Proceedings)

Prof. Dr. Gerhard Strube, Univ. Freiburg (IIG)

IK 2001

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Cognitive Science An Introduction

Gerhard Strube

Centre for Cognitive Science University of Freiburg, Germany

Part 1: Characterising Cognition o o o o o o o o

The science of the mind What is cognition anyway? Cognition for adaptation Consciousness Mental representation Cognition: embodied, situated, social Social agents do communicate What is a cognitive system?

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Cognitive Science o

Cognitive Science [CogSci] is a new discipline Philosophy (at least 400 BC), Psychology (ca. 1870), Artificial Intelligence [AI] in 1956 v The birth of CogSci: an Arthur D. Sloane Foundation program, launched in 1975 v Journal founded in 1977, Cognitive Science Society in 1979 v

o

There are (too) many definitions of cognition, and only some of these are fruitful. „Life is cognition“ (Maturana & Varela, 1986): I‘m not interested in the cognition of cabbage and carrots! v Cognition = consciousness: But most of it is not conscious! v

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CogSci is the science of the mind o

Let’s define CogSci through its methodology: m

‘Cognitive Modeling’ produces generative theories v

m

Our theories generate, step by step, the very phenomena they seek to explain

Cognitive modeling integrates different traditions: Formal analysis (logic, philosophy, theoret. linguistics) v Empirical analysis (psycholog. & neurosc. experiments) v Constructive synthesis (AI programming) v

m

Cognit. Modeling fits with CogSci’s general hypothesis: v

Cognitive processes are computational, cognition is information processing.

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What is cognition anyway? o

The subject matter of CogSci: MIND m

Is MIND a new kind of substance? v

m

(Descartes 1641: „res cogitans“, the matter that thinks)

Is MIND identical to the BRAIN? Reductionism: If we know all about the brain, we‘ll know everything that can be known about the mind v Functionalism (e.g., J.Fodor): Mind is the software of the brain v

It is neither feasible nor interesting to describe MIND through the biophysical functions of each and every neuron in the brain u The very same cognitive state may be differently ‘implemented’ in different individuals, or even in the same individual at diff. times u

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Perspectives on cognition (1) o

The phenomenological view: Cognition as consciousness Introspection and early psychology m philosophical phenomenology and the body (MerleauPonty, 1945) m “Causal powers of the brain, and consciousness, but nothing else” (Searle, 1990) m Problems of subjectivist approaches: the Mind-Brain Problem m

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Perspectives on cognition(2) o

Die Cartesian tradition: cognition as symbol manipulation Leibniz (ca. 1670): envisioning a calculus of thinking m Frege (1879): inventor of modern logic (tool for thinking) m Turing (1937): inventor of the (abstract) universal symbol-processing machine m Newell & Simon (1975): Physical Symbol Systems as the ‘necessary and sufficient’ condition for intelligent behaviour m

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Perspectives on cognition(3) o

A biological view: cognition as adaptation of a system with respect to its environment m

o

Time scales of adaptation: u Phylogenesis u Ontogenesis (learning) u ‘actual genesis’ (thinking)

Integrating different views: Cognitive systems

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Cognition for adaptation (1) o

sensors

effectors o

SYSTEM

o

environment

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Non-cognitive systems that exhibit selfregulation, use a preformed inflexible coupling that cannot be altered through learning. Example: an amoeba Note that all ‘higher’ species exhibit this type of non-cognitive regulation as well!

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Cognition for adaptation (2) o

cognition

o o

sensors

effectors SYSTEM

environment

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Cognitive systems are selfregulating in their environment by: a flexible coupling, that may be modified through experience , and lets the system choose between different alternative behaviours. (For me, cognition proper starts with learning)

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Thinking as reasoning about action (S. Freud, 1900: “Probehandeln”) o o

o

Deliberating alternative courses of action and anticipating possible consequences is the prototypical cognitive act. (Potentially) conscious mental representations, I.e., ‘objects’ and ‘events’, are ideally tuned prerequisites of action and reasoning about action (Prinz, 1996) Possibly, consciousness is just the cognitive process by which mental try-outs are realised: m m

The contents of consciousness are a model of the world, and a model of ourselves as acting agents in that model world The view of consciousness as a model acknowledges the subjective and constructivist (but at least partly realistic) character of what we perceive and think about the world.

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Consciousness o

Consciousness is actively constructed Its constructivist character shows in illusions and neuropsychological phenomena like blindsight v According to philosophers Dennett (1991) and Metzinger (1993), consciousness results from constructing a model of one’s self and integrating it into our model of the world v

o

The function of consciousness Consciousness is in the service of action control because it provides a flexible coupling between perception and action, partially independent of reality itself. v We may assume that this is why consciousness developed during phylogenesis. v

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Consciousness and the Computational Theory of Mind (CTM) o

If we view MIND as ‘the brain’s software’, it follows that there are: data structures (mental representations: world model, self model) as the result of actual perception (or knowledge stored in memory), m computational processes like comparing entities, computing similarities or analogies, deductive reasoning, etc., that operate upon the data structures m

o

It also follows that our mind must monitor our action in order to learn sth. about the possible consequences of action.

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Mental Representation o

Traditional cognitive science: veridical symbolic mental representations m algorithmic processes, defined over these representations m Newell & Simon (1975): Physical Symbol System Hypothesis m

o

The ‘new’ AI and CogSci: Mental representations are sufficiently adequate constructs (‘satisficing’: Simon, 1955) m There are many (algorithmic, partly stochastic) units that cooperatively produce a system’s behaviour m

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Is there a language of thought? o

Complexity The ‘objects’ and ‘events’ we mentally represent may have a very complex structure (e.g., a nested one) m The linguistic expressions we use – I.e., our sentences – are of high (recursive) complexity m

o

‘Mentalese’ Fodor (1975) argues that our mental representations are language-like m The computational consequences are: We need variable binding and recursion m

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Summary:

Functionalism, the philosophical foundation of CogSci o

Cognition (the MIND as compared to the body) is a field that has an existence of its own m rests on physical (e.g., brain) processes, m but is not reducible to them. m

o

Three levels of theory (Marr, 1982; Newell, 1982): knowledge level, or computational level m symbol level, or level of representations and algorithms m level of implementation m

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How relevant is implementation? o

Traditional CogSci: v

o

Since higher levels of description are logically independent of the lower ones, issues of implementation have largely been ignored.

Critique: System behaviour and especially response times are highly dependent on implementation, in spite of being logically independent. m Architecture and processing belong together. A given architectures may be well-suited for a certain algorithm or not. m

v v

AI: Real-time systems, any-time algorithms ... CogSci & Psychology: Reaction times are a key variable

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Modes of adaptation – steps of cognition? o

o

o

o

Innate behavioral coordinations are evolutionary formed and exist in all living systems (not memory-based) Elementary learning (conditioning of stimulus-response associations): ontogenetic adaptation, exist even in lower animals like flatworms (associative memory) Generalisation and categorisation: established for birds and mammals (semantic memory) Episodic memory, planning (and probably consciousness): possible in mammals only, certain evidence only for primates (apes & humans) This memory hierarchy is due to Tulving (1983)

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Embodied Cognition o

Cognitive processes always happen in systems that are not only cognitive. Natural cognitive systems (organisms), in phylogenesis, had everything necessary for survival and reproduction before they acquired CNS and cognitive processes. m Technical systems have basic, non-cognitive functions as well (think of Brooks‘ robot architecture, with noncognitive ‘behaviors’ and eventually cognitive levels on top). m

m

If you happen to read about ‘cognition in single cells’ etc., mind that this is just metaphor. We use the term ‘cognition’ for adaptation that is based on representation and learning.

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Situated Cognition o

Being adaptive, cognition happens in the environment (not in empty symbol-space) Not everything is represented mentally (e.g., in planning): We make use of external representations, like signposts m Good design helps interaction (you need not remember where to grasp a coffee mug) m

o

Autonomous Agents (AA) Systems that are fit for survival in their environment are called autonomous. m Systems that pursue their own goals are called AA. m

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Social Cognition o

We are social by nature Recently, some ‘agent architectures’ have been proposed with a ‘social level’ on top. However… m … all naturally cognitive beings are social from the very start. They are autonomous & social agents. m

Sexual reproduction is basically social, and predates cognition in evolutionary terms. v Individual cognitive development is dependent on social rapport and learning in social settings. v Human cognitive development additionally depends on cultural heritage: language (mother tongue), artifacts, institutions… v

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Social Agents Do Communicate o

Natural communication is rich (almost) all animals communicate within their species m variety of means, e.g., odours, colour, gestures, etc. m human language (spoken, written, signed) is the only medium that is independent of context (we can talk about things past, or not existant at all) m but non-verbal communication (posture, gestures, emotional expression, etc.) is important as well m

o

Technical communication, in contrast, is still limited to application-specific formalisms, m but that is bound to change! m

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What is a Cognitive System? o

a ‘single mind’, but situated: an autonomous agent m

o

a homogeneous group of agents m

o

examples: a human being, an animal, a robot, a softbot ex.: a team (of people or robots)

a heterogeneous group of agents m

ex.: HCI (human-computer interaction) v v

o

traditionally: one human (the user), one computer system currently: any group of agents formed with respect to a task ex.: a cockpit (Hutchins, 1995)

Interaction with the environment and communication among agents are of fundamental importance.

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A Mind Teaser For You… m

Monty Hall was a US Quizmaster back in the Fifties. Here is the typical Monty Hall Problem: You are the candidate, facing 3 doors. The prize is behind one (and only one) of these 3 doors. v You choose the door that you believe to hide the prize. v Quizmaster Monty Hall (who of course knows where the prize is) now opens not the door you chose, but another one – and shows you that the prize is not there. He offers you to switch your choice from the door you have chosen to the other, still closed door, for a payment of ten dollars. v v

m

Would it be wise to accept Monty’s offer?

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Part 2: Cognition & Action Control o o o o o

autonomous agents rational choice & human rationality a look at nature: hunger & feeding behavior interactions between cognitive and non-cognitive control three levels of action control

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Embodied Cognition Revisited o

Cognition, being integrated into our body and our social life, must be studied as a functional domain integrated with others. m m

o

What we do is not only determined by cognitive factors. Motivation research seeks to uncover the causes of our deeds v biological needs & drives; secondary (learned) motives v situative stimuli (incentives) v action control: how we build intentions and plan actions

Traditional AI systems follow just one objective, which has been set by the system’s designers. The ‘New AI’ systems are autonomous and try to satisfy multiple objectives.

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Autonomous Agents o o o o o o

“A complex agent has complex goals. First of all, it has many goals, second the goals it has vary over time, third they have different priorities, and fourth their priorities vary according to the situation and according to their interrelationships. So it is definitely important that an autonomous agent can mediate among goals and handle their conflicts or even try to exploit their interrelationships to optimize their achievement over time.” (P. Maes, Situated agents can have goals, 1990)

o

How can it be done? It needs cognition – and what else?

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The Classical Theory of Motivation and Decision: Rational Choice (1) o

Expectancy-value models of risky choice: m

Lewin, Dembo, Festinger & Sears (1944): Vr = Vs × Ps + Vf × Pf (resulting valence = sum of positive [s = success] and negative [f = failure] valences, each weighted by expected probability of occurrence)

m

Atkinson (1957): V=M×I (Valence as product of personal motive strength and incentive strength)

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The Classical Theory of Motivation and Decision: Rational Choice(2) o

Expectancy-value models (ctn‘d): m m m

o

Lewin, Dembo, Festinger & Sears (1944): Vr = Vs × Ps + Vf × Pf Atkinson (1957): V = M × I Atkinson & Feather (1966):

Tr = Ts + Tf = Ms × Ps × Is + Mf × Pf × If (resultant tendency = sum of positive [approach] and negative [avoidance] tendencies; each tendency = product of motive strength, expected probability of success or failure, and positive or negative incentive strength) Rational choice has become the dominant paradigm in economy, as well as in the psychology of motivation.

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Rational Choice and Human Rationality o

o o

o

Rational Choice presupposes complete processing of all the relevant information (e.g., probabilities of success). Therefore, rational choice models only work for highly uncommon situations; they are biologically unrealistic. Putting it fundamental: human rationality in everyday settings cannot be adequately described by rational choice models. An example you already know: The Monty Hall problem

o

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An Example of Action Control: Hunger and Feeding (1) o

A biological control circuit: m

a well-known system with negative feedback

Fig.: Kandel, Schwartz & Jessell (1995, p. 615).

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Hunger and Feeding (2):

Rat Weightwatchers

o

o è è è o

Exp. by Keesey et al. (1976): For 4 weeks, newborn rats are allowed to eat how much they want. They grow weight. The sample is then randomly divided into 3 groups: Group a is stuffed. Group b is free to eat as before. Group c is set on a minimum diet. After 3 weeks, they all are allowed to eat how much they want, as in the beginning.

Fig.: Kandel, Schwartz & Jessell (1995, 618) G. Strube, Univ. Freiburg

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Hunger and Feeding (3):

Cognitive Control o

Of rats and men... m m

Weight control is difficult for us to achieve because of cognition! Physiological control by feedback is the ONLY system that controls feeding in rats, with minimal exceptions: v

o

When a rat has eaten sth. novel, and gets sick (e.g., by an injection) up to 36 hrs. in between, it will never taste that kind of food again.

Cognitive control in people: m m m

Beyond the primary biological need (hunger), there is a lot of secondary (learned, socially mediated) motives excitatory: tasty smell, appetizing looks, pleasant memories; effective advertising; trying to break a Guiness record, etc. inhibitory: table manners, wellness (diet) and striving for a slim body, but also religious motives (Lenten, fasting), etc.

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Interactions Between Cognitive and Non-cognitive Control o

Cognitive influence upon autonomous functions needs appropriate proprioceptive stimuli. in feeding, it’s the feeling of hunger m we cannot cognitively control our blood pressure, except when external measure is provided (bio-feedback) m

o

Cognitive mediation is variably effective m

you cannot hold your breath until you suffocate (although you can abstain from food until starving to death)

Cognitive control is not master, but modifier m Sometimes, cognitive influence can introduce severe pathological oscillations, as in anorexia & bulimia m

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Three Levels of Action Control symbolic-cognitive regulation (planning, goal management, action control)

associative-cognit. regulation

Interaktion durch Aktivierung und Hemmung, u.U. auch symbolisch; prinzipiell kognizierbar

(associativec learning: S-R, motive-goal-action)

physiological regulation

Interaktion nur partiell, durch Aktivierung u. Hemmung

(continuous feedback loops, but also reflexes) G. Strube, Univ. Freiburg

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Characterising the 3 Levels o

physiological (non-cognitive) regulation reflexes: only a few neurons; feedback loops: (neuro-)hormones v direct influence on effectors v no place for learning v

o

associative-cognitive regulation conditioning & stimulus-controlled elicitation of motor schemata v in principle, open to intentional control; may be partly represented on the symbolic level as well v

o

symbolic-cognitive regulation (deliberation) effecting muscles either directly, or through motor schemata allows for reasoning about action (deliberation) and decision based on mental simulation v cognitively penetrable (Fodor ’83), or even identical to consciousness v v

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Part 3: Human Information Processing o o

Perceptual & motor systems Working memory & attention mental rotation m capacity of short-term memory m Deploying cognitive resources m

o

Memory & forgetting

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Perceptual Systems o

Enormous processing capacity high degree of parallel execution m only the results of sensory processing are conscious m

o

High sensitivity and acuity the eye can perceive a single quantum of light m ears can determine horizontal direction to 0.3 degrees m

o

Robustness huge range of intensity, adaptive through habituation m Perceptual constancy, re. size, colour, etc. m context sensitivity m

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Motor Systems o

local control: reflexes m

o

three neurons are enough

two different central systems of control slow, voluntary movements under control of the frontal cerebral lobe, needs attention (pyramidal system) m automated motor programs, stored in the cerebellum v motor schemata are parameterised and elicited from the frontal cortex v this leads to ballistic movements, which cannot be modified or stopped during execution m

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The Bottleneck: Working Memory o

low capacity: only 5-7 units (‘chunks’, i.e., meaningful units) m gute Codierung (Referenz auf Inhalte des Langzeitgedächtnisses) erhöht durch komplexere Einheiten die Kapazität m

o

fast forgetting maintenance needs attention (concentration) m else contents vanish within seconds m

o

intentional (conscious, deliberative) action control inevitably puts a ‘load’ on working memory

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The Structural Theory of WM

Central Executive + Slave Systems: PL, VSSP o

PL: Phonological Loop

WM according to Baddeley & Hitch (1974), Baddeley (1986): centrale exekutive (CE), the coordinating and controlling subsystem, is not modalityspecific m phonological loop (PL), a subsystem specialised for spoken language (identical with short term memory, STM) m visual-spatial sketchpad (VSSP), a subsystem specialising on the manipulation of mental images m

CE Central Executive

VSSP: Visuo-spatial Sketchpad G. Strube, Univ. Freiburg

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VSSP: Mental Rotation (1) Shepard & Metzler (1971) The Experiment 8 Ss viewed a pair of objects on two HP vector displays m Objects were either identical (but most of the time rotated), or different m Ss had to decide by keypress as fast as possible whether both objects were identical m

m

m

Source: Shepard, R.N., & Metzler, J. (1971). Mental rotation of three-dimensional objects. Science, 171, 701-703. Fig.: Front cover, Science, Feb. 1971

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Mental Rotation (2) Shepard & Metzler (1971) o

Hypothesis If the objects are analogically represented, one of them must be rotated until it looks identical with the first one. m Therefore, reaction times should be proportional to the rotation angle. m In case of a non-analogical representation (e.g., 2 left, then 3, then one block…), no such relation follows. m

o

Result m

For all 6 objects and 8 Ss, RTs were almost linearly dependent on the angle of rotation.

Fig. Shepard & Metzler (1971), p. 702. G. Strube, Univ. Freiburg

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Sort-term Memory, or PL: Capacity = 7±2 units o

„The magical number Seven, plus or minus Two“ G. A. Miller (1956) m m m

o

„What‘s in a chunk?“ H. A. Simon (1966) m m

o

low capacity of short-term memory (PL according to Baddeley) constant capacity in terms of meaningful units (chunks) 7±2 in perception/attention as well (e.g., subitising) units (chunks) = knowledge assemblies in long-term memory Chunking = construction of complex units (e.g., chess positions)

‘higher’ capacity due to clever encoding of chunks m m m

ex.: M C M X C I X has seven units... ... or just one, if you recode it as the year 1999 the current world record (recounting more than 80 ciphers from short-term memory) is due to extensive recoding as well

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Selective Attention o

Selection is necessary in the service of action m

o

When during processing does selection occur? m

m

o

Sometimes, it would be good if we could do several things in parallel (e.g., being the pilot of an aircraft) In the early years of cognitive psychology (1950s), it was assumed that selection takes place before any processing occurs However, at least some (automatic) processing occurs even with unattended stimuli Today, we know that all information will be processed to some degree. The real bottleneck occurs because just one action can be prepared for execution at any given time.

Decision for action is the crucial step where selection occurs.

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Automatic and Controlled Processes (Shiffrin & Schneider, 1977) o

o o

Automatic processes develop by extensive training under constant stimulus-response (S-R) relations. Automatic processes don’t need central resources. Automatic processes will always be executed in the presence of an eliciting S, they are not controllable.

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o

o

Controlled processes are those actions that are under continuous cognitive control. *** Switching between different actions (S-R relations) costs central resources (and time) Interference between actions occurs whenever two tasks are structurally inkompatible.

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Dual-Task Performance o

Classical examples of successful dual-task performance due to automatisation: m

Allport et al. (1972): Sight-reading (piano) and shadowing a text, with students of music v

m

m

Note: shadowing = listening to a text over earphones and trying to repeat it as fast as possible.

Shaffer (1975): Skilled typists typed a text they read and shadowed another text (This won’t work the other way round because of structural interference between speaking and listening to language) Spelke, Hirst & Neisser (1976): Over 14 weeks, 2 student volunteers practised to read a story for retelling it while taking ‘blind’ dictation of single words.

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How to Deploy Cognitive Resources Metacognition and Strategies o

o

o

Strategic knowledge m Almost always, there is more than one way to do a given task. m It’s important to know which method is most efficient, and when. Metacognition has two related meanings: m Controlling our own cognitive functioning. m Assessing and predicting our own performance. Adaptive usage of resources m Setting parameters for executing a task: speedaccuracy trade-off

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Consequences of WM Capacity Limits o

Problem solving by systematic heuristic search is impossible for people m

o

Making use of the environment (situated cognition) m m

o

backtracking needs too much WM overhead Externalisation by taking notes, etc. ‘lazy planning’ by using situational opportunities (e.g., signposts): situated problem solving

Central Role of Long-Term Memory m m

Consistency, esp. spatial & procedural (routinisation) enables construction & application of task-specific action schemata

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Memory and Forgetting o

There is no known limit to how much we can store in memory m

o

Only our long-term memory has an unlimited capacity.

When we forget something, the reason may be that memory traces ‘erode’ (what’s not used, will be forgotten), or that v similar contents of memory interfere with what we want to remember v

m

m m

models of long-term memory (e.g., linear associative networks) demonstrate that both reasons may follow from the same basic architecture Forgetting follows a well-known curve: We forget much during the first minutes/hours, and lesser and lesser the longer the distance Whenever we use certain contents of memory, they get strengthened. Thereby, forgetting is a kind of mental hygiene.

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Strategic Effects o

Retrieval Plans m

m

o

We all acquire effective strategies re. memory: We organise contents, use multiple encoding, utilise already established schemas in LTM. Certain amnestic patients show a dissociation: They have no ‘memory’ (neither recall nor recognition) but implicit memory: v it shows in word completion tasks (Warrington & Weiskrantz, 1968) v and in savings when learning to recognise drawings

Levels of Processing (Craik & Lockhart, 1972) m

Intention and attention (e.g., orthography v. semantic judgments) determine what and how it is stored in memory.

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Part 4: Concepts and Schemas o o o o o o o

Semantic memory The traditional theory of concepts Prototype theory Concepts as theories, as schemas Event schemas (scripts) Visual object schemas Analogical representation in LTM

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Conceptual Knowledge Constitutes Semantic Memory o o o

o o

o

Concepts are cognitive categories, tools for thinking and communicating about everything. Concepts are abstractions based on similarities (functional, visual, etc.) of things experienced. Usually, concepts have a ‘name’, i.e., there is a word for them (a noun for objects, a verb for events, etc.). Semiotic theory holds that words designate things via the concepts we hold in mind. Not only humans, but also apes, esp. chimps, are able to develop concepts and use symbols consistently. Ontogenesis: Our first concepts relate to concrete objects and frequently repeated actions (J. Mandler, 1986); we acquire an ontology according to systematic constraints (F. Keil, 1981). Actual genesis (concept learning & induction): How much we generalise depends on our background knowledge re. variability of certain features in the domain (Nisbett et al., 1983).

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The Classical Theory of Concepts m

m

o

Necessary and sufficient features define whether something is to be regarded as an instance of a concept, e.g., a natural category like BIRD. This view, dating from Aristotle (ca. 330 BC) underlies all ‘ontologies’ in AI and Computational Linguistics.

Problems with the classical theory m m m

We always come across exceptions (e.g., birds that cannot fly: because they are penguins, or have a wing broken) Even for artificial, well-defined categories like ‘odd’ and ‘even’ numbers, not all instances are equal psychologically. Often, it is not quite clear whether something is a member of a category or not: Is a tomato an instance of fruit or of vegetables? Is a carpet, or a mirror, an instance of furniture or not?

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Prototype Theory (1) o

Basic Results m

m m

Concepts are defined by family resemblance (according to Wittgenstein, 1953), e.g., all games exhibit certain common features, but no feature is common to all games. Psychologically, instances within a category are graded according to their typicality re. the concept (Rosch, 1973) The Prototype is the ‘best’ exemplar (Rosch et al., 1976) v

m m

Note: Since it is an idealisation, it need not exist (except in mind)

There are basic categories (e.g., ‘chair’). Usually, they are acquired early in life and are linguistically designated with simple words. Basic (and sub-)categories have only one prototype, but superordinate categories (e.g., ‘furniture’) may have several.

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Prototype Theory (2) o

Empirical demonstrations m

m

o

Category boundaries may shift dependent on context (Labov, 1973: The boundary between CUP and BOWL is different for a content of ‘coffee’ or ‘stew’) Prototypes may be acquired from exemplars that do not contain the prototype itself (Posner & Keele, 1970, using random variations of two basic dot patterns)

Problems with prototype theory m

m

Typicality is not compositional: Although a ‘guppy’ is neither a typical fish nor a typical pet, it is a highly typical instance of pet fish Barsalou (1983) found typicality effects even for ad hoc categories like ‘things you could sell at a garage sale’. This finding puts in doubt the assumption that prototypes are stored in memory.

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Other Theories of Concepts... o

Exemplar theories (e.g., m

o

Concepts as theories about parts of the world m

o

There are no representations of concepts, only of instances. Prototypes are abstracted on the fly. However, this assumption is highly un-economic, although working models have been proposed (Hintzman, MINERVA). Rumelhart (1986) and others hold that concepts have no real boundaries at all, but are just regions of our world knowledge, i.e., concepts are just ‘bundles of explanations’.

Schema Theories m

Very popular for internally structured concepts (e.g., event representations). The best-known formalism is ‘frames’. v

Note: The usage of ‘schema’ is highly individualistic in CogSci!

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Schemas For Representing Events o

o

Scripts (Schank & Abelson, 1977; Schank, 1982): Schemas that enable inferences about routine events. Psychological “reality” of scripts v Intrusions in recall, failing discrimination in recognition of content typical for a script (Graesser, Kowalski & Smith, 1980) v no effect of learning on typical script contents

(Vaterrodt & Bredenkamp, 1989) o

Scripts in text comprehension: Situation models (van Dijk & Kintsch, 1983; Kintsch, 1996), mental models (Johnson-Laird, 1983; Glenberg, 1994)

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Asterix and

An Example of Scripts (1) etc. ...

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from: Asterix, vol. 3 59

Asterix and the Pirates An Example of Scripts (2) o

Scripts (as background knowledge) are helpful m

to infer what has not been told (bridging inferences) v

m

Ex. from Schank & Abelson: „John went to a restaurant. He ordered chicken. He left a large tip.“

Bridging may be delightful, because the filling-in is left to your imagination: v

ex. from Asterix, vol. 10

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Visual Object Schemata in Long-Term Memory

o

o

How do we recognise objects as seen from various perspectives? Why is it so difficult to identify the object above?

G. Strube, Univ. Freiburg

We have acquired canonical object representations (Marr, 1982), or object parts and their structure (Biederman, 1987). o In the picture on the left, the most important feature of the mixer (height) is occluded . o

Fig. from Biederman (1987) Introduction to Cognitive Science

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Analogical Representations o

analogical (as opposed to propositional) direct representation of continuously varying features almost exclusively discussed for the visuo-spatial domain v analogical representations are often helpful, but not always so v v

o

Empirical evidence for analogical representation m

Dual Coding in verbal learning (Paivio, 1980) v v

m

Mnemotechnics Imageability; modality-specific markers (Engelkamp & Zimmer)

Mental Rotation (Shepard & Metzler, 1971) v v

see above: WM and its VSSP a visual WM does, however, not resolve the imagery debate: Are there analogical representations in LTM as well?

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Part 5: Words, Sentences, and Texts o o o o o o o

Language is specific for our species Language perception Syntax and Parsing Semantic Interpretation Text Understanding Not treated here: Language Production Visit the Psycholinguistics course (Hemforth & Konieczny) for more details on language.

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Language Ability is Specifically Human o

o o

o

Extended training of chimps has shown that they can master the systematic usage of up to several hundred symbols, but that apes are severely limited with respect to syntax. However, all human beings learn the language spoken by the people interacting with them without any special training. Natural language being species-specific, raises the hypothesis that all human languages must have some common characteristics, so-called linguistic universals. m Problem: There is no consensus on how a ‘universal grammar’ should be defined (for an offer, cf. Chomsky, 1981; 1995). Language is a product of human cognition (both were developed together during phylogenesis). m Linguistic and other cognitive abilities also co-develop in children.

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Language-Specific Knowledge o

We produce and understand utterances on the basis of regularities that are described by linguistics. v

o

NB: Grammar (i.e., Lexicon + Morphology + Syntax) is descriptive, not normative (not a school grammar book, which is prescriptive)

This basis is Language-Specific Knowledge (LSK). LSK: grammatic (lexical, morphological, syntactic) knowledge, semantic knowledge (re. scope of negation, re. deixis, etc.) and pragmatic knowledge (e.g., cooperative principles like the maxims of Grice, 1975) v In addition, we use our common and domain-specific knowledge (background knowledge, world knowledge) for language processing. v

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The Speech Signal o

Characteristics fundamental frequency and intonation contour m Formants (vowels) and transients (consonants) m Pauses are no valid indications of word boundaries m

o

Pros & Cons Pro: undirected signal, well suited for mass communication m Con: signal difficult to analyse (no technique yet) m

G. Strube, Univ. Freiburg

Introduction to Cognitive Science

„What are you doing?“ (from: Goldstein, 1997) Introduction to Cognitive Science

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Understanding Utterances speech signal, or text phonemic or graphemic analysis lexical access (word identification) conceptual and backgrd. knowledge

parsing (syntactic analysis) semantic interpretation and inferences

knowledge representation G. Strube, Univ. Freiburg

Introduction to Cognitive Science

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The Mental Lexicon and Semantic Memory o

What’s in the mental lexicon? m m m m

o

What’s in semantic memory? m m m

o

word stems (lemmas) and morphological information (word forms) phonological and articulatory information syntaktic information (word class, argument structure) pointer to concepts in semantic memory (‘meaning’) taxonomic information (sub- & superordinate concepts) charakteristic and defining features schemas for objects and events

Priming m

Listening or reading activate the appropriate entries of the mental lexicon, in turn activation spreads to related word and concepts.

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Why is Parsing Difficult At All? o

Almost all utterances are ambiguous (at least locally) m m

o

Almost all words have a host of meanings m

o

local ambiguity: „Daß der Doktorand der Professorin drei Tage Urlaub beantragt/abgetrotzt hat, erstaunt mich wirklich.“ global ambiguity: „Müllers sahen die Störche auf ihrem Flug nach Afrika.“ „mit der Lufthansa ... nach New York fliegen“, vs. „mit dem Glückslos..., mit der besten Freundin..., mit der besten Laune