or more naive individuals and then measuring whether the skill is expressed by the formerly naive individual(s). The second, transmission or diffusion chains, uses the same idea but measures whether the formerly naive individual subsequently transfers the acquired behavior to another naive individual (e.g., Galef & Allen, 1995). The third is the dual action task, in which there are two functionally equivalent methods available for solving a task. Individuals observe one or the other method and then are tested on whether their performance matches the method employed by the demonstrator (e.g., Humle & Snowdon, 2008). Social learning can permit behavioral calibration to the unpredictable properties of an environment with a degree of specificity not permitted by genetically coded information (Galef & Laland, 2005). However, it is a common misconception to assume that social learning is always more advantageous than individual learning, and indiscriminate copying of surrounding individuals is unlikely to be a stable strategy in all species (Boyd & Richerson, 1988; Laland, 2004). Theoretical models have demonstrated that social learning is more effective than individual learning when two conditions are met: (1) the cost of individually acquiring accurate information is high, and (2) knowledgeable individuals are present that have experienced the same environment (Boyd & Richerson, 1988). Therefore,

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we would expect that even closely related species might display different propensities to acquire information socially if they have evolved in different environments. Laland has expanded on these models to argue that natural selection should have favored heuristics dictating when individuals should acquire information socially, and from whom the information should be obtained. Decades ago, Klopfer (1959, 1961) proposed two hypotheses to account for interspecific variation in social learning. He reasoned that solitary species should demonstrate less social learning than gregarious species, and species with conservative foraging strategies should exhibit less social learning than species with opportunistic foraging styles. In fact, most attempts to predict or explain interspecific differences in social learning to date have been based on differences in either sociality or feeding ecology. Influence of Sociality on Social Learning Coussi-Korbel and Fragaszy (1995) presented a model relating social learning to social organization. They argued that behavioral coordination in time and/or space is common to all forms of social learning and that the extent to which behavioral coordination is expressed predicts social learning. An example of behavioral coordination in time (but not space) is a flock of birds feeding simultaneously while the nearest neighbors are at some distance. An example of behavioral coordination in space (but not time) is when an individual approaches a location where a conspecific was previously active but is no longer present. It is only behavioral coordination in both time and space that requires physical proximity between individuals. Differences in the amount of spatial proximity sought out and tolerated by conspecifics vary greatly across species and can often be related to larger social constructs such as dominance structures or mating systems. In despotic species, proximity between conspecifics, particularly near food or desirable items, will be infrequent. In more egalitarian species, however, proximity between conspecifics will be more common. Therefore, the social context in which an individual is immersed is likely to influence opportunities for, and subsequent expression of, social learning. However, it is not always the case that hierarchically organized species demonstrate less social learning. It appears that when the social hierarchy promotes close, constant monitoring of other individuals and their behavior, the probability for social learning may increase because of the increased attention paid to conspecifics. When the attention structure brought about by the hierarchy promotes social learning, it should do so in a heterogeneous fashion (Coussi-Korbel & Fragaszy, 1995). That is, not all

c03.indd Sec2:38

individuals will acquire the socially learned behavior, but rather those that are positioned relatively lower within the hierarchy will be more likely to attend to a demonstrator that is relatively more highly ranked and to subsequently express the modeled behavior. Some studies have made explicit attempts to relate social organization to social learning by performing interspecific comparisons of phylogenetically similar species. Cambefort (1981) compared the discovery and propagation of a feeding skill in chacma baboons (Papio ursinus) and vervet monkeys (Cercopithecus aethiops). The results were also compared with those of a previous study of mandrills (Mandrilli sphinx; Jouventin, Pasteur, & Cambefort, 1976). These three primate species belong to the same family, Cercopithecidae (Nowak, 1999), but exhibit different social organizations. The propagation of the socially learned feeding skill was closely related to the social structure or, more specifically, the attention structure of each species. Vervet monkeys exhibited the weakest hierarchy and group cohesion, and there was no social transmission of the novel foraging skill. In chacma baboons, which exhibited intermediate hierarchy strength, group cohesion, and attention to social partners, there was weak transmission of the novel foraging skill. Finally, in mandrills, which demonstrated the strongest hierarchy, strongest group cohesion, and most attention to social partners, there was fast social propagation of the novel foraging skill. Therefore, the pattern of transmission of the novel behavior mapped onto the hierarchical organization of the group. Another study compared the social transmission of flavor preferences in two hamster species—the golden hamster (Mesocricetus auratus), which is solitary, and the dwarf hamster (Phodopus campbelli), which is moderately social (Lupfer, Frieman, & Coonfield, 2003). When given a choice between a flavor chosen by a demonstrator and another flavor, dwarf hamsters preferred the demonstrator ’s flavor, whereas golden hamsters preferred the demonstrator ’s flavor only if the demonstrator was their mother. In the wild, adult dwarf hamsters interact with one another to share burrows and raise pups. In contrast, golden hamsters rarely interact with one another outside of the mother–pup relationship. Therefore, the degree of social learning expressed may have been predicted by the social organization of the species. A similar finding was reported when the more social pinyon jay (Gymnorhinus cyanocephalus) was compared to the less social Clark’s nutcracker. The individual and social learning abilities of the two species were compared, and the results indicated that pinyon jays learned faster socially than they did individually, whereas the nutcrackers’ performance was not enhanced in the social learning condition (Templeton, Kamil, & Balda, 1999).

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Social Learning 39

Whereas interspecies comparisons provide insights about the effect of the social organization of a species on social learning, intraspecies comparisons allow for investigation of the effects of social relationships within a species on social learning. Nicol and Pope (1994) found that the social transmission of a behavior (keypecking) in flocks of laying hens (Gallus gallus) was influenced by the hierarchy of the group and that social learning was greatest when the demonstrator was a dominant individual. A similar effect of social relationships was found in a study of cooperatively breeding common marmosets. Dyads were presented with a task requiring both individuals to act, whereby one manipulated an apparatus to bring food into the reach of the second individual, who could then retrieve the food. Dyads in which the more dominant individual assumed the role of retriever were more successful at solving the coproduction task. However, in the successful dyads, dominant individuals did not consume more rewards than subordinates, even though they had more direct access to the food rewards (Werdenich & Huber, 2002). Schwab, Bugnyar, Schloegl, and Kotrschal (2008) demonstrated that affiliative relationships among kin enhanced the performance of common ravens (Corvus corax) in a social learning task. Drea and Wallen (1999) reported intriguing results indicating that subordinate rhesus macaques (Macaca mulatta) performed less well on learning tasks when in the presence of dominant individuals, even though they had learned the information equally well. Drea and Wallen (1999) were not investigating social learning specifically, but their findings, combined with those of other studies on the effects of social relationships on performance, indicate that the identity of the demonstrator, as well as by-standers in the experimental setup, likely influences the degree of social learning expressed. Intraspecies comparisons can also elucidate developmental differences in social learning. Dependence on social learning may vary in predictable ways throughout the lifetime of an individual (Galef & Laland, 2005). As mentioned previously, there are likely fewer opportunities for social learning in a species that is rarely in proximity with conspecifics, but during periods of offspring dependence proximity will be more frequent and social learning opportunities may be quite regular. Black rats (Rattus rattus) learn socially to open pinecones to obtain seeds; however, it is only the pups, not adults, that are able to acquire the technique by observing experienced rats (Aisner & Terkel, 1992). Across many studies and species, it seems that juveniles are more likely than adults to incorporate new actions into their behavioral repertoire (e.g., Goodall, 1986; InoueNakamura & Matsuzawa, 1997; Kawai, 1965). Chimpanzees are generally adept at social learning both in the wild (reviewed in Matsuzawa, 2001) and in

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captivity (reviewed in Whiten, Horner, & Litchfield, 2004). Chimpanzee societies exhibit a strict dominance hierarchy, with the most tolerant and longest lasting relationships existing between mother and offspring. An intraspecific comparison of tool use acquisition by young chimpanzees in Gombe National Park, Tanzania, indicated a striking sex difference in the social acquisition of the skill. Females began using tools for termite fishing at a younger age than males, although there was no difference in the behavior of the mother (the model) toward males and females. Young females spent more time watching their mothers use the tools, whereas males spent more time playing at the termite mound. Therefore, the attention paid by the juveniles was an influential factor in determining the onset of the socially learned skill (Lonsdorf, Eberly, & Pusey, 2004). Again, the direction of attention and the close social relationship appears crucial for predicting the occurrence of social learning. Influence of Feeding Ecology on Social Learning Predictions about the prevalence of social learning across species have also been made on the basis of feeding ecology. Specifically, species with opportunistic or generalist lifestyles, in which individuals are exposed to more environmental variation, should be more likely to demonstrate social learning than species that are conservative or specialists (Johnston, 1982; Klopfer, 1961). This hypothesis rests on the reasoning that social learning allows an individual to modify its behavior to the current environment more efficiently or quickly than would be possible by either individual learning or genetically determined behavior. In this sense, social learning is an adaptive specialization shaped by natural selection. The influence of feeding ecology on social learning has been explored experimentally. Klopfer (1961) demonstrated that greenfinches (Chloris chloris) learned a food discrimination task less well in pairs than individually, as opposed to great tits (Parus major), which did not suffer a learning decrement in pairs. He speculated that a failure to learn socially about novel foods would only fail to be maladaptive in species that display conservative feeding habits, such as the great tit. Dolman, Templeton, and Lefebvre (1996) compared two populations of Barbados Zenaida doves (Zenaida aurita) with different foraging styles. One population consistently exhibits conspecific aggression while foraging, whereas the other forages in flocks without aggression. The aggressively foraging species performed better on a social learning task that employed a heterospecific demonstrator, whereas the nonaggressive species performed better with a conspecific demonstrator. In this study, the type of feeding environment appeared to predict

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the pattern of social learning expressed, as the population that typically interferes with conspecifics to compete for food was unable to acquire a socially learned skill from a conspecific. The prevalence of social learning throughout the animal kingdom seems to be best explained by a combination of social, developmental, and ecological factors. Phylogenetic predictions alone appear to do a relatively poor job of explaining interspecific variation in social learning, especially if one considers that comparisons across populations of a single species often lead to as much variation as between-species comparisons. Social Learning and the Brain It is unlikely that the neuronal bases that underlie social learning differ dramatically from mechanisms known to underlie other forms of learning (for a review of the neuronal basis of learning, see Chapter 26). However, an additional pattern of neuronal activation may be unique to social learning and may not be involved in asocial learning such as classical or operant conditioning or trial-and-error learning. Mirror neurons have been identified in macaques (Macaca ssp.) in the ventral premotor and rostral inferior parietal cortex (see Figure 8.11 in Chapter 8 for the approximate location). The defining characteristic of mirror neurons is that they fire both when the animal performs an object-oriented action with its hand or mouth, and when the animal observes another individual (human or conspecific) performing the same motor action (di Pellegrino, Fadiga, Fogassi, Gallese, & Rizzolatti, 1992; Gallese, Fadiga, Fogassi, & Rizzolatti, 1996; Rizzolatti, Fadiga, Gallese, & Fogassi, 1996). A leading hypothesis regarding the function of the mirror neuron system posits that mirror neurons are the basis of action understanding (Rizzolatti, Fogassi, & Gallese, 2001). Indeed, accumulating evidence suggests that the understanding of the meaning of actions determines the discharge of mirror neurons, rather than observations of the actions themselves. Umiltà et al. (2001) demonstrated that the majority of mirror neurons of rhesus monkeys respond during the observation of partially hidden actions, when full visual information about the action is not necessary to recognize the goal. The mirror neuron system also responds to the sound of a goal-related action, even when the monkey cannot see the action (Keysers et al., 2003; Kohler et al., 2002). These observations provide support for the interpretation that the mirror neuron system recognizes actions performed by other individuals and matches these actions on neurons that code the same action, providing the observer with the motoric perspective of the actor. It is through this interpretation that the mirror neuron

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system has garnered the attention of those interested in social learning. A growing amount of information available from EEG, transcranial magnetic stimulation, functional magnetic resonance imaging (fMRI), and positron emission tomography (PET) studies indirectly supports the presence of a mirror neuron system in humans (reviewed in Rizzolatti & Craighero, 2004). However, data suggest that there are some key differences between the mirror neuron systems of macaques and humans. For example, the mirror neuron system of humans, but not rhesus monkeys, responds to intransitive, meaningless movements. Perhaps this difference in sensitivity of the mirror neuron system sheds some light on the different imitative tendencies of humans and monkeys: Human children will “over imitate” (Horner & Whiten, 2005), copying movements that are irrelevant to accomplishing the task. (For a more detailed review of the mirror neuron system, see Chapter 16.) It is tempting to speculate that the mirror neuron system underlies social learning, given that the system allows for matching of the observation of motor actions with their execution. However, more data are needed on the involvement of motor neurons during the observation and acquisition of socially learned skills to understand whether mirror neurons are central to social learning processes. Furthermore, given that there are fundamental differences between macaques and humans in the properties of the mirror neuron system, data from additional species, ideally species that vary in their expression of social learning, will also be necessary to elucidate the role of the mirror neuron system in social learning.

SPATIAL MEMORY Spatial memory can be very important to help animals navigate through their environments to find mates or food. Because closely related species differ in the degree to which they rely on spatial memory, the comparative approach has been extremely powerful for understanding the neural mechanisms that underlie these behavioral differences. Foraging and food-caching behavior vary between species in both corvids (crows, ravens, jays) as well as parids (chickadees, tits, titmice). Some species must store food each fall and remember the locations of storage sites in order to find food over the winter. The Clark’s nutcracker from Arizona has been estimated to store up to 30,000 pine nuts each fall and appears to remember their locations even when the ground cover is transformed by snow (Balda & Kamil, 1992). Because other species within the same family do not need to store food over the winter, some have predicted that the food-caching species will show greater

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Spatial Memory 41

Brain Mechanisms and Spatial Memory The hippocampus is known as a primary region involved in both spatial memory and in transforming short-term to longterm memories. Several studies have shown differences in hippocampal volume, or the structure of neurons within the hippocampus, as a function of ecological differences. Birds from 3 seed-caching families had greater hippocampal volume relative to both body weight and brain volume than birds from 10 families that do not cache seeds (see Figure 3.4; Sherry, Jacobs, & Gaulin, 1992). Hampton and Shettleworth (1996) found large hippocampal volume and better spatial nonmatching-to-sample performance in food-storing black-capped chickadees (Poecile atricapillus) compared with nonfood-storing dark-eyed juncos

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100 Hippocampal Complex Volume (mm3)

spatial learning ability and greater spatial memory than closely related species that do not store food. Several experimental studies support these predictions. Bednekoff, Balda, Kamil, and Hile (1997) showed that Clark’s nutcracker and pinyon jays (Gymnorhinus cyanocephalus) had better memory for seed cache location up to 60 days than noncaching Mexican jays (Aphelecoma ultramarina) and western scrub jays (A. coerulescens), although all species showed good recall up to 250 days after learning. Bond, Kamil, and Balda (2007) found better serial reversal learning performance in highly social pinyon jays compared with Clark’s nutcracker and Western scrub jays. Western scrub jays, in contrast, are able to learn to avoid caches where stored food has spoiled with passage of time, suggesting that scrub jays have a form of declarative memory that involves not only space but also time (Clayton, Yu, & Dickinson, 2001). Therefore, each species of corvid has particular abilities that serve functions appropriate for that species. In rodents, Barkley and Jacobs (2007) found that Merriam’s kangaroo rat (Dipodomys merriami), which hoards food, was more accurate in remembering locations for food than the nonhoarding Great Basin kangaroo rat (D. microps). Monogamous versus polygynous species differ in spatial memory requirements because males of monogamous species have relatively small territories and home ranges relative to those of males from polygynous species that must travel over a wider range to locate multiple females. Gaulin and Fitzgerald (1989) hypothesized that polygynous male meadow voles (Microtus pennsylvanicus) would learn spatial mazes more rapidly than females of the same species, whereas there would be no sex differences between males and females of monogamous pine and prairie voles (M. pinetorium and M. ochrogaster). Using a series of Hebb-Williams spatial mazes, Gaulin and Fitzgerald found a sex difference in polygynous voles with males learning faster, but, as predicted, there was no sex difference in maze acquisition in monogamous voles.

Nonstoring Food storing

10

1 1

10

100

1000

Body Weight

Figure 3.4 Hippocampus volume plotted against mean body weight for 13 families of birds. Open symbols indicate families with some species that store food. Both axes are plotted logarithmically. (Adapted from Sherry, Jacobs, & Gaulin, 1992)

(Junco heyemalis). Within these species, there is also variation in caching behavior, spatial memory, and hippocampal volume. Pravosudov and Clayton (2002) compared chickadees from Alaska (where food resources are scarce) with those from Colorado and found that Alaskan birds cached more food, recovered food more efficiently, were more accurate on spatial (but not on nonspatial) learning tasks and had greater hippocampal volume with more neurons. Migratory subspecies of juncos (which presumably have greater need for spatial memory) performed better than nonmigrating juncos in a spatial memory task and had more densely packed neurons in the hippocampus. Polygynous male meadow voles, which must travel over a greater distance to find multiple females than monogamous males or females of either type, also had larger hippocampal volume than females and than either sex of monogamous pine voles (see Figure 3.5; Jacobs, Gaulin, Sherry, & Hoffman, 1990). As noted previously, polygamous males also were faster to solve multiple spatial maze problems than were polygamous females, whereas monogamous males and females were equal in solving spatial mazes (Gaulin & Fitzgerald, 1989). However, increased hippocampal volume is not universally found in species that do more seed caching. Pravosudov and de Kort (2006) found that noncaching Western scrub jays had similar hippocampal volumes to food-caching European jackdaws (Corvus monedula), and Brodin (2005) found that willow tits (Parus montanus) from Europe had twice the hippocampal volume of blackcapped chickadees but did not differ in food hoarding behavior. We cautioned earlier about accepting size alone as a measure of complexity. Precise measures of neuronal

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42

Comparative Cognition and Neuroscience 700 Male Female

Home Range Size (m2)

600 500 400 300 200 100 0 Meadow Vole

Pine Vole

0.05 Male Female

Hippocampal Volume Relative to Brain Volume

0.049 0.048 0.047 0.046 0.045 0.044 0.043 0.042 0.041 Meadow Vole

Pine Vole

Figure 3.5 Home range size and relative volume of hippocampus in polygamous meadow voles and monogamous pine voles. Male meadow voles have significantly greater home range sizes and hippocampus volume than female meadow voles and monogamous voles of either sex. (Adapted from Jacobs, Gaulin, Sherry, & Hoffman 1990)

number, density, and dendritic fields may be more accurate measures of brain differences across species. Much more than spatial memory may be involved in food caching, such as remembering about food quality and time of storage (Clayton, 1998). This suggests that not only the hippocampus but many other brain areas may be involved in cognitive processes relating to foraging and food caching. COOPERATION, RECIPROCITY, AND DONATION Cooperative behavior emerges numerous times throughout the animal kingdom and, as is the case for social learning, its presence does not fit with a simple phylogenetic or brain size explanation. Cooperative interactions between conspecifics can be divided into two categories based on the distribution of costs and benefits to the actors. In one form of cooperation, cooperation for mutual benefit, the act is beneficial to all individuals involved (West, Griffin, & Gardner, 2007). The second form of cooperation occurs when one individual incurs a cost, albeit potentially temporary, while providing a benefit to a second individual. From an evolutionary

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perspective, this costly form of cooperation is most often accounted for by kin selection (W. D. Hamilton, 1964) or reciprocal altruism (reciprocity; Trivers, 1971). The most commonly observed form of mutually beneficial cooperation is cooperative hunting (Packer & Ruttan, 1988). Data from African wild dogs (Lycaon pictus) and spotted hyenas (Crocuta crocuta) demonstrate that hunting success, prey mass, and the probability of multiple kills increased with the number of adults taking part in the hunt (Creel & Creel, 1995; Drea & Frank, 2003). Mutually beneficial cooperative hunting also occurs among African lions (Panthera leo; Schaller, 1972; Scheel & Packer, 1991). In the open plains of Etosha National Park in Namibia, lionesses have acquired preferential hunting roles within their pride, and hunts were more likely to be successful when the group size was large and huntresses occupied their preferred roles (Stander, 1992). Elaborate displays of cooperative hunting have also been observed in some wild populations of chimpanzees (Pan troglodytes; Boesch & Boesch, 1989; Gilby, Eberly, & Wrangham, 2008). Cooperative hunting is not limited to megavertebrates, however. A South American spider (Anelosimus eximius) weighing approximately 1 mg works in groups of a few to several thousand individuals to construct large basket-shaped webs that cover several cubic meters, allowing the spiders to cooperatively capture and subdue prey up to 30 times their own body size (Rypstra & Tirey, 1991). Mutually beneficial cooperation occurs outside of the hunting context as well. For example, many species exhibit mobbing, which is an antipredator behavior characterized by multiple individuals simultaneously attacking or harassing a predator. This behavior has been observed in mammals, fish, birds, and insects (Bartecki & Heymann, 1987; Curio, 1978; Dominey, 1983; Hennessy & Owings, 1978; Hoogland & Sherman, 1976; Shields, 1984). Other examples of mutually beneficial cooperative behavior include alliance formation, in which two or more individuals combine efforts against a third (reviewed in Dugatkin, 2002); mutual grooming (Dugatkin, 1997); and even cooperative mate acquisition (DuVal, 2007). Cooperative interactions that are not mutually beneficial are more difficult to explain within evolutionary theory. If one actor incurs a cost and a second actor acquires a benefit, the question arises as to why the first actor takes part in the costly act. If the individuals are related, then kin selection is the mechanism most commonly referenced to account for this behavior. It is assumed that costs incurred by the actor are offset by the indirect fitness benefits obtained through increasing the survival of relatives with shared genes (W. D. Hamilton, 1963). Well-documented examples of kin selection include cooperative breeding or helping at the nest, whereby reproductively mature individuals remain in

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Cooperation, Reciprocity, and Donation 43

their natal group to assist in the rearing of younger siblings (behavior in birds reviewed in Brown, 1987; behavior in mammals reviewed in Solomon & French, 1997). If the individuals are unrelated, then reciprocal altruism (Trivers, 1971) is often credited as the mechanism for maintaining the apparently costly behavior. Under reciprocal altruism, one individual incurs a cost and provides a benefit to a second individual at present, and at a later time the benefit is repaid by the second individual. In his seminal paper, Trivers listed the following factors that should increase the likelihood of reciprocal altruism occurring in a species: (a) long life span, (b) low dispersal rate, (c) high degree of mutual dependence, (d) extensive parental care, (e) lack of strong dominance hierarchies, and (f) tendency to aid in combat. Examples of reciprocal altruism in the wild include reciprocal sharing of blood meals by vampire bats (Desmodus rotundus; Wilkinson, 1984), predator inspection by sticklebacks (Gasterosteus aculeatus; Milinski, 1987), and reciprocal grooming in impalas (Aepyceros melampus; Hart & Hart, 1992). However, many have argued that empirical evidence for reciprocity is lacking and that, although the idea of reciprocity is theoretically appealing, its occurrence is extremely rare outside of humans (Hammerstein, 2003; Stevens & Hauser, 2004). Schuster and colleagues (Schuster, 2002; Schuster & Perelberg, 2004) have argued that coordinating behavior with a conspecific, often one involved in cooperative behavior, may be intrinsically rewarding. They posited from experiments on rats that economic analyses of the costs and benefits of cooperative interactions would not provide enough information to an outside observer to determine whether the cooperative interaction was beneficial to an actor because intrinsic rewards also play a role, as they do in other social interactions. This hypothesis remains to be tested on additional species and in varying contexts, but it provides a novel way of thinking about when cooperative behavior may emerge across taxa when economic reasoning falls short of explaining observed cooperative behavior. Investigations of cooperation historically focused on the selective pressures that could lead to its emergence (Alexander, 1974; Axelrod & Hamilton, 1981; Brown, 1983; I. M. Hamilton, 1963; W. D. Hamilton, 1964; Mayr, 1961; Trivers, 1971). However, interest in the proximate mechanisms of cooperation has grown (Brosnan & de Waal, 2002), and this may explain why cooperative behavior emerges in some taxa but not others. Recent studies have included investigations into the actors’ understanding of the partner ’s role in the cooperative act, the effects of various spatial and temporal reward distributions on cooperative performance, the impact of the relationship between actors on their ability to cooperate, the social system of the

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species presented with an opportunity to cooperate, and the cognitive skills required to cooperate. Understanding of the partner ’s role in a cooperative act varies across species. Boesch and Boesch (1989) were the first to call attention to and distinguish between cooperation in which the actors take into account their partner ’s behavior and cooperation in which individuals are mutually attracted to the same resource and act independently of one another. The authors conceptually organized the hunting behavior of wild chimpanzees into four categories of increasing complexity based on the degree to which actors integrated their own behavior with that of their partner. This categorization scheme has been subsequently used to understand partner interactions in many cooperative contexts (Chalmeau, Lardeux, Brandibas, & Gallo, 1997; Chalmeau, Visalberghi, & Gallo, 1997; Cronin et al., 2005; Mendres & de Waal, 2000). Great apes (including humans) appear to understand the role their partner plays in a cooperative act and adjust their behaviors accordingly (Brownell, Ramani, & Zerwas, 2006; Chalmeau & Gallo, 1996a, 1996b; Chalmeau, Lardeux, et al., 1997). Variables used to evaluate understanding of the partner typically include measures of attempts to solve the apparatus in the absence of the partner and glances exchanged between actors. Investigations of whether monkeys understand the role of their partner have produced mixed results both within and across species. Some studies with tufted capuchin monkeys demonstrated that capuchins did not take into account the role of their partner when confronted with a cooperative task. The subjects solved the task but did so by chance alone, as determined by the high number of uncoordinated attempts by each actor (Chalmeau, Visalberghi et al., 1997; Visalberghi, 1997; Visalberghi, Pellegrini Quarantotti, & Tranchida, 2000). Others have argued that capuchin monkeys can solve a cooperative task and take into account their partner ’s role when the task design is intuitive, that is, when the subjects are able to see how the apparatus works and presumably understand the effects their actions have on the apparatus (Mendres & de Waal, 2000). We investigated the extent to which pair-bonded cotton-top tamarins understood the role of their partner in a cooperative problem-solving task and found that tamarins adjusted their behavior based on the presence or absence of their partner (Cronin et al., 2005), providing evidence that cotton-top tamarins are capable of understanding their partner ’s role in a cooperative task. Whether an individual will attend to a partner ’s behavior will likely vary with the amount of behavioral coordination and attentiveness to social cues typically expressed by that species. Intra- and interspecific variation in cooperation may be affected by the relationship between actors, as is the case

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for social learning. As argued by van Schaik and Kappeler (2006), individuals bonded over an extended length of time likely do not evaluate the immediate costs and benefits of their behavior but rather evaluate the long-term benefits and costs exchanged throughout the relationship. Dominance asymmetries may also affect cooperative success, either in the form of coercion by dominants to solve the task or avoidance of the task by subordinates (Chalmeau, 1994; Chalmeau & Gallo, 1996b; Chalmeau, Lardeux, et al., 1997; Tebbich, Taborsky, & Winkler, 1996). Melis, Hare, and Tomasello (2006) have found that tolerance of cofeeding in chimpanzee dyads is predictive of their success on a cooperative task. In addition, the social characteristics of a species (e.g., their characteristic degree of tolerance for nearby conspecifics, behavioral coordination, and mutual dependence) may influence the likelihood of successful cooperation (Snowdon & Cronin, 2007). Trivers (1971) noted that in species with strong dominance hierarchies, the likelihood of reciprocal altruism is reduced. Because of high levels of intragroup competition in Guinea baboons (Papio papio), Japanese macaques (Macaca fuscata), and rhesus macaques (M. mulatta), these species did not coordinate efforts to move heavy food-baited stones (Burton, 1977; Fady, 1972; Petit, Desportes, & Thierry, 1992), whereas Tonkean macaques (M. tonkeana), which are characterized by less strict dominance hierarchies and greater social tolerance, were more often successful at coordinating their actions to displace the baited stone (Petit et al., 1992). Consistent with the idea that social context can predict performance on cooperative tasks are the observations that chimpanzees, characterized by a strict dominance hierarchy and low social tolerance, performed better on competitive tasks than cooperative tasks (Hare & Tomasello, 2004) and that the socially tolerant bonobos outperformed chimpanzees on cooperative tasks (Hare, Melis, Woods, Hastings, & Wrangham, 2007). Attempts to investigate the evolutionary origins of the human tendency to act in the best interest of others (i.e., to act prosocially) have led to interesting results. Initial investigations into the evolutionary origins of prosociality indicated that humans’ closest living relatives, chimpanzees, overwhelmingly refused to donate food to a social partner, even when the potential donor could do so with very little effort and could not obtain the food for itself (Jensen et al., 2006; Silk et al., 2005; Vonk et al., 2008). One interpretation that followed from these findings was that the human tendency to act prosocially must have emerged recently in our evolutionary history because the trait was not present in the common ancestor of chimpanzees and humans. However, as we cautioned earlier in this chapter, convergent as well as divergent evolution should be considered. Some evidence has emerged to indicate that prosocial tendencies may be evident elsewhere in the primate order,

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more specifically in a species that exhibits a social system similar to that of humans: the cooperatively breeding common marmoset. Burkart and colleagues (2007) have shown that common marmosets donated food to conspecifics in a task nearly identical to that used with chimpanzees. The authors interpreted their findings as evidence that a cooperatively breeding social system, one shared by humans and cooperatively breeding marmosets and tamarins, is key to the emergence of prosocial tendencies. Additional research is needed to determine whether the social systems of the species are the most important factor in these findings, as marmosets and chimpanzees differ in many other ways as well. However, these initial results highlight the importance of considering social factors in addition to phylogeny. Discussions of cognitive requirements for cooperation are most often made in relation to the ability to understand the role of the partner, as discussed previously, or the ability to engage in reciprocal altruism. Some have argued that the paucity of empirical evidence for reciprocal altruism is due to its steep cognitive demands (Hammerstein, 2003; Stevens & Hauser, 2004). Cognitive skills hypothesized to be necessary for reciprocal altruism include numerical quantification, time estimation, delay of gratification, detection and punishment of cheaters, analysis and recall of reputation, and inhibitory control (Stevens & Hauser, 2004). To date, these cognitive requirements have been discussed only on theoretical grounds and have not been examined empirically. Others assert that reciprocal altruism does not require complex cognition. Two forms of reciprocal altruism that do not require advanced cognition have been put forth. The first is generalized reciprocity, in which individuals decide whether to cooperate based on prior experiences, irrespective of the identity of the current partner (Pfeiffer, Rutte, Killingback, Taborsky, & Bonhoeffer, 2005). Generalized reciprocity does not require analysis and recall of individual reputations. This concept has been well exemplified in recent studies with Norway rats (Rattus norvegicus; Rutte & Taborsky, 2007, 2008). The second form of reciprocal altruism is symmetry-based or attitudinal reciprocity. Symmetry-based reciprocity occurs when closely bonded individuals help one another without stipulating returns. Because of the symmetrical and long-lasting characteristics of the relationship, the benefits generally balance out over a lifetime without the need for any purposeful “scorekeeping” (de Waal & Luttrell, 1988). We have found that unrelated, pair-bonded cottontop tamarins not only cooperate for mutual rewards but also cooperate when rewards are reciprocally distributed between actors and when rewards are repeatedly received by a single individual in the dyad. The tamarins were sensitive to the reward scenarios, cooperating most consistently for mutual rewards and least consistently when the rewards repeatedly went to the same individual, but cooperation

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Cooperation, Reciprocity, and Donation 45

persisted nonetheless (Cronin et al., 2005; Cronin & Snowdon, 2008). The tamarins’ cooperative performance in this study may have been due to attitudinal reciprocity as described by de Waal and Luttrell (1988); that is, the tamarins were cooperative with their long-term mates without keeping track of the exact costs and benefits incurred. Further studies of cooperation in tamarin dyads that differ in their social relationships are needed to further test this interpretation. As with the other phenomena discussed here, cooperative behavior emerges throughout the animal kingdom with little regard for phylogeny. Investigations into the social organizations of species and the social relationships within species have proved the most fruitful for explaining and predicting species and individual differences. Unfortunately, little is known about the neural processes that underlie cooperative interactions in nonhuman animals. However, scientists can gain some insight and begin to make predictions about neural involvement from recent studies performed on the most cooperative species: humans. Cooperation and the Brain Studies emerging from the nascent field of neuroeconomics have begun to elucidate some of the regions of the brain involved in human cooperative behavior (see also McCabe, this volume). These studies have primarily used fMRI data collected while humans engage in cooperative games. However, a single cooperative interaction necessarily includes multiple sequential stages, including the stage in which an individual decides whether to cooperate and the stage in which he or she experiences the outcome of the

cooperative interaction. Each stage is likely to recruit different brain regions, and fMRI investigations have aimed to isolate different stages of cooperative interactions. A recent fMRI study utilized the prisoner ’s dilemma to evaluate neural activation during the initial stage of cooperation, when participants decided whether to cooperate with a confederate. The prisoner ’s dilemma is a widely used game in which two individuals are given the simultaneous choice either to cooperate with the other individual or to defect. The payoff to the individual depends on his or her own choice and the choice of the partner, which is revealed once both individuals have made their choices. Regardless of the other player ’s choice, the choice to defect yields a higher payoff than the choice to cooperate. But if both players defect, both do worse than if both had cooperated (see Figure 3.6A). Rilling et al. (2007) contrasted activations when the participant decided to cooperate with those when the participant decided to defect. Results indicated that the choice to defect was associated with stronger activation in the rostral anterior cingulate cortex (rACC) and the dorsolateral prefrontal cortex (DLPFC). The activation of the rACC and the DLPFC is interesting because the DLPFC is often associated with executive control and goal maintenance and the rACC with the processing of aversive experiences and the detection of cognitive conflict. This pattern of activation seems to indicate that defection is not a simple, favorable choice by participants. The choice to cooperate was correlated with activation in the orbitofrontal cortex (OFC; see Figure 8.12 in Chapter 8). The OFC has been implicated in the processing of rewarding and punishing factors and in emotional decision making

Cooperate Defect

Player A

Player B Cooperate

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R⫽3

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Figure 3.6 A. Prisoner’s dilemma matrix. Payoff awarded to Player A is the top value within each cell; payoff awarded to Player B is the bottom value within each cell. Different payoff values can be used, however this rule must be followed: T > R > P > S. B. Trust game diagram. Each node represents a point at which a player must make a decision. At the first node, Player 1 either

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P1: $0 P2: $405 P2 decision P1 decision

P1: $180 P2: $225 P1: $45 P2: $45

(B)

decides to split the benefits equally, or to allow Player 2 a move. If Player 1 decides to allow Player 2 a decision, the pot increases and both the potential gain and loss increase for Player 1. Player 2 then decides between an option that allows both players rewards but provides him/herself with a greater reward, or an option that provides him/herself with all the rewards. Different payoff values can be used. (Diagram modified from McCabe et al., 2001).

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(Bechara, Damasio, & Damasio, 2000; Ichihara-Takeda & Funahashi, 2006). Furthermore, Rilling et al. (2007) found a negative correlation between OFC and DLPFC activation that they interpreted as interplay between emotional and cognitive control leading up to the execution of cooperation or defection. Additional insight into the brain regions recruited during the decision to cooperate with another individual comes from fMRI investigations of humans engaged in a trust game that required individuals to decide whether to invest money in a second individual who could increase or decrease the payoff to the first individual (see Figure 3.6B). The investigation revealed that those participants who consistently attempted cooperation exhibited more specific prefrontal cortex (PFC) activation when playing the game with a human as compared to a computer employing a fixed, known probabilistic strategy (McCabe, Houser, Ryan, Smith, & Trouard, 2001). However, differential PFC activation between social and nonsocial versions of the game was not found for individuals who rarely cooperated. Using another version of the trust game, King-Casas et al. (2005) reported that the magnitude of neuronal activity in the dorsal striatum correlated with the intention to trust the other player, and the peak of the response shifted earlier in the decision process as player reputations developed in this reciprocal game. Rilling et al. (2002) found that different patterns of neural activation determined by whether the playing partner was identified as a computer or a human. Greater activation was observed in the anteroventral striatum, the rACC, and the OFC during social cooperation than during cooperation with a computer. The OFC and ventral striatum are recruited while one is anticipating an economic reward (e.g., Padoa-Schioppa & Assad, 2006; Roesch & Olson, 2004; Schultz, Dayan, & Montague, 1997), however Rilling et al. (2002) controlled for monetary gain in the social versus nonsocial contrast. Some investigations have specifically examined the differences in neural activation following a potentially cooperative interaction. Using the prisoner ’s dilemma and fMRI, Rilling, Sanfey, Aronson, Nystrom, and Cohen (2004) found that participants displayed increased neural activity in the ventral striatum in response to reciprocated interactions and decreased neural activity in the ventromedial PFC in response to unreciprocated interactions. The authors interpreted these results to indicate that the mesolimbic dopamine system processes errors in predictions about whether a social partner will act reciprocally. In fact, many of the regions recruited during cooperative interactions are the same regions classically involved in processing reward. The neural circuitry often involved in reward includes the caudate and particularly the ventral striatum, amygdala, and the medial and orbital PFCs

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(reviewed in Cardinal, Parkinson, Hall, & Everitt, 2002; see also Volume 2, Chapter 40). It is interesting that even when the effects of tangible rewards are controlled, social cooperative interactions continue to engage some of the reward circuitry, suggesting that cooperating with another individual may be intrinsically rewarding. Similarly, Harbaugh, Mayr, and Burghart (2007) found that voluntary monetary donations also increase the neural activity of reward regions. It seems that data obtained from neuroscientific investigations as well as behavioral observations (i.e., Schuster, 2002; see above) converge to support the hypothesis that acting cooperatively with conspecifics is rewarding, and because these regions are rich in dopamine, it is likely that dopamine is involved in these cooperative interactions. The neuropeptide oxytocin may also be involved in cooperative social interactions. Oxytocin is a nonapeptide produced in the supraoptic and paraventricular nuclei of the hypothalamus. Oxytocin is released into the periphery from magnocellular neurons of the supraoptic nuclei, which project to the posterior pituitary. In addition, oxytocin is released centrally from parvocellular neurons of the paraventricular nuclei (Uvnas-Moberg, 1998). Oxytocin has been identified repeatedly in the coordination of positive social interactions in a wide range of species (reviewed in Uvnas-Moberg, 1998). In addition to having a well-known role in the onset of uterine contractions, milk letdown, and mother–infant bond formation (Gimpl & Fahrenholz, 2001; Levy, Kendrick, Goode, Guevara-Guzman, & Keverne, 1995; Nelson & Panksepp, 1998), oxytocin facilitates the formation and maintenance of important relationships outside the mother–infant context. Oxytocin is integral to the formation of pair bonds in monogamous voles (Carter, 1998; Carter, DeVries, & Getz, 1995; Young, 1999; see Box 3.1 and below). Oxytocin correlates with the expression of trust in humans (Zak, Kurzban, & Matzner, 2005) and increases the amount of generosity (donation) in humans (Zak, Stanton, & Ahmadi, 2007). Furthermore, treatment with oxytocin increases one’s willingness to accept risk in interpersonal interactions (Kosfeld, Heinrichs, Zak, Fischbacher, & Fehr, 2005). Because of the involvement of oxytocin in positive social interactions, some have begun to speculate that oxytocin may play a role in prosocial behaviors such as cooperation, reciprocity, and donation (i.e., Rutte & Taborsky, 2007). The term cooperation is used in different ways by economists, psychologists, and ethologists, and, as we have shown, investigations of cooperative behavior have spanned studies of individuals acting altruistically, engaging in reciprocity, taking risks for kin, providing benefits to mates, and engaging in economic games. The common link in all of these forms of cooperation is that they provide a benefit of some sort for another individual, and the most

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Pair Bonds

BOX 3.1 OXYTOCIN AND VASOPRESSIN Oxytocin (OT) and arginine vasopressin (AVP, also known as antidiuretic hormone) are closely related in structure but have different physiological roles. Both are made of nine amino acids (peptides) folded into a ring, and the two hormones differ in only two amino acids. These hormones are released into the blood from the posterior pituitary gland at the base of the brain, but they are also released from neural tissue in the hypothalamus and are active in several areas of the brain. Until 20 years ago, the best known role for these hormones was in the regulation of peripheral physiological processes. OT is involved in uterine contractions at birth (and indeed a synthetic version of OT is frequently used to induce labor) and in the milk letdown reflex in nursing. AVP is involved in the contraction and relaxation of smooth muscle (e.g., blood vessels) and regulates transport of water and sodium across cell walls, especially in the kidney. In recent years, other behavioral functions of these hormones have become widely known. OT is involved in the formation of pair bonds in monogamous female rodents (Carter, 1998), and AVP is involved in pair bond formation in monogamous male rodents. AVP is also involved in the aggressive behavior shown by territorial monogamous male rodents (Marler et al., 2003). AVP also plays a role in paternal behavior in monogamous rodents (Marler et al., 2003), and OT plays an important role in regulating female sexual behavior and maternal behavior. Both hormones are anxiolytic (i.e., they reduce anxiety), and release of OT during deep massage, stroking, and sexual intercourse has been hypothesized to play an important role in social bonding in humans. Indeed Gonzaga, Turner, Keltner, Campos, and Altemus (2006) found that OT levels increased during displays of romantic love, but not sexual arousal, in college women, suggesting a role of OT in human pair bonding and social reinforcement. information known so far about the brain has come from humans engaged in neuroeconomic games. However, these patterns of neuronal activation provide just a starting point for investigations of other forms of cooperative behavior. Much more information is needed to determine whether species that typically cooperate with one another, such as cooperatively hunting hyenas, have regional specializations or patterns of neuronal activation that differ from those of species that do not typically cooperate with one another. If a comparative neuroscience approach were to

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be applied to cooperative behavior, one might speculate that in species in which it is beneficial to act cooperatively with conspecifics, cooperative behavior will have become associated with the brain’s reward system throughout the evolutionary history. But hypotheses of this sort remain to be tested.

PAIR BONDS In many mammalian species, close affiliative relationships between males and females outside of mating are rare. The closest relationships are found in species with biparental or cooperative care such as prairie voles, titi monkeys, tamarins, and marmosets. In mammals, females incur the costs of gestation and lactation, and males can never be certain of paternity of infants. Under such conditions, it has been assumed that males will be more successful attempting to fertilize as many females as possible rather than investing in parental care. However, in species with biparental or cooperative care, fathers often play a critical role in infant survival, and thus the reproductive success of both parents depends on joint infant care. Biparental, socially monogamous species are expected to have closer, more affiliative social relationships than polygamous species, and it is, in fact, under these conditions that close relationships between mates are found. The formation of the relationship has been well studied. Williams, Catania, and Carter (1992) found that prairie vole females cohabiting with a male for 24 hours with or without mating developed a strong bond. Cohabitation with mating for less than 24 hours, but not cohabitation alone, also led to pair formation. Savage, Ziegler, and Snowdon (1988) found that newly paired cotton-top tamarins spent more time in contact, in grooming, and engaged in sexual activity than did established pairs, with males initiating affiliation more often than females. Widowski, Porter, Ziegler, and Snowdon (1992) found that exposure to a novel cottontop tamarin male with no direct physical contact led to ovulation in reproductively suppressed female cotton-top tamarins. Schaffner, Shepherd, Santos, and French (1995) also found that male black tufted-ear marmosets (Callithrix kuhli) initiated more proximity behavior in the first 40 days after pairing and that sexual behavior decreased over time. Silva and Sousa (1997) studied variation in pair formation in sexually naive common marmosets and found that those that became pregnant within the first 10 weeks had more affiliative behavior, especially grooming, and greater coordination of behavior than those pairs that did not conceive immediately. This is the only study on the reproductive consequences of within-species variation in pair formation.

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Determination that a pair bond exists requires some experimental documentation of attachment: that an animal is distressed upon separation from its mate and displays greater affiliation than baseline upon reunion, that an individual will behave aggressively toward intruders, or that an individual will preferentially seek contact with its mate when given a choice between conspecifics. Mendoza and Mason (1986b) found greater disturbance, more aggression to intruders, and higher cortisol levels in the monogamous titi monkey (Callicebus moloch) compared to the polygynous squirrel monkey (Saimiri sciureus). When separated and offered a choice between mate and infant, titi monkeys chose their mates preferentially. Infants that were separated from both parents chose fathers over mothers (Mendoza & Mason, 1986a). Subsequently, when paired adult titi monkeys were separated for 30 min or 5 days and tested with either their mate or an opposite-sex stranger, male and female titi monkeys showed equal affiliation upon reunion with the mate. In contrast, they showed high levels of arousal when placed with an opposite-sex stranger. These results suggest strong and lasting relationships (FernandezDuque, Mason, & Mendoza, 1997). Cotton-top tamarins also exhibit distress when separated from mates for short time periods, with increased rates of long calling (which is used for within-group cohesion and by lost animals) during the period of separation and increased affiliative and sexual behavior when reunited after separation (Snowdon & Ziegler, 2007). Males are more vocal and appear more disturbed by separation than females. They also display levels of aggression toward intruders of both sexes, with males displaying equal amounts whether the mate is present or absent and females displaying higher levels only in the presence of the mate. Males also groom females significantly more often than females groom males (Snowdon & Ziegler, 2007). All of these results suggest that males are more responsible for maintaining relationships than females. Partners in pair-bonded species can also buffer against the effects of stress. T. E. Smith and French (1997) tested male and female tufted-ear marmosets in novel environments and found that levels of cortisol were lower when pairs were together in a novel environment than when they were alone. Rukstalis and French (2005) played back calls of the mate to isolated marmosets and found that the calls alone reduced cortisol levels as well as stress-related behavior. In summary, lasting adult heterosexual affiliative relationships are found in a few species that are socially monogamous or cooperatively breed. Several studies have looked at behavioral changes during pair formation and have evaluated attachment through studies involving challenges by unfamiliar animals of same and opposite sex and studies involving separation and reunion. The individual

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recognition of a specific individual as a mate and the differential response to the mate than to other potential mates are also key components of a pair bond. What are the neural and hormonal mechanisms that maintain these relationships? Role of Oxytocin and Vasopressin Oxytocin is a neuropeptide that has been implicated in affiliative relationships. In the past 15 years, extensive research, primarily on monogamous voles, has indicated the importance of oxytocin in formation of affiliative relationships, especially in females (for reviews, see the following: mother–infant relationships, Nelson & Panksepp, 1998; and heterosexual adult relationships, Carter, 1998; Carter et al., 1995; Insel, 2003). Oxytocin, but not the closely related nonapeptide vasopressin, appears critical for female prairie voles in relationship formation (Insel & Hulihan, 1995). However, vasopressin levels increase in male prairie voles after pair formation, suggesting a sex difference in which hormones are involved in a pair bond for these rodents (Winslow, Hastings, Carter, Harbaugh, & Insel, 1993). In non-pair-bonding species of voles, oxytocin is not effective for inducing a pair bond and has a different distribution of receptors in the brain (reviewed by Young, 1999). Nonetheless, oxytocin infused chronically into male rats (a polygamous species) led to increased social interactions (Witt, Winslow, & Insel, 1992). Pedersen and Boccia (2002) found that oxytocin was critical for initiation and maintenance of sexual behavior in female rats. Oxytocin injected into the medial preoptic area of male rats also facilitated social recognition, whereas vasopressin did not (Popik & van Ree, 1991). However, Dantzer, Koob, Bluthé, and Le Moal (1988) reported that vasopressin in the septal region facilitated social memory in rats. Oxytocin knockout mice fail to recognize familiar conspecifics, and oxytocin administered to the medial amygdala prior to initial exposure facilitated social recognition (Bielsky & Young, 2004; Ferguson, Aldag, Insel, & Young, 2001). Rosenblum et al. (2002) compared highly affiliative bonnet macaques (Macaca radiata) with socially distant pigtail macaques (M. nemestrina) and found that the more affiliative bonnet macaques had higher cerebrospinal fluid oxytocin levels and lower corticotrophin-releasing hormone levels than the pigtail macaques. Winslow, Noble, Lyons, Sterk, and Insel (2003) reported that mother-reared male rhesus macaques had elevated cerebrospinal fluid oxytocin levels compared with nursery-reared macaques, and there was a significant correlation between oxytocin levels and affiliative behavior. In contrast, vasopressin levels did not differ with rearing condition but correlated positively with fearful behavior in rhesus macaques.

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Pair Bonds

Interest in oxytocin in humans has increased recently because of the role of oxytocin in both mother–infant attachment and adult affiliative behavior. Wismer Fries, Ziegler, Kurian, Jacoris, and Pollak (2005) found that children had elevated oxytocin levels when performing a task that included contact by the mother but not contact by an unfamiliar female. In contrast, children adopted from Eastern European orphanages several years previously showed low oxytocin responses to both adopted mothers and an unfamiliar female, indicating that early experience affects the oxytocin system. Oxytocin administered intranasally increases trust among humans (Kosfeld et al., 2005; Zak et al., 2005). Infusions of oxytocin into people with autism and with Asperger syndrome reduced repetitive behavior (Hollander et al., 2003) and increased retention of social cognition (Hollander et al., 2006). These studies support the role of oxytocin in producing calm behavior and facilitating social memories in humans. Turner, Altemus, Enos, Cooper, and McGuinness (1999) found that women asked to recount a negative experience of loss or abandonment showed decreased levels of serum oxytocin correlated with the degree of negative emotion expressed. Using data from Turner et al. (1999), Gonzaga et al. (2006) reported that women engaging in affiliation signals with a romantic partner had a significant positive correlation between the amount of positive signaling and serum oxytocin. Grewen, Girdler, Amico, and Light (2005) reported higher plasma oxytocin in both men and women in relationships with strong partner support. Thus, changes in oxytocin appear to track affiliative and positive emotional states in both sexes and across many species. In humans, peripheral measures of oxytocin levels appear to correlate with differences in affiliation, and peripheral infusions of oxytocin can increase affiliative behavior and social memory. Most comparative research on pair bonds has involved evaluating species differences; however, within-species individual differences in relationship quality and hormonal correlates are found in nonhuman primates as well. We have observed a fivefold variation in the amount of affiliative behavior between pairs of cotton-top tamarins and have also observed a fivefold variation in urinary oxytocin levels in both sexes that correlates with the amount of affiliative behavior expressed. Variation in sexual behavior explains most of the variance in male oxytocin levels, whereas variation in contact and grooming explains most of the variance in female oxytocin levels (Snowdon et al., in preparation). In summary, there is clear evidence for the importance of oxytocin for pair bonding in females of monogamous vole species and for the importance of vasopressin in male voles. Oxytocin can supersede the effects of mating

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in pair bond formation. Oxytocin affects sexual behavior in female rodents and macaques and also appears to be important for social recognition in male rodents. In rodents, tamarins, and human primates, peripheral measures of oxytocin appear to be correlated with affiliation, attachment, trust, and positive emotions, and peripheral administration of oxytocin can alter affiliative behavior. Brain Mechanisms Comparative work between monogamous and polygynous voles has shown that monogamous female voles but not polygamous voles have increased oxytocin receptor density in the nucleus accumbens and caudate putamen and greater vasopressin receptor density in the ventral pallidum (Table 3.2; Young & Wang, 2004). Little is known about oxytocin distribution in monogamous, pair-bonded primate species, but Wang, Moody, Newman, and Insel (1997) found that there were no sex differences in the distribution of immunoreactive oxytocin neurons and fibers in the common marmoset. Oxytocin immunoreactive neurons were found in the paraventricular and supraoptic nuclei of the hypothalamus, the bed nucleus of the stria teminalis, and the medial amygdala. Vasopressin cells were found in the paraventricular, supraoptic, and suprachiasmatic nuclei and in the lateral area of the hypothalamus. The only sex difference was that males had a greater density of vasopressin reactive cells in the bed nucleus of the stria terminalis than females. In a biparental species, in which both parents are essential for infant care and both sexes contribute to the formation and maintenance of a pair bond, it is reasonable to find no sex differences in distribution of oxytocin-reactive neurons in the brain. In further work on common marmosets, Wang, Toloczko, et al. (1997) found vasopressin receptor binding in the nucleus accumbens, diagonal band, lateral septum, bed nucleus of the stria terminalis, amygdala, and anterodorsal and ventromedial hypothalamus, in addition to areas with immunoreactivity. Marmosets differed from voles in important ways: No vasopressin-producing cells were found in the amygdala. There was no plexus of immunoreactive fibers in the lateral septum. There was much greater visualization of vasopressin immunoreactive cells in the bed nucleus of the stria terminalis. Taken together, the results on marmosets suggest relatively little sexual dimorphism, an extensive overlap of oxytocin and vasopressin immunoreactive cells, and a different distribution than that found in rodents. So far, pair formation and maintenance has been treated as a unitary, species-specific trait, and yet there may be considerable individual variation within species, as we have noted. Recently, Hammock and Young (2005) described variation in social engagement and affiliative

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licking and grooming suggests the possibility for an interaction between early experience and gene expression.

TABLE 3.2 Oxytocin and arginine vasopressin receptor distribution as a function of mating system in voles and monogamous common marmosets. Brain Area

Monogamous vs. Polygamous Volea,b

Monogamous Primatec

SUMMARY

Oxytocin Nucleus accumbens

Monogamous

NA

Prelimbic cortex

Monogamous

NA

Bed nucleus of stria terminalis

Monogamous

NA

Midline thalamus

Monogamous

NA

Ventral reunions

Monogamous

NA

Lateral amygdala

Monogamous

NA

Central amygdala

Both

NA

Lateral septum

Polygamous

NA

Ventromedial hypothalamus

Polygamous

NA

Diagonal band

Monogamous

Yes

Laterodorsal thalamus

Monogamous

No

Central amygdala

Monogamous

Yes

Basolateral amygdala

Monogamous

Yes

Bed nucleus stria terminalis

Monogamous

Yes

Nucleus accumbens

Both

Yes

Accessory olfactory bulb

Both

No

Superior colliculus

Both

No

Lateral septum

Polygamous

Yes

Periventricular hypothalamus

NA

Yes

Ventromedial hypothalamus

NA

Yes

Suprachiasmatic hypothalamus NA

Yes

Vasopressin

a Oxytocin data from “Oxytocin Receptor Distribution Reflects Social Organization in Monogamous and Polygamous Voles,” by T. R. Insel and L. E. Shapiro, 1992, Proceedings of the National Academy of Sciences, USA, 89, pp. 5981–5985. b Vasopressin data from Insel, Wang & Ferris (1994). c Marmoset vasopressin data from “Vasopressin in the Forebrain of Common Marmosets (Callithrix jacchus): Studies with in situ Hybridization, Immunocytochemistry and Receptor Autoradiography,” by Z. Wang, D. Toloczko, L. J. Young, K. Moody, J. D. Newman & T. R. Insel, 1997, Brain Research, 768, pp. 147–156. Note. NA ⫽ not reported or known.

behavior in monogamous male prairie voles and correlated this variation with a polymorphism in the promoter region of the vasopressin 1a receptor. Thus, variation in the promotor region of the receptor genes, and consequently the expression of vasopressin 1a receptors, may account for behavioral variation within a species. Similar studies remain to be done with respect to oxytocin receptors. The variation in receptor sensitivity to estrogen described by Champagne et al. (2001) as a function of early maternal

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We have demonstrated that both the proximate and ultimate mechanisms contributing to inter- and intraspecific variation across a wide range of cognitive phenomena can be best understood through a comparative lens that integrates not only phylogeny and brain size but also social and ecological factors. In our discussions of social learning, cooperation, spatial memory, and pair bonding, we have emphasized strong theoretical predictions about socioecological influences on the expression of these behaviors within and across taxa, and, when data have been available, we have highlighted underlying neuronal and hormonal differences that may relate to the observed variation. One major implication of this comparative perspective is that no single species will likely serve as a suitable model for understanding the myriad of interesting human cognitive abilities. For example, there is strong momentum to use various species of macaques as model species for humans in neuroscience, but we should keep in mind the potential implications of the differing social and ecological pressures that macaque and human lineages have faced since the divergence of our ancestry nearly 40 million years ago. Awareness of the cognitive domains in which macaques and humans would be expected from a socioecological perspective to differ may sharpen interpretations of findings from macaque studies and generate interesting hypotheses about interspecific variation if we were to look beyond humans and macaques. To better understand the selective pressures that have contributed to the wide range of expression of cognitive abilities across taxa, we must embrace data collected on primates and nonprimates alike and rely on multifaceted hypotheses that incorporate social and ecological pressures as well as phylogeny and brain size. Interdisciplinary collaboration is integral to accomplishing this feat but should yield a more comprehensive and generalizable understanding of both human and nonhuman cognition.

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Williams, J. R., Catania, K. C., & Carter, C. S. (1992). Development of partner preferences in female prairie voles (Microtus ochrogaster): The role of social and sexual experience. Hormones and Behavior, 26, 339–349. Winslow, J. T., Hastings, N., Carter, C. S., Harbaugh, C. R., & Insel, T. R. (1993, October 7). A role for central vasopressin in pairbonding in monogamous prairie voles. Nature, 365, 545–548. Winslow, J. T., Noble, P. L., Lyons, C. K., Sterk, S. M., & Insel, T. S. (2003). Rearing effects on cerebrospinal fluid oxytocin concentration and social buffering in rhesus monkeys. Neuropsychopharamacology, 28, 910–918. Wismer Fries, A. B., Ziegler, T. E., Kurian, J. R., Jacoris, S., & Pollak, S. D. (2005). Early experience in humans is associated with changes in neuropeptides critical for regulating social behavior. Proceedings of the National Academy of Sciences, USA, 102, 17237–17240. Witt, D. M., Winslow, J. T., & Insel, T. R. (1992). Enhanced social interactions in rats following chronic, centrally infused oxytocin. Pharmacology, Biochemistry, and Behavior, 43, 855–861. Young, L. J. (1999). Oxytocin and vasopressin receptors and speciestypical social behaviors. Hormones and Behavior, 36, 212–221. Young, L. J., & Wang, Z. (2004). The neurobiology of pair bonding. Nature Neuroscience, 7, 1048–1054. Zak, P. J., Kurzban, R., & Matzner, W. T. (2005). Oxytocin is associated with human trustworthiness. Hormones and Behaviour, 48, 522–527. Zak, P. J., Stanton, A. A., & Ahmadi, S. (2007). Oxytocin increases generosity in humans. Public Library of Science, 2(11), E1128.

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Chapter 4

Biological Rhythms LANCE J. KRIEGSFELD AND RANDY J. NELSON

“a rose is not necessarily and unqualifiedly a rose . . . it is a very different biochemical system at noon and at midnight.” —Colin Pittendrigh, 1965

When it comes to success, the expression “timing is everything” is an oft-touted mantra. Traditionally, however, timing was not considered important in the study of the mechanisms underlying behavior. Yet, behavioral constructs such as learning, memory, sensation, perception, attention, and motivation vary markedly according to the time of day or season of the year. Motivation can be defined generally as why individuals do what they do. From a neurobiological perspective, an animal eats because its hunger circuits are activated by specific neurochemicals. However, the response of circuits underlying motivated behaviors is quite different given time of day or year, and it is essential to consider timing when asking questions about the biological mechanisms underlying behavior. To add to this example, an animal eats at night in response to the impact of its internal biological clock on the responsiveness of hunger circuits to specific neurochemical signals. Despite the impact of timing systems on brain and behavior, their influence on dependent measures of interest is frequently ignored. For example, learning and memory performance are typically examined during the middle of the day in nocturnal (night active) rodents, a time when they are normally asleep. As a result, many reported deficits in performance can be attributed to time-of-day effects rather than (or in addition to) the intended manipulation. One goal of this chapter is to underscore the impact of biological rhythms on behavior and physiology to encourage a full appreciation of the significance and magnitude of such timed changes for investigators in the behavioral sciences.

FUNCTIONAL SIGNIFICANCE OF BIOLOGICAL RHYTHMS One of the most predictable features of life on earth is the regular pattern of environmental changes associated with the movement of the planet. Life evolved in a cyclic environment. Except for organisms living at the bottom of the oceans or deep within caves, day follows night, and the seasons change. These orderly and predictable changes in the environment have existed since life first began to evolve, although the timing of these events may have changed slightly over time. The rotation of the earth results in periodic exposure to the radiation of the sun, which causes predictable changes in light and ambient temperatures, as well as associated changes in the relative humidity of the air and in the oxygen levels of aqueous habitats. The biological clocks of animals and plants permit them to start or stop locomotor activities or activate photosynthetic machinery, respectively, in preparation for light. The action of the master biological clock in synchronizing individual bodily functions has been compared to the role of the conductor of an orchestra. As a result, internal processes serve to prepare the body for certain activities to occur later; for example, the elevated adrenal secretions coinciding with the morning onset of activity prepare an individual for increased activity levels and for breaking the nightly fast. All eukaryotic and most prokaryotic organisms tested to date, from unicellular organisms to humans, display circadian rhythms. Predictably, circadian rhythms have not evolved in organisms that live for less than 24 hours. Similarly, circannual rhythms have evolved only in animals that live for a year or more. Physiological systems reveal a wide range of rhythmic changes. For instance, neurotransmitter turnover, body temperature, and blood plasma levels of adrenalin, potassium, sodium, cortisol, androgens, and growth

We thank Zachary Weil for helpful comments on an earlier version of this chapter. The authors were supported during the preparation of this chapter by NIH grants HD050470 (LJK), MH57353, and NSF grant OIS 04-16897 (RJN). 56

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hormone all show pronounced circadian rhythms. Some of these changes may be on the order of 100% to 200% from baseline values. Peak daily cortisol concentrations, for example, which usually occur just prior to or immediately after awakening, coincide with the onset of activity in the morning. This programmed elevation of cortisol concentrations increases blood pressure and cardiac output prior to the active phase of the day. Increased cortisol concentrations are not driven by increased activity levels because the same circadian rhythm is observed in bedridden patients under constant conditions (Aschoff, 1965). In some instances, neurochemical receptors are produced prior to a circadian-programmed increase in ligand production, with both processes being coordinated by the circadian system. Body temperature peaks in mid-afternoon, when people are most active, but again, muscular activity is not solely responsible for this “heating” (Refinetti & Menaker, 1992). It may seem that the concept of biological clocks and programmed temporal changes in behavioral function is at odds with the concept of homeostasis in biological sciences and medicine. Homeostatic processes work to maintain physiological parameters within specific and often narrow ranges. Biologists and physicians frequently considered large fluctuations in physiology and behavior to be pathological; until recently, many resisted the idea of the programmed changes in physiology and behavior that we now understand to underlie homeostatic processes. As a result, we should not consider the appropriate parameters for a specific system to be fixed but, instead, variable relative to time of day or year. What orchestrates physiology and behavior so that organisms engage in appropriate behaviors at suitable times of day or during optimal seasons of the year? Some rhythms are the result of environmental factors that impose temporal control over behavior and physiology—these rhythms are termed driven rather than endogenous (from within). In contrast, most temporal changes in brain and behavior are controlled by internal biological clocks. This chapter focuses on these latter clocks in the control of behavior.

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Chronobiology has borrowed terms and concepts extensively from the field of engineering. For example, a rhythm is defined as a recurrent event that is characterized by its period, frequency, amplitude, and phase (Vitaterna, Takahashi, & Turek, 2001). Period is the length of time required to complete one cycle of the rhythm in question (e.g., the amount of time required to go from peak to peak or trough to trough). Frequency is computed as the number of completed cycles per unit of time (e.g., 6 cycles/day). Amplitude is the amount of change above and below the average value; that is, the distance of the peak from the average. The phase represents a point on the rhythm relative to some objective time point during the cycle. For example, under standard conditions, the phase of onset of the activity portion of a mouse’s activity-rest cycle corresponds closely with the onset of dark (Figure 4.1). Phase relations among various biological rhythms can also be described (e.g., the onset of the active phase of the daily sleep-wake cycle tightly corresponds with the peak of stress hormones secretion in both mice and humans). Many biological rhythms have been recognized for thousands of years. In the past, however, rhythms in migration, daily activity patterns, or hibernation were attributed to exogenous factors. Although exogenous factors may serve a permissive or synchronizing role for biological rhythms, endogenous timing mechanisms mediate many of the observed rhythms in brain and behavior. The best evidence to document whether a rhythm is driven by exogenous or endogenous signals is obtained via isolation studies (Aschoff, 1981; DeMairan, 1729; Thrun, Moenter, O’Callaghan, Woodfill, & Karsch, 1995; Vitaterna et al., 2001; Zucker & Boshes, 1982). Persistence of a biological rhythm in the absence of environmental cues provides compelling evidence that the rhythm under study is generated from within the individual and not driven by the environment. If a biological rhythm disappears under constant conditions, then it is reasonable to suggest that some cyclic cue in the environment drives the biological rhythm. This logic

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Figure 4.1 Components of biological rhythms. Rhythms can be analyzed in terms of amplitude, frequency, and period length. Note. The relationship from one rhythm to another is expressed in terms of phase relationships. Both cycles have a period of 24 hours (frequency  1 day).

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has now been applied in hundreds of studies that have firmly established the existence of biological rhythms driven by endogenous biological clocks. Incredibly, the period of the daily activity rhythm of one individual can be transferred to another individual by means of brain tissue transplants that contain the master biological clock (Lehman et al., 1987; Ralph, Foster, Davis, & Menaker, 1990). All animals (and all eukaryotic and some prokaryotic plants) studied to date have endogenous clocks that mediate biological rhythms (Roenneberg & Merrow, 2002). The periods of biological rhythms range from the 1-ms cycle of firing among some neurons to longer cycles such as the 90-minute cycle of REM sleep, the 4- or 5-day estrous cycles of rats, the annual cycle of hibernation, the 17-year cycle of cicada emergence, or the 100-year cycle of century plant flowering. How are these biological rhythms generated? Are they all mediated by biological clocks? Certainly, the periods of some biological rhythms, including most central nervous system and cardiovascular rhythms vary widely within the same individual depending on activity. The periods of other endogenous cycles, such as the wake-sleep cycle, are largely constant for the same individual, but there may be great inter-individual or species variation. Four types of biological rhythms can be identified, however, that are typically coupled with environmental factors, and the periods of these rhythms vary little under natural conditions. These relatively constant biological rhythms mimic the periods of the geophysical cycles of night and day (circadian), the tides (circatidal), the phases of the moon (circalunar), and the seasons of the year (circannual; Dunlap, Loros, & DeCoursey, 2004). These rhythms persist when animals are isolated from the respective environmental cues, but when isolated, these rhythms only approximate the periods of the environmental cues to which they are normally synchronized. Thus, the terms for many biological rhythms use the prefix circa (Halberg, 1959). Although isolation experiments are necessary to determine the extent of endogenous generation of any biological rhythm, it should be emphasized that the environment often exerts permissive effects on endogenously driven biological rhythms. The evidence for circadian or circannual rhythms is much stronger than for circatidal and circalunar rhythms, especially among vertebrates. Thus, this chapter focuses on circadian and circannual rhythms in behavioral neuroscience. Light serves as an important environmental time cue, or zeitgeber (from the German, meaning “time giver”), for most species (Morin & Allen, 2006; Roenneberg, Daan, & Merrow, 2003). Temperature is an important zeitgeber for some poikilothermic animals, and possibly a secondary zeitgeber for birds and mammals, but light is the primary zeitgeber among homeothermic animals (Foster,

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Hankins, & Peirson, 2007). As mentioned previously, the internal clock runs with a period that is approximately 24 hours in constant conditions, yet the functions controlled by this clock recur precisely every 24 hours when entrained (synchronized) to a 24-hour light-dark cycle. Because day length varies across the year in relatively small increments, internal clocks must exhibit plasticity. If a mouse is placed in constant dim light so that there is no daily zeitgeber, then the onset time of its locomotor activity begins to drift out of synchrony with local time; similarly, if a person is put into a windowless room for weeks, his wake-sleep cycle will drift out of phase with local conditions. Without the daily light-dark cycle providing a daily reset, the endogenous circadian clock of a hamster or human only approximates 24-hour cycles. Biological rhythms that are not synchronized with environmental cues are called free-running. Each individual displays its own free-running period (abbreviated as ), which remains relatively constant. The free-running periods of biological clocks are precise, but are not exactly 24 hours. The observation that different hamsters display an array of different free-running periods when housed in the same room suggests that they are not synchronized by each other ’s behavior, and that subtle geophysical cues are not providing temporal information. However, social factors can provide a zeitgeber for humans living in constant conditions (Mistlberger & Skene, 2004, 2005; Turek & Zee, 1999). Appropriately timed exercise or exogenous melatonin also influences human circadian timing (Mistlberger & Skene, 2005). As noted, individual animals display species-specific times of locomotor activity onset that are often linked to the timing of food intake, water consumption, and social activities (Mistlberger & Skene, 2004, 2005). If the zeitgeber is phase-shifted, then animals adapt their activity to the new regimen in a few days (length of time is dependent on the extent of change). The circadian rhythms of other physiological processes, such as adrenocortical hormone release, body temperature, and blood plasma volume, may require more or less time to synchronize to the new lighting regime (Davidson, Yamazaki, Arble, Menaker, & Block, 2006; Yamazaki et al., 2000). This process of adaptation to rapid phase shifts in environmental lighting results in what is commonly called “jet lag” when people travel through different time zones. Ultradian (shorter than circadian) and infradian (longer than circadian) rhythms are biological rhythms that do not correspond to any known geophysical cycles. Ultradian cycles are commonly observed; for example, the 90-minute cycle characteristic of REM sleep is a welldocumented ultradian rhythm (Schulz & Lavie, 1985). The pulsatile secretion of several hormones, including gonadotropin-releasing hormone (GnRH), luteinizing hormone

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Circadian Control of Brain and Behavior 59

(LH), testosterone, growth hormone, and corticosterone, represent ultradian rhythms (Schulz & Lavie, 1985). Ultradian rhythm variation in free estradiol and cortisol concentrations during menstrual cycles correlates with depressive symptoms (Bao et al., 2004) as well as sleep disturbances (Voss, 2004). Ultradian rhythms in locomotor activity, feeding, and metabolism have been reported in a number of high-metabolic mammalian species such as shrews and voles (reviewed in Liu, Li, & Wang, 2007), and may be related to ancient metabolic cell cycles (Lloyd, Lemar, Salgado, Gould, & Murray, 2003). Infradian rhythms are less frequently observed than ultradian rhythms; such biological rhythms are longer than a day, but shorter than a lunar month. Generally, infradian rhythms in testicular function are rare among vertebrates. Dormice (Glis glis) have been reported to display infradian rhythms of about 60 days in body mass and body fat content (Grimes, Melnyk, Martin, & Mrosovsky, 1981; Melnyk, Mrosovsky, & Martin, 1983a, 1983b). The most common types of infradian rhythms are those associated with ovarian cycles; that is, estrous or menstrual cycles. Hamsters and mice display 4-day estrous cycles; rats have estrous cycles of either 4 or 5 days. Estrous cycles are 16 days in guinea pigs and sheep. Estrous cycles persist in constant conditions (i.e., self-sustaining), but are endogenously generated rhythms that do not correspond to any known geophysical cue. Although human menstrual cycles approximate a lunar month, the length of the menstrual cycle seems to be only coincidentally similar to the length of the lunar month, rather than reflecting any adaptive link to the lunar cycle (Knobil & Hotchkiss, 1988). CIRCADIAN CONTROL OF BRAIN AND BEHAVIOR Evolution of the Circadian System From prokaryotic to eukaryotic organisms, the 24-hour solar cycle has had a pervasive impact on the evolution of life. As suggested previously, a variety of conditions require that organisms internally track daily time. For instance, all energetic requirements for normal health and functioning cannot be simultaneously fulfilled, and peaks in energetic processes must be partitioned throughout the day. Likewise, all behavioral requirements (e.g., foraging, mating, nest building) cannot be simultaneously performed. To allow organisms to anticipate daily environmental change and synchronize their behavior and physiology accordingly, individuals have evolved an endogenous circadian timekeeping mechanism. Not only does this system allow for the coordination of internal physiology, but it also allows animals to predict recurring 24-hour events, such

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as food availability and predator activity, and adjust their behavior appropriately. Perhaps the most direct evidence for the adaptive significance of circadian rhythms comes from studies of cyanobacteria—a group of prokaryotes. Analysis of relative fitness of different strains of cyanobacteria shows that strains with a circadian period similar to the light/dark cycle of the environment have greater reproductive success (Ouyang, Andersson, Kondo, Golden, & Johnson, 1998; Woelfle, Ouyang, Phanvijhitsiri, & Johnson, 2004). Strains whose clocks were disrupted were defeated by strains with a functional clock, but only when held in a light/dark cycle; any competitive advantage was lost in constant conditions. In multicellular organisms, this competitive advantage is also observed; Arabidopsis thaliana, as in cyanobacteria, grow faster and survive longer when housed in light cycles closest to their endogenous circadian period (Dodd et al., 2005). Circadian rhythms are strikingly ubiquitous, with most behavioral, biochemical, and physiological responses of organisms showing daily variation that persists in constant environmental conditions. As described next, some genes are part of the cellular clockwork mechanism, whereas others are controlled directly or indirectly by these core clock genes (i.e., clock-controlled genes). To further understand the evolution of the circadian system, one logical step is to determine the percent of the genome under circadian control across taxa. In Arabidopsis, about 6% of the estimated 8,000 genes studied are rhythmic (Harmer et al., 2000). In contrast, in the retina of Xenopus perhaps only a few critical proteins (0.2%) are directly under the control of the circadian clock (C. B. Green & Besharse, 1996). In Drosophila, an oligonucleotide-based high-density array to measure gene expression changes on a whole genome level revealed that approximately 7% to 9% of genes express a circadian pattern (Ceriani et al., 2002; Claridge-Chang et al., 2001; C. B. Green & Besharse, 1996; McDonald & Rosbash, 2001). Investigations of head versus body rhythms showed that the genes exhibiting circadian patterns have little overlap, suggesting specificity of function for core clock genes (CCGs) in distinct systems (Ceriani et al., 2002). In mice the same general conclusion has been reached by comparing gene expression patterns between liver and heart (Storch et al., 2002). While 8% to 10% of genes are rhythmically expressed in one of the tissues, there are few genes that show circadian regulation in both tissues. Even the genes that have a rhythm in both tissues are frequently out of phase, suggesting independent local function. Determination of similarities and differences across species in clock gene and CCG homologues, and the signals that set their phase, will be necessary to fully understand the evolution of the circadian system.

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Biological Rhythms

Adaptive Significance of the Circadian System Animals have evolved to synchronize their endogenous circadian rhythms with the environment in order to promote survival (e.g., DeCoursey & Krulas, 1998). Numerous behaviors are restricted to specific times of day in response to a variety of selection pressures. For example, diurnal species (i.e., species active during the day) confine behaviors such as feeding, locomotion, foraging, and reproduction to the light hours in order to avoid predation. Likewise, nocturnal (i.e., active at night) species such as owls are active at night to maximize the availability of nocturnal prey (e.g., small rodents). As described later, the circadian “clock” that synchronizes rhythms in behavior and physiology is localized to a discrete bilateral nucleus in the anterior hypothalamus. If this master brain clock is destroyed, animals lose their ability to restrict behaviors to a particular time of day, and this arrhythmicity may compromise survival (DeCoursey & Krulas, 1998; DeCoursey, Krulas, Mele, & Holley, 1997). Humans have evolved to maintain maximum performance during daytime and circadian variation in learning and memory, attention, reaction time, and perception are prominent (Babkoff, Caspy, Mikulincer, & Sing, 1991; Colquhoun, 1981). This is especially important for individuals working night shifts that require critical cognitive performance. Generally, human performance peaks with the daily afternoon peak in body temperature, although verbal reasoning appears to peak earlier in the circadian cycle (Colquhoun, 1981). When people were maintained in the laboratory for a month in either 24- or 24.6-hour days, about half of the individuals adapted with peak melatonin occurring during the normally scheduled sleep period (synchronized), whereas half displayed peak melatonin while awake (nonsynchronized). Nonsynchronized individuals reduced total sleep time, as well as sleep latency and rapid eye movement (REM) latency. Cognitive performance was impaired among the nonsynchronized individuals and enhanced among the synchronized subjects (Wright, Hull, Hughes, Ronda, & Czeisler, 2006). People are likely to be nonsynchronized when working during the night shifts, but resorting to a normal day-night schedule during the weekend, or when undergoing repeated jet travel across several time zones. The ability of people to estimate time intervals also varies on a circadian basis. For example, time perception evaluations at 0900, 1300, 1700, and 2100 hours revealed that short-term time perception was more influenced by circadian phase than by memory load or various psychophysiological factors (Kuriyama et al., 2003). Such human studies should be interpreted cautiously because either approximately onethird of the day is missing or circadian influences are masked by sleep deprivation. These latter difficulties can be overcome by distributing small meals and naps throughout the day and having subjects maintain a constant routine designated by

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the researchers (Brown & Czeisler, 1992; el-Hajj Fuleihan et al., 1997; Khalsa, Jewett, Duffy, & Czeisler, 2000). One study of visual selective attention used a different technique to dissociate the effects of circadian phase and time awake (Horowitz, Cade, Wolfe, & Czeisler, 2003). After 38 hours of no sleep, observers increased reaction times for spatial configuration and conjunction tasks. Observers traded accuracy for speed when sleepy, which could lead to decision errors. Indeed, extended shift durations lead to increased impairments in attentional errors, significant medical errors, and so-called adverse events in critical care units (Barger et al., 2006). In a web-based analysis of medical errors, interns working more than 5 extended-duration shifts per month reported more attentional failures during lectures, rounds, and clinical activities, including surgery; fatiguerelated preventable adverse events (including fatality) increased by 300% in these interns. Temporal variation in behavior is associated with temporal variation in underlying physiology and biochemistry. These underlying processes are modulated by endogenously driven circadian rhythms that are synchronized by environmental time cues. Clearly, a body clock that is not synchronized with the environment is not adaptive. Some rhythms, however, exhibit a peak in activity during one time of day while other rhythms are at a nadir at this time. For example, in humans, cortisol concentrations peak at dawn while melatonin and prolactin concentrations peak in the middle of the night. The relationship between daily fluctuations in physiology and the environmental light/dark cycle is different for nocturnal and diurnal species. Thus, although a myriad of rhythms have a period of about 24 hours, each of these rhythms has a unique phase with respect to one another and to the light/dark cycle (Figure 4.2). Stated differently, although bodily rhythms exhibit different phases

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Figure 4.2 Examples of circadian rhythms in humans. Note. Shaded areas represent times of sleep whereas the unshaded areas represent times of activity. All parameters exhibit unique peaks and troughs relative to each other. This phase relationship among individual rhythms is critical for optimal functioning and the maintenance of homeostasis.

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Circadian Control of Brain and Behavior 61

relative to some objective point in time, the phase relationship among rhythms remains stable, unless a perturbation occurs (e.g., jet lag). Consequently, circadian rhythms help to maintain homeostasis within the body. In addition, the phase of specific rhythms helps to prepare the body for necessary daily activities in advance of their actual occurrence. For example, cortisol rhythms rise in humans prior to waking in order to facilitate the onset of morning activity (e.g., Van Cauter & Refetoff, 1985; Weitzman et al., 1971). It is not difficult to imagine how physiological functioning would suffer without an internal circadian clock synchronized to the environment. Most of us have experienced general feelings of malaise and other maladies following a long flight across time zones. While we can recover within a few days from acute jet lag, millions of frequent flyers, shift workers, individuals with sleep disorders, and other individuals whose work day is not fixed are exposed chronically to such temporal disruptions. These individuals provide the opportunity to examine the effects of more chronic circadian disruptions. In fact, this loss of synchrony between the circadian clock in the brain and the environment leads to pronounced clinical pathologies. One recent study found that elderly mice subjected to temporal disruptions equivalent to a flight from Washington to Paris, once a week for 8 weeks, die sooner a result of their bodies being out of sync with local time (Davidson, Sellix, et al., 2006). Flight attendants frequently traveling across time zones exhibit cognitive deficits associated with reductions in temporal lobe structures (Cho, 2001; Cho, Ennaceur, Cole, & Suh, 2000). Numerous studies show that shift workers have a higher incidence of cancer (Conlon, Lightfoot, & Kreiger, 2007; Hansen, 2006; Kubo et al., 2006; O’Leary et al., 2006; Patel, 2006), diabetes (Karlsson, Alfredsson, Knutsson, Andersson, & Toren, 2005; Morikawa et al., 2005; Poole, Wright, & Nattrass, 1992; Robinson, Yateman, Protopapa, & Bush, 1990; Sanborn, Currie, & Bailey, 1982), ulcers (Costa, 1996; Koda et al., 2000; Kolmodin-Hedman & Swensson, 1975; Segawa et al., 1987), hypertension and cardiovascular disease (Alstadhaug, Salvesen, & Bekkelund, 2005; Costa, 1996; Hwang & Lee, 2005; Kivimaki et al., 2006; Wolk, Gami, Garcia-Touchard, & Somers, 2005), psychological disorders (Bildt & Michelsen, 2002; De Koninck, 1991; Leonard, Fanning, Attwood, & Buckley, 1998; Munakata et al., 2001; Skipper, Jung, & Coffey, 1990; Venuta, Barzaghi, Cavalieri, Gamberoni, & Guaraldi, 1999), and a host of other clinical issues. In fact, just changing the time of day during which cancer chemotherapy is administered can nearly double the chances of survival in patients suffering from cancers with an estimated 30% to 40% 5-year survival rate, including childhood leukemia and colorectal carcinomas (Hrushesky, 1990, 1993, 1995; Kanabrocki et al., 2006; Lee & Balick, 2006; Levi, 1987, 1994, 2001, 2002; Takimoto, 2006). These findings, although largely correlated, point to a critical role

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for internal circadian timing in maintaining normal brain functioning and peripheral physiology. The two fundamental functions of the circadian system, internal organization and entrainment to the environment, are fundamental for optimal regulation of physiology and behavior. For each aspect of physiology and behavior, the circadian system sits upstream of a regulatory system, modulating the timing and synchronization of events. The mechanisms controlling circadian function—coordination of bodily systems and synchronization with the environment— represents the focus of the following sections. The Brain Clock Whereas a “master” circadian clock has been localized to the suprachiasmatic nucleus (SCN) located in the anterior hypothalamus in mammals (Figure 4.3; Moore & Eichler, 1972; Stephan & Zucker, 1972), it is now more appropriate to conceptualize the circadian system as an assembly comprised not only of a master clock, but also a series of subordinate clocks whose phase and coordinated activity is set by the SCN. As described in the following sections, the SCN has direct access to environmental time via retinal projections to the clock. Because subordinate central and peripheral clocks do not have access to such time cues, it is necessary for the SCN to communicate such information throughout the CNS and periphery. In addition to the core clock genes (CCGs) responsible for SCN and subordinate clock function, CCGs represent important output and local coordination systems. The stability of this hierarchical arrangement is necessary for normal body functioning and disease prevention. Numerous lines of evidence indicate that the master mammalian circadian clock is located in the SCN. The first

Figure 4.3 The mammalian circadian clock is located in the suprachiasmatic nucleus (SCN) of the anterior hypothalamus. Note. The SCN is pictured in this schematic of a coronal section through a rodent brain. The SCN is situated at the base of the brain directly above the optic chiasm (OC) and directly surrounding the third ventricle (V3). The sagittal schematic in the upper right corner depicts the approximate rostral-caudal location depicted in the coronal section.

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Biological Rhythms

indication that the SCN is the master clock comes from studies in which lesions ablating the SCN abolish circadian rhythmicity in adrenal corticoid secretion and locomotor behavior (Moore & Eichler, 1972; Stephan & Zucker, 1972). SCN-lesioned animals continue to show the full range of normal behaviors, but their temporal organization is lost and never recovers, irrespective of how early in development the lesions are performed (Mosko & Moore, 1979). The initial conclusion that the SCN serves as the master clock in the brain has been confirmed in the subsequent 30 years by converging lines of research involving in vivo, ex vivo, and in vitro studies carried out in many different laboratories. For example, transplants of donor SCN tissue into the brains of arrhythmic, SCN-lesioned hosts restore circadian rhythmicity in behavior (Lehman et al., 1987; Ralph et al., 1990). Importantly, rhythms are restored with the period of the donor SCN, indicating that the transplanted tissue does not act by restoring host-brain function but that the “clock” is contained in the transplanted tissue. Further evidence that clock function is contained within the SCN comes from studies demonstrating that circadian rhythms in neural firing rate persist in isolated SCN tissue maintained in culture (D. J. Green & Gillette, 1982; Groos & Hendriks, 1982; Shibata, Oomura, Kita, & Hattori, 1982). These studies confirmed that input from extra-SCN brain sites is not necessary for circadian rhythms in this nucleus (D. J. Green & Gillette, 1982; Groos & Hendriks, 1982; Shibata et al., 1982). In hypothalamic slice preparations, the SCN is intrinsically capable of sustaining not only circadian rhythms in neuronal firing rate, but also rhythms in glucose utilization and vasopressin secretion (Gillette & Reppert, 1987; D. J. Green & Gillette, 1982; Newman & Hospod, 1986). Primary cultures and organotypic explants of the rat SCN are similarly characterized by the distinctive capacity to generate circadian rhythms in vasopressin and vasoactive intestinal polypeptide (VIP) release for multiple cycles (Earnest & Sladek, 1987; Shinohara, Honma, Katsuno, Abe, & Honma, 1994; Watanabe, Koibuchi, Ohtake, & Yamaoka, 1993). Vasopressin and VIP rhythms produced by the same SCN explant are independently phased suggesting that these circadian rhythms may be generated by neurons that comprise two separable populations of oscillators within the SCN (Shinohara et al., 1994). An excellent overview of these studies in historical perspective is available (Weaver, 1998). The Molecular Clock Within a cell, circadian rhythms are produced by an autoregulatory transcriptional/translational negative feedback loop that takes approximately 24 hours (Box 4.1). While the general mechanism for circadian oscillations at the

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cellular level is common among organisms, the components comprising the feedback loop differ. For the purpose of clarity, only the core mammalian feedback loop is described. Earlier work proposed a core feedback loop that begins when two proteins, CLOCK and BMAL1, bind to one another and drive the transcription of messenger RNA (mRNA) of the Period (Per) and Cryptochrome (Cry) genes by binding to the E-box (CACGTG) domain on these gene promoters. Three Period (Per1, Per2, and Per3) and two cryptochrome genes (Cry1 and Cry2) have been identified. The mRNA for these genes is translated into PER and CRY proteins in the cytoplasm of the cell over the course of the day. Throughout the day, these proteins build up within the cytoplasm, and when they reach high enough levels, they form hetero- and homo-dimers. These newly formed dimers then feed back to the nucleus where they bind to the CLOCK:BMAL1 protein complex to turn off their own transcription (Figure 4.4).

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Figure 4.4 A simplified model of the intracellular mechanisms responsible for mammalian circadian rhythm generation. Note. The process begins in the cell nucleus when CLOCK and BMAL1 proteins dimerize to drive the transcription of the Per (Per1, Per2, and Per3) and Cry (Cry1 and Cry2) genes. In turn, Per and Cry are translocated to the cytoplasm and translated into their respective proteins. Throughout the day, PER and CRY proteins rise within the cell cytoplasm. When levels of PER and CRY reach a threshold, they form heterodimers, feed back to the cell nucleus, and negatively regulate CLOCK:BMAL1 mediated transcription of their own genes. This feedback loop takes approximately 24 hours, thereby leading to an intracellular circadian rhythm. From Kriegsfeld and Silver, 2006. Reprinted with permission.

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BOX 4.1 TRANSCRIPTION, TRANSLATION, AND POSTTRANSLATIONAL EVENTS Gene transcription is the process by which the sequence of nucleotides in a single strand of DNA is transcribed into a single strand of complementary RNA. Transcription factors bind to the beginning of the DNA sequence where the gene to be transcribed is located. In order to synthesize RNA, the two tightly twisted strands of DNA must be unraveled by enzymes called helicases. A gene consists of a unique linear sequence of DNA. Among eukaryotic organisms, some of the nucleotide sequences within the gene are noncoding sequences, called introns, which alternate with coding sequences, called exons. There are special markers denoting the start and end points of each gene. A distinct sequence of nucleotides, called a promoter or facilitory region, marks the start of the gene. The binding of a transcription factor to the promoter allows special enzymes, called RNA polymerases, to attach to the promoter and begin the process of RNA synthesis. The sequence of RNA nucleotides, determined by the sequence of nucleotides along the DNA, eventually determines the sequences of amino acids in the protein gene product of the specific gene in question. After transcription, enzymes clip out the intron sequences; then other enzymes splice together the remaining segments (exons) to form messenger RNA (mRNA). The mRNA leaves the cell nucleus, travels to the rough endoplasmic reticulum (RER), and serves as the template for translation into a linear sequence of amino acids, which occurs on ribosomes. After translation is completed, further processing occurs. The penultimate product is typically packaged into vesicles before being transported to the Golgi apparatus. Additional posttranslational processing usually occurs within the rough endoplasmic reticulum, the Golgi apparatus, and in the vesicles to yield the final version of the peptide/protein. More recently, it has become clear that the cellular clockwork is more complex, with a number of integrated feedback loops whose regulators are often, themselves, controlled by elements of the core clock mechanism. Two other promoter elements have emerged as important for circadian rhythm generation, DBP/E4BP4 binding elements (D boxes) and REV-ERB/ROR binding elements (RREs; Ueda et al., 2005). REV-ERB, an orphan nuclear receptor, negatively regulates the activity of the CLOCKBMAL1 complex and is also acted on by PER and CRY. Transcription of REV-ERB is controlled by the same mechanism controlling Per and Cry transcription. Similarly, the transcription factor DPB is positively regulated by the

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CLOCK:BMAL1 complex (Ripperger & Schibler, 2006) and acts as an important output mechanism by driving rhythmic transcription of other output genes via a PAR basic leucine zipper (PAR bZIP; Lavery et al., 1999). Whereas most work to date has focused on transcriptional regulation as the key mechanism driving cellular rhythms, posttranscriptional and posttranslational events are also critical for circadian coordination (Baggs & Green, 2003; C. Kramer, Loros, Dunlap, & Crosthwaite, 2003; Reddy et al., 2006). In addition to transcriptional/translational control of cellular clock function, regulatory kinases also play a pronounced role in regulation of circadian period. Over a decade ago, a circadian mutation called tau was identified that resulted in a shortened circadian period in Syrian hamsters (Ralph & Menaker, 1988). It is now known that the tau locus is encoded by casein kinase I epsilon (CKI; Lowrey et al., 2000; H. Wang, Ko, Koletar, Ralph, & Yeomans, 2007). In normal rodents, CKI phosphorylates PER and “tags” it for degradation throughout the day. Eventually, PER acts to overwhelm CKI, and dimerizes with CRY to feed back to the cell nucleus. The mutant form of CKI is unable to phosphorylate PER, leading to a short circadian period in tau mutant mice due to premature nuclear entry of PER:CRY dimers (Lowrey et al., 2000; Vielhaber, Eide, Rivers, Gao, & Virshup, 2000). The pronounced effects of single circadian gene mutations have been dramatized in a study identifying the genetic basis for a sleep abnormality in humans known as familial advanced sleep-phase syndrome (FASPS). In affected individuals, sleep onset occurs very early, around 19:30 hr, sleep duration is normal, and wake-up time is advanced to about 4:30 hr. This sleep disorder was found to be the result of a single point mutation in the CKI binding region of the PER2 gene, causing hypophosphorylation by CKI in vitro (Toh et al., 2001). Thus, as in the tau mutant, abnormal phosphorylation of PER protein by CKI likely leads to premature negative feedback of PER:CRY heterodimers, thereby speeding the “gears” of the circadian clock. A role for Per3 in human delayed sleep phase syndrome has also been reported (Archer et al., 2003). Whereas extraordinary progress has been made in uncovering the mechanisms responsible for clock function at the molecular level, the complexity of this mechanism is still not fully understood. Determining the specific interactions among complementary feedback loops will allow a further understanding of cellular clock function. Because downstream effects of CCGs are crucial in translating cellular clock function to physiological outcomes, it will be necessary to broaden our understanding of the means by which core clock mechanisms convey relevant timing information at a systems level.

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Ubiquity of Circadian Clocks The genes regulating circadian rhythmicity and their protein products have been found in numerous sites, including extra-SCN brain loci and in the periphery (Abe et al., 2002; Balsalobre, Damiola, & Schibler, 1998; Kriegsfeld, Korets, & Silver, 2003; Yamazaki et al., 2000). These findings led to questions regarding the unique nature of the master oscillator in the SCN, the functional significance of extra-SCN oscillators, and mechanisms of coordination of these widely dispersed clocks. As described in the following sections, the SCN serves to coordinate cellular oscillators throughout the CNS and periphery. Without a master clock, all other timing systems cease to function as independent cellular oscillators in target systems lose their combined coordination. The loss of coherent rhythmicity in the SCN or peripheral tissue can be due to dampening of rhythms in individual cells or to loss of synchrony among a population of cells in the tissue. Use of Per1-luciferase reporter animals indicates that rhythms in peripheral tissues damp then disappear over time due to uncoupling (desynchronization) among oscillators that retain their individual rhythms (Nagoshi et al., 2004; D. Welsh, 2004). Presumably, peripheral clock cells normally get phase information from the SCN to synchronize individual oscillators to each other. In this view, the SCN sets the phase of peripheral circadian clocks daily, coordinating the activity of tissues and organs of the body relative to one another, thereby maintaining homeostasis. However, the phase of peripheral clocks is also significantly influenced by the daily food intake schedule (e.g., Stokkan, Yamazaki, Tei, Sakaki, & Menaker, 2001). In contrast to hamsters, which show strong circadian organization of nocturnal feeding, common voles (Microtus arvalis) feed throughout the day with an ultradian rhythm of 2 to 3 hours. In voles, the clock-gene mRNAs display high circadian amplitudes in the SCN, but display virtually no cyclicity in the liver (van der Veen et al., 2006). Entrainment of the Circadian System As suggested previously, in order to be adaptive for an organism, circadian rhythms must be synchronized to local environmental time. In addition to a direct visual pathway from retinal ganglion cells to the visual cortex, there is also a direct retinohypothalamic tract (RHT) projecting from the optic nerve to the SCN (Klein & Moore, 1979; Moore & Klein, 1974; Figure 4.5). This second visual pathway is necessary and sufficient to entrain (synchronize) the SCN to the environmental light/dark cycle. If the primary visual pathway is transected at the level of the optic tract beyond the optic chiasm (i.e., caudal to the SCN), then the animal

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Figure 4.5 The visual pathway mediating entrainment in mammals. Note. Light stimulates intrinsically photosensitive ganglion cells within the eyes that convey illumination information directly to the SCN of the hypothalamus. This pathway is separate from the classical visual system and uses melanopsin as the light-sensitive photopigment.

is visually blind, but the circadian system continues to respond to photic cues by synchronizing the animal to the light/dark cycle (Klein & Moore, 1979). These studies demonstrate the route whereby environmental photic information can reach the SCN. However, the specific retinal photoreceptor responsible for transmitting this signal was initially enigmatic, as entrainment was independent of traditional, image-forming photoreceptors; mice lacking both rod and cone photoreceptors (rd/rd) exhibit grossly normal entrainment even though they are visually blind (Foster et al., 1993; Freedman et al., 1999; Lucas, Freedman, Munoz, Garcia-Fernandez, & Foster, 1999; Van Gelder, 2001). These findings led to a search for a novel nonrod/noncone photoreceptor. Several years of rapid discovery beginning in this millennium identified a subset of light-responsive ganglion cells containing the photopigment melanopsin (Berson, Dunn, & Takao, 2002; Hannibal & Fahrenkrug, 2002). These ganglion cells project directly to the SCN and were initially thought to be the sole photoreceptors necessary for entrainment. However, melanopsin deficient mice exhibit only minor impairments in entrainment (Lucas et al., 2003; Panda et al., 2002; Ruby et al., 2002). This discrepancy was resolved by showing that entrainment is abolished in mice doubly mutant for both melanopsin and traditional rod/cone photoreceptors (Hattar et al., 2003; Panda et al., 2003). These findings suggest that traditional rod/cone photoreceptors project to

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specialized light-responsive ganglion cells that then transmit this integrated photic information directly to the SCN. Thus, these two types of receptors likely work together to entrain the circadian clock, and either receptor type can support entrainment in the absence of the other.

Circadian Output and the Control of Behavior Diffusible SCN Output Early work in which SCN grafts were shown to restore circadian patterns in activity-related behaviors suggested that circadian rhythmicity could be supported by diffusible output from the clock (Lehman et al., 1987; Ralph et al., 1990; Silver, Lehman, Gibson, Gladstone, & Bittman, 1990). This supposition was based on the fact that transplants restored circadian function independent of the establishment of neural SCN connections with the host brain. This possibility was demonstrated definitively by encapsulating donor SCN tissue in a membrane that prevented neural outgrowth while allowing the diffusion of signals between graft and host; behavioral rhythms were still restored under these conditions (Silver, LeSauter, Tresco, & Lehman, 1996). One candidate diffusible signal is prokineticin-2 (PK2; Cheng et al., 2002). This protein is expressed rhythmically in the SCN and its receptor is present in all major SCN targets (Cheng, Bittman, Hattar, & Zhou, 2005; Cheng et al., 2002). Likewise, PK2 administration during the night (when levels are low) inhibits wheel-running behavior. Whether this signal normally operates in a diffusible manner and/or is released synaptically requires further examination. A second candidate diffusible signal is transforming growth factor-alpha (TGF-alpha; A. Kramer et al., 2001). As with PK2, TGF-alpha is expressed rhythmically in the SCN and its administration inhibits wheel-running behavior. The receptor for TGF-alpha is also expressed in the subparventricular zone (SPVZ), the major target of the SCN. Again, the degree that TGF-alpha is released in a diffusible manner under normal conditions requires further study. Studies in which fiber output is eliminated from the SCN (allowing only for diffusible output), in conjunction with administration of PK2 and TGF-alpha antagonists, are necessary to begin to answer this question. Although it is intriguing to speculate on the role of these signals in communicating information from the SCN, the problem of unequivocally identifying an endogenous, physiologically relevant diffusible SCN signal is complex. The necessary and sufficient criteria to confirm the existences of a diffusible signal in a fluid volume have been summarized previously (Nicholson, 1999). First, evidence that the removal or replacement of the signaling substance results in a change in the response being controlled and an

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assay of the substance should indicate that it is present or increases, or both, in a well-defined temporal relationship to the response (and similarly declines when the response disappears). In addition, evidence must be obtained that a fluid compartment is the conduit for a diffusible or transported signal. The signal must have access to and enter the compartment where the fluid dynamics and turnover in the compartment should allow appropriate movement of the signal. Although PK2 and TGF-alpha meet some of these criteria, further research is necessary to clarify the role of these molecules in communicating circadian information. Neural Control of Neurosecretory Factors In contrast to behavioral rhythms (e.g., locomotion, drinking, gnawing), endocrine rhythms require neural projections from the SCN to endocrine targets; endocrine rhythms are abolished after knife cuts severing SCN fibers (Hakim, DeBernardo, & Silver, 1991; Nunez & Stephan, 1977) and are not restored in SCN-lesioned transplanted animals (Meyer-Bernstein et al., 1999; Nunez & Stephan, 1977; Silver et al., 1996), presumably due to inadequate neural innervation of the host brain by the graft. Further evidence for a neural SCN output signal regulating hormone secretion is seen in studies of female hamsters. When housed in constant light, the activity of a subset of hamsters “splits” into two separate activity bouts within a 24-hour interval. These split females display two daily preovulatory LH surges, each approximately half the concentration of a single surge in a nonsplit female (Swann & Turek, 1985). Under normal conditions, both halves of the bilaterally symmetrical SCN are active in synchrony. In ovariectomized, estrogen-implanted split hamsters examined during one of their activity bouts, however, activation of the SCN occurs on one side of the brain (monitored by expression of the early immediate gene FOS), but not on the other, suggesting that each half of the SCN can control an activity bout (de la Iglesia, Meyer, Carpino, & Schwartz, 2000). Remarkably, FOS activation in GnRH neurons was only seen on the side of the brain in which SCN FOS expression occurred (de la Iglesia et al., 2000; de la Iglesia, Meyer, & Schwartz, 2003). These findings suggest that the precise timing of the LH surge is derived from a neural signal originating in the SCN and communicated to ipsilateral GnRH neurons, as a diffusible output signal would reach both sides of the brain. Importantly, some hypothalamic sites are activated ipsilaterally, while others are activated either ipsilaterally or bilaterally in the split animal, again supporting the notion of multiple SCN output pathways (Yan, Foley, Bobula, Kriegsfeld, & Silver, 2005). Neural output from the SCN has been extensively investigated in rats and hamsters using tract-tracing techniques (Kalsbeek, Teclemariam-Mesbah, & Pevet, 1993;

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Kriegsfeld, Leak, Yackulic, LeSauter, & Silver, 2004; Leak & Moore, 2001; Morin, Goodless-Sanchez, Smale, & Moore, 1994; Stephan, Berkley, & Moss, 1981; Watts & Swanson, 1987). Importantly, many of these monosynaptic projections target brain regions containing neuroendocrine cells producing hypothalamic-releasing hormones. Direct projections have been traced from the SCN to the medial preoptic area (MPOA), supraoptic nucleus (SON), anteroventral periventricular nucleus (AVPV), the paraventricular nucleus (PVN), the dorsomedial nucleus of the hypothalamus (DMH), and the lateral septum and the arcuate (Arc). The SCN also projects to the pineal through a multisynaptic pathway (Klein, 1985; Klein et al., 1983). There is abundant evidence for direct neural SCN control of neuroendocrine cell populations (Buijs, Hermes, & Kalsbeek, 1998; Buijs, van Eden, Goncharuk, & Kalsbeek, 2003; Egli, Bertram, Sellix, & Freeman, 2004; Gerhold, Horvath, & Freeman, 2001; Horvath, Cela, & van der Beek, 1998; Kalsbeek & Buijs, 2002; Kalsbeek, Fliers, Franke, Wortel, & Buijs, 2000; Kalsbeek, van Heerikhuize, Wortel, & Buijs, 1996; Kriegsfeld, Silver, Gore, & Crews, 2002; Van der Beek, Horvath, Wiegant, Van den Hurk, & Buijs, 1997; Van der Beek, Wiegant, Van der Donk, Van den Hurk, & Buijs, 1993; Vrang, Larsen, & Mikkelsen, 1995). Because these cell populations can regulate neurochemicals that are secreted into the CSF (Reiter & Tan, 2002; Skinner & Caraty, 2002; Skinner & Malpaux, 1999; Tricoire, Moller, Chemineau, & Malpaux, 2003) or general circulation, SCN-derived signals can control widespread systems in the brain and body. Considered together, the findings summarized previously suggest several possibilities. For example, behavioral rhythms may be controlled by a diffusible signal(s), whereas endocrine rhythms may require neural output. Alternatively, behavioral and endocrine rhythms can both be supported by diffusible signals, but the threshold for supporting behavioral rhythms is lower. Finally, behavioral rhythms are controlled by both neural and diffusible signals, and either can maintain rhythmic function, while endocrine rhythms can only be supported via neural connections. Definitive identification of biologically significant endogenous diffusible signal(s) and the precise mode of SCN control is a current line of inquiry.

SEASONAL CHANGES IN BRAIN AND BEHAVIOR In common with daily fluctuations in energy availability, intake, and requirements, energy availability and requirements vary throughout the year, and individuals must parse their various activities across the year to maximize energy

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utilization and survival. Thus, all energetically expensive activities (e.g., mating, migrating, foraging, nest building, and thermoregulation) cannot be simultaneously performed. To allow anticipation of annual fluctuations in energy conditions and synchronization of their behavior and physiology accordingly, individuals have evolved mechanisms to determine time of year. In common with the circadian system, seasonal timekeeping mechanisms permit the coordination of internal physiology, and also allow animals to predict recurring seasonal events, such as food availability, general weather conditions, and predator activity, and adjust their behavior appropriately. Seasonal Changes in Reproductive Function Whereas many seasonal changes in behavior have been documented, breeding seasons represent the most salient seasonal change in behavior. In addition to mating behaviors, other associated behaviors such as food intake, aggression, and territorial defense show marked seasonal fluctuations. In general, small animals tend to breed during the long days of spring and summer, whereas large animals with relatively extended periods of gestation, breed during the short days of autumn. In both cases, offspring are produced in the spring when food availability is at a seasonal maximum. The specific timing of breeding represents a selective compromise among competing trade-offs such as the availability of food for gestation or lactation. Syrian hamsters (Mesocricetus auratus) have been studied as the exemplar of a long-day breeder. Individuals of this species reduce circulating concentrations of reproductive steroid hormones, LH, and follicle-stimulating hormone (FSH), after exposure to simulated winter day lengths or appropriate melatonin treatment (Bartness, Powers, Hastings, Bittman, & Goldman, 1993; Swann & Turek, 1988). In sheep, the most commonly studied short-day breeder, exposure to short days leads to increased LH secretion, manifested as an increase in the frequency of pulsatile LH secretion (Lehman et al., 1997). Such changes in pituitary gonadotropin secretion lead to changes in gonadal growth and gonadal steroid hormone secretion and the subsequent onset of mating behavior during the autumn. Circulating testosterone concentrations decrease rapidly in response to the short day via enhanced negative feedback effects on gonadotropin-releasing hormone (GnRH) secretion in male rodents (Turek, 1977). Short-day male rodents display lower circulating testosterone concentrations compared with long-day males. Because testosterone modulates many behaviors (in addition to reproduction), this hormone is another potential mediator of seasonal adjustments in the brain, which could act alone or in combination with melatonin or other hormones. Examples of testosterone affecting

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seasonal behaviors include the combined effect of testosterone and photoperiod on locomotor activity in male Syrian hamsters (Ellis & Turek, 1983) and the reinstatement by exogenous testosterone of female odor preference in castrated male meadow voles maintained in long or short days (Ferkin & Gorman, 1992). Testosterone replacement prevents elevated food intake in castrated male deer mice (Blank, Korytko, Freeman, & Ruf, 1994) and restores aggressive behaviors in Syrian hamsters maintained in short days (Jasnow, Huhman, Bartness, & Demas, 2000). Although no correlation has been reported between testosterone concentrations and spatial learning and memory among male meadow voles (Galea, Kavaliers, Ossenkopp, & Hampson, 1995), short days reduce testosterone, hippocampal volume, and spatial learning in deer mice (Perrot-Sinal, Kavaliers, & Ossenkopp, 1998). However, direct manipulation of testosterone in seasonally breeding rodents and the potential interaction between photoperiod and testosterone has rarely been investigated in learning and memory paradigms (Pyter, Trainor, & Nelson, 2006). Testosterone increases the volume of song-production brain regions in songbirds (Ball et al., 2004) and altered neurochemistry (Bittman, Tubbiola, Foltz, & Hegarty, 1999) and neuroanatomy (Gomez & Newman, 1991) in photoperiodic rodents. For example, testosterone increases the number of cells expressing the proopiomelanocortin gene in the hypothalamus (Bittman et al., 1999) and restores neuronal branching and morphology within the amygdala (Gomez & Newman, 1991) of castrated Syrian hamsters. Steroid hormones have important effects on learning and memory although there are species differences. Despite the presence of androgens and estrogens in both males and females, for the most part studies on males have focused on the role of androgens whereas studies on females have focused on the role of estrogens. In addition to manipulating photoperiod to induce seasonal changes in brain and behavior, studies have mimicked similar seasonal results by manipulating melatonin, the physiological signal into which ambient photoperiod is transduced (Bartness et al., 1993; Carter & Goldman,

Figure 4.6 Input pathway from the retina to the pineal gland in mammals.

Pineal

SCN

PVN

OC

Eye SCG

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1983; Nelson, Badura, & Goldman, 1990). The synthesis of melatonin occurs exclusively at night and is inhibited directly by light. The duration of melatonin release is proportional to the length of the dark phase (Illnerova, Hoffmann, & Vanecek, 1984); consequently, short-day animals experience longer durations of melatonin than do long-day animals. Infusion of appropriate duration profiles of physiological concentrations of melatonin to pinealectomized hamsters has shown the critical role of melatonin duration in signaling day length (Bartness et al., 1993). Peromyscus and other small rodents, whose gestations are relatively short (about 3 weeks), are long-day breeders and respond reproductively to long-day (short-duration) melatonin signals. Melatonin affects circadian rhythms of behavior (Golombek, Pevet, & Cardinali, 1996), but also provides seasonal information throughout the body. Melatonin receptors are distributed discretely throughout the rodent brain (Drew et al., 2001; Dubocovich, Rivera-Bermudez, Gerdin, & Masana, 2003; Weaver, Carlson, & Reppert, 1990). Importantly, melatonin receptors are present in the hippocampal area (entorhinal cortex) and other nonreproductive regions of rodent brains (Musshoff, Riewenherm, Berger, Fauteck, & Speckmann, 2002; Weaver et al., 1990), providing a neural substrate for direct effects of melatonin on learning and memory and other cognitive and motivated behaviors (described next). Manipulation of melatonin duration to mimic long or short days induces seasonal changes in behavior. Removal of the source of melatonin via pinealectomy impairs photoperiodic responses (Bartness & Goldman, 1989; Goldman, 2001). The neural mechanisms regulating circadian changes in melatonin secretion have been well characterized (Figure 4.6). Circadian rhythms in melatonin secretion in most mammals depend on neural efferents from the SCN to the region of the paraventricular nucleus of the hypothalamus (PVN). This projection continues through the medial forebrain bundle to the superior cervical ganglion (SCG) of the spinal cord. From the SCG, sympathetic neurons drive pineal melatonin secretion during the dark, while melatonin production and secretion are inhibited during the light

Note. Light information is transduced into a neural signal in the retina and transmitted via a direct retino-hypothalamic tract to the suprachiasmatic nucleus (SCN). From the SCN, fibers synapse in the paraventricular nucleus of the hypothalamus (PVN). From the PVN, fibers travel through the medial forebrain bundle to the superior cervical ganglion (SCG). Postganglionic fibers from the SCG then project to the pineal gland to modulate melatonin production/secretion. OC  Optic chiasm.

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portion of the LD cycle (Cassone, 1990; Ganguly, Coon, & Klein, 2002). This multisynaptic pathway has been confirmed using the transneuronal retrograde tracer, pseudorabies virus, injected into the pineal gland (Card, 2000; Larsen, Enquist, & Card, 1998). This technique confirmed the links in the pathway from the SCN to the pineal gland, and also suggested that two parallel circuits from the SCN (one from the dorsomedial and one ventrolateral) likely drive melatonin secretion. This day-night regulation of melatonin is regulated by the SCN (for a review, see Cassone, 1990), and, as with other hormonal systems mentioned previously, lesions of the SCN abolish circadian rhythms in melatonin production and secretion (Scott, Jansen, Kao, Kuehl, & Jackson, 1995; Tessonneaud, Locatelli, Caldani, & Viguier-Martinez, 1995). Although melatonin influences GnRH secretion, this hormone does not appear to act directly on GnRH neurons. Given the widespread distribution of the GnRH system, it has been difficult to determine the melatonin-sensitive systems that, in turn, act on GnRH neurons to regulate their activity. Because regression of the reproductive axis is, in part, due to melatonin-mediated increases in the negative feedback of sex steroids, those brain regions co-expressing melatonin receptors and androgen receptors may be critical in this response. The dorsomedial hypothalamus (DMH) of Syrian hamsters binds both melatonin and androgen with high affinity (Maywood, Bittman, & Hastings, 1996). Lesions of the DMH block short-day and melatonin-induced regression of the reproductive system (Lewis, Freeman, Dark, Wynne-Edwards, & Zucker, 2002; Maywood et al., 1996), suggesting that the DMH is a key target of melatonin in this species. In other species such as Siberian hamsters, melatonin feedback to the SCN is critical for melatonin-induced seasonal alterations in several physiological parameters. Lesions of the SCN block the effects of daily, long-duration melatonin infusions (i.e., short-day pattern) on body mass, fat pad distribution, and reproductive function (Bartness, Goldman, & Bittman, 1991; Bittman, Bartness, Goldman, & DeVries, 1991). This finding suggests that the circuit beginning with the SCN also requires the SCN as a target. In addition to acting on the DMH, melatonin binding is largely seen in the pars tuberalis of seasonally breeding mammals (Bittman & Weaver, 1990; Weaver et al., 1990). In hypothalamic-pituitary transected sheep, melatonin implants in region of the pars tuberalis reduce prolactin secretion in a manner similar to short-day lengths (Lincoln & Clarke, 1997). However, melatonin implants in this region do not affect gonadotropin secretion, suggesting that this region may be important for the regulation of the lactotropic, but not gonadotropic, photoperiodic effects (Lincoln & Clarke, 1997). Research on the means by which melatonin regulates other parameters is still in its infancy.

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It is unclear whether these melatonin-sensitive targets project directly on the GnRH system or indirectly via projections to other peptidergic systems. One candidate intermediary system is kisspeptin, a peptide recently shown to have marked stimulatory effects on the GnRH system. After chronic exposure to short days, Siberian hamsters with suppressed reproductive function exhibit marked reduction in kisspeptin cell labeling in the anteroventral periventricular nucleus, a neural target of the SCN (Greives et al., 2007; Mason et al., 2007). In common with other species, a subset of individual Siberian hamsters fails to respond to day-length information, however, and maintains their reproductive function. These so-called photoperiod nonresponsive individuals exhibit kisspeptin expression akin to that of long-day animals, suggesting that short photoperiods (and melatonin) are ignored by the brains of these animals. These results suggest an important role for kisspeptin in coordinating and relaying environmentally relevant information to the reproductive axis as well as a role for this peptide in regulating seasonal changes in reproductive function (Revel et al., 2006, 2007). A number of studies suggest a role for clock genes in the control of seasonality (Hofman, 2004; Johnston et al., 2003; Lincoln, Andersson, & Loudon, 2003). In Syrian and Siberian hamsters, photoperiod alters the duration of clock and clock-controlled gene expression, while the amplitude of gene expression is influenced by photoperiod in the pars tuberalis (Johnston et al., 2003; Messager, Hazlerigg, Mercer, & Morgan, 2000). In sheep, however, the relative timing of clock genes is altered by photoperiod in the pars tuberalis, providing a mechanism of temporal encoding and downstream control (Hazlerigg, Andersson, Johnston, & Lincoln, 2004; Lincoln et al., 2003; Lincoln, Johnston, Andersson, Wagner, & Hazlerigg, 2005; Lincoln, Messager, Andersson, & Hazlerigg, 2002). These correlational results are intriguing and suggest that phase and/or amplitude of clock and clock-controlled genes in SCN brain targets and endocrine glands may predict their responsiveness to upstream signals on a daily schedule. Seasonal Changes in the Avian Song System Associated with seasonal breeding are activities related to reproduction such as territorial defense, migration, or communication. For example, male canaries sing more frequently in spring than in winter, and they appear to lose components of their songs after each breeding season and incorporate new song components each spring (Brenowitz & Beecher, 2005). In spring, as day length increases, the testes grow and secrete androgens, song frequency increases, song repertoire enlarges, and the higher vocal center (HVC) and the robust nucleus of the archistriatum (RA), two brain

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nuclei necessary for song production, double in size. Under autumnal day lengths, testes regress in size and androgen production virtually stops, frequency of singing decreases, song repertoire shrinks, and the HVC and RA regress in size (Nottebohm, 2005). Treatment with testosterone in autumn mimics spring hormonal conditions and supports song production. The seasonal plasticity in behavior has been assumed to reflect the seasonal changes in brain morphology induced by hormonal adjustments (Tramontin & Brenowitz, 2000), although the precise mechanisms underlying seasonal plasticity in song complexity remains unspecified (Brenowitz, Lent, & Rubel, 2007). Estrogens, converted from testicular androgens or produced de novo in CNS neurons (Schlinger, Soma, & London, 2001), appear necessary to activate the neural mechanisms underlying the song system in birds. Androgens enter neurons containing aromatase, which converts them to estrogens. Aromatase is generally localized in neurons adjacent to other neurons containing estrogen receptors in the hypothalamus and preoptic area of songbird brains, as well as in limbic structures and in the structures constituting the neural circuit controlling bird song. The brain appears to be the primary source of estrogens, which activate masculine behaviors in many bird species (London, Monks, Wade, & Schlinger, 2006). Photoperiod is important in some birds to mediate these changes, including recruitment of new neurons (Nottebohm, 2005). Testosterone, or its metabolites including estrogens, appears to drive the seasonal changes in brain structure and behavior. In addition to seasonal changes in singing behavior, substantial seasonal changes occur in the morphology of several song nuclei. For example, the volume of the HVC and RA increases by 99% and 77%, respectively, among male canaries maintained under spring day lengths (12 hours light/day) relative to birds in autumnal conditions ( 12 hours light/day; Nottebohm, 1981). Similar results have been reported for more than 25 other bird species (reviewed in Ball, Riters, & Balthazart, 2002). These seasonal changes in the size of specific brain structures are probably mediated by testosterone or its metabolites (Ball et al., 2002; London et al., 2006). Testosterone appears to act via brain-derived neurotrophic factor (BDNF) to promote survival of new neurons in the brains of adult songbirds (Rasika, Alvarez-Buylla, & Nottebohm, 1999). Testosterone upregulates BDNF in the HVC of adult male and female canaries and infusion of antibody against BDNF blocks androgen-induced neurogenesis. Behavioral feedback is important because singing increases BDNF expression in the HVC in proportion to the number of songs produced (Li, Jarvis, Alvarez-Borda, Lim, & Nottebohm, 2000). BDNF seems to determine life expectancy of newly born cells in adult songbird brains.

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Vernal elevation in testosterone concentrations coincident with long days provokes BDNF production that protects new neurons within 10 days of cell birth (Alvarez-Borda, Haripal, & Nottebohm, 2004). Other avian models of seasonal brain plasticity include seasonal change in hippocampal volume of food-caching (Hoshooley & Sherry, 2007; Smulders, Sasson, & DeVoogd, 1995) and brood parasitic (Sherry, Forbes, Khurgel, & Ivy, 1993) birds. The hippocampus is involved in spatial learning and memory, and generally, species with a larger relative hippocampal volume display better spatial learning and memory (Sherry, Jacobs, & Gaulin, 1992). Hippocampal size is reduced during the winter in these bird species when spatial learning and memory performance associated with food storing (Barnea & Nottebohm, 1994, 1996) and nest parasitism (Sherry et al., 1993) is reduced. Comparable studies of seasonal brain plasticity in mammals have been relatively rare, despite the fact that seasonal breeding has been well-documented in nontropical mammals. Seasonal Changes in Mammalian Brain and Behavior Although the brain constitutes only about 2% to 3% of the total body mass in rodents, it consumes over 10% of total energy expenditure (Mink, Blumenschine, & Adams, 1981). Thus, minor reductions in brain mass could save significant energy (Jacobs, 1996). Seasonal changes in brain weight have been documented in rodents and shrews (Yaskin, 1984). Brain weights are higher in summer than winter (Yaskin, 1984). Winter brain mass is reduced in grey squirrels (Sciurus carolinensis; Lavenex et al., 2000b) and ferrets (Mustela putorius; Weiler, 1992). Also, brain mass (absolute and corrected for body mass) and specific brain regions (e.g., hippocampus) are reduced during winter in common shrews (Sorex araneus) and bank voles (Clethrionomys glareolus; Yaskin, 1984). A significant part of the seasonal change in brain weight might merely reflect differences in water content; however, the neocortex and the basal portion of the brains (i.e., the corpus striatum) of rodents and shrews show seasonal cytoarchitectural changes. Seasonal changes in hippocampal morphology have been reported in hibernating ground squirrels (Citellus undulatus; Popov, Bocharova, & Bragin, 1992) and meadow voles (Microtus pennsylvanicus; Galea & McEwen, 1999), although hippocampal volume did not vary with season in grey squirrels (Lavenex, Steele, & Jacobs, 2000). In European hamsters (Cricetus cricetus) and sheep, seasonal changes in the innervation of the brain have been reported (Buijs et al., 1986; Xiong, Karsch, & Lehman, 1997). Seasonal changes in neuroendocrine function (Wehr, 1998), hypothalamic peptide expression (Hofman & Swaab,

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1995), and serotonin function (Alstadhaug et al., 2005; Brewerton & George, 1990) have also been reported in humans. Seasonal changes in human behavioral pathology are also observed in anxiety and depression (Enns et al., 2006; Lewy, Lefler, Emens, & Bauer, 2006; Rosenthal et al., 1984; Sigmon et al., 2007), migraine headaches (Alstadhaug et al., 2005; Brewerton & George, 1990), as well as incidence, severity, and mortality of strokes (Carolei, Marini, De Matteis, Di Napoli, & Baldassarre, 1996; H. Wang, Sekine, Chen, & Kagamimori, 2002; Y. Wang et al., 2003). Thus, despite the relative lack of seasonal organization of reproductive function, it is apparent that humans retain responsiveness to photoperiod (reviewed in Bronson, 1995, 2004), and that photoperiod-mediated adjustments in rodent brain and behavior may be important to understand seasonal changes in human brain and behavior. Photoperiod also affects cell division in the dentate gyrus and subependymal zone of adult mammals (Huang, DeVries, & Bittman, 1998). Long-term exposure to short days in Syrian hamsters doubles the number of new neurons produced in these brain regions, as well as in the hypothalamus and cingulate-retrosplenial cortex (Huang et al., 1998). There are no appreciable photoperiodic differences in brain volume of either the granule cell layer of the hippocampus or the dentate. These results are in contrast to studies of Peromyscus which likely represents a species difference. No differences in cell proliferation are observed in brains of grey squirrels (Lavenex et al., 2000), although the use of low doses of BrdU may not pick up differences (Gould & Gross, 2002). Motoneurons controlling penile muscles, neuromuscular junctions, and somas are smaller in adult male Siberian hamsters and white-footed mice exposed to short days compared with animals exposed to long days (Forger & Breedlove, 1987). Adult male deer mice (Peromyscus maniculatus) maintained in short days have smaller brains and lower adjusted hippocampal volume relative to those maintained in long days (Perrot-Sinal et al., 1998). Thus, several examples of seasonal influences on several brain measures exist, but what is lacking is an understanding of how seasonal factors influence neural structures in mammals. It is likely that seasonal brain changes will be subtle in mammals compared to birds because birdsong generally displays an all-or-none seasonality; seasonal changes in learning and memory are consistent, albeit subtle, and such variation likely provides important grist for evolutionary processes. Seasonal Changes in Learning and Memory Deer mice and white-footed mice tested during the breeding season display better spatial learning performance compared to mice tested during the nonbreeding season (Galea,

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Kavaliers, Ossenkopp, Innes, & Hargreaves, 1994; Pyter, Reader, & Nelson, 2005). This may represent differences in territory size, and therefore spatial memory requirements (Jacobs, 1996). The relationship among home range size and spatial learning and memory has been established in several rodent species. For example, male Peromyscus maniculatus have larger home ranges than females during the breeding season that decline in winter (Bronson, 1985, 1988). This is paralleled by superior spatial learning and memory performance in male Peromyscus as compared to females in long, but not short, days (Galea, Kavaliers, & Ossenkopp, 1996; Galea et al., 1994). The effects of photoperiod on learning and memory are consistent with a life-history explanation of reproductive strategy. Both hippocampal size and spatial learning and memory performance are sexually dimorphic among polygynous rodents (Jacobs, 1996). Polygynous male rodents outperform females in spatial tasks and have larger hippocampi (Galea et al., 1994, 1996; Jacobs, 1996). In common with birds, spatial learning and memory performance is often positively correlated with hippocampal size in mammals (Jacobs, Gaulin, Sherry, & Hoffman, 1990; Sherry, Jacobs, & Gaulin, 1992). In food caching birds, one might predict that hippocampal volume increases during winter (Pravosudov & Clayton, 2002), and that caching behaviors and hippocampal volume should increase among individuals inhabiting particularly harsh conditions. Indeed, black-capped chickadees (Poecile atricapillus) from the most harsh conditions (i.e., exposed to lowest temperatures, shortest day lengths, and most snow cover) had the largest hippocampal volumes and most hippocampal neurons compared to birds from mild conditions (Roth & Pravosudov, 2008). Thus, environmental conditions can shape specific brain structures in precise ways to enhance survival and reproductive success. Seasonal Changes in Aggression It is well documented that androgens regulate aggressive behavior, especially the dramatic male-male interactions associated with mating territories. For instance, many male mammals and birds set up and defend territories before the onset of the breeding season (Goymann, Landys, & Wingfield, 2007; Wingfield, Jacobs, & Hillgarth, 1997). Females often choose males on the basis of resources available in these territories so evolutionary pressures are high on males to compete successfully. Aggressive behavior is costly, both in terms of energy and in terms of potential injury or death. Thus, individuals often exchange information about the likely outcome of the aggressive encounter without actual combat. The energetic costs and survival from wounds vary seasonally leading to fluctuations in

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the likelihood of aggressive encounters as the cost-benefit ratio changes. Individuals of several rodent species undergo a seasonal shift from highly territorial, asocial behavior during the breeding season to a social, highly interactive existence during winter. Such species typically undergo reproductive quiescence at the end of the breeding season in response to short days. The resulting decrease in androgen secretion may be necessary or permissive for the seasonal shift in sociality, but in wood rats (Neotoma fuscipes) nonsteroidal mechanisms mediate the seasonal change in social behavior (G. S. Caldwell, Glickman, & Smith, 1984). The seasonal change in social organization confers several advantages. During the breeding season, rodents control resources that promote their own survival and that of their offspring, and they often aggressively exclude nonkin from access to resources. During the winter, however, this strategy is abandoned in favor of group living that conserves energy and enhances survival in the face of low temperatures and reduced food availability. Many species of rodents conserve energy during the winter by forming aggregations of huddling animals (Madison, 1984). In these aggregations, different sexes and even different species commingle. Even in the absence of huddling behavior, animals may tolerate one another better in close quarters during the winter than during the breeding season. For example, male meadow voles (Microtus pennsylvanicus) are highly territorial in the spring and summer and occupy open meadows, whereas red-backed voles (Clethrionomys gapperii) breed in forest habitats. During the winter months, meadow voles migrate into the spruce forest habitats occupied by the redbacked voles, presumably to take advantage of the protective cover provided by the trees. In some cases, they share nests with individuals of other rodent species (Madison, 1984). Consistent for a role of androgens mediating both mating and aggression, male meadow voles trapped during the winter and tested in paired encounters in a neutral arena displayed less interspecific aggression than voles trapped in summer. The winter reduction in aggressiveness permits energy-saving group huddling. As the animals enter their breeding condition in the spring, they reestablish mutually exclusive territories. Some males within a population do not undergo reproductive regression, maintaining testicular function and producing sperm and androgens during simulated winter conditions (for review, see Prendergast, Kriegsfeld, & Nelson, 2001). The advantages of continuous breeding capability evidently incur substantial hidden costs because only a minority of each population adopts this strategy. One such cost may be that reproductively competent males, because of unusual aggressiveness during the winter, are unable to participate in communal huddling and thus incur greater energetic costs in overwintering.

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High behavioral and energetic costs associated with the maintenance of the reproductive system in winter may explain why nonregressive types do not normally predominate in temperate or boreal-zone populations of rodents (Prendergast et al., 2001). This observation is supported by a field study of winter nesting behavior of prairie voles (M. ochrogaster; McShea, 1990). Most voles in the population studied were reproductively inactive during the winter and formed groups of huddling individuals. Two males, however, remained in breeding condition and never huddled with other animals. In pairwise aggression tests, these two males were much more aggressive than reproductively quiescent individuals. In another study, reproductive status also influenced odor preferences of meadow voles maintained in simulated winter day lengths (Gorman, Ferkin, Nelson, & Zucker, 1993). Males that retained reproductive capability in winter day lengths preferred the odors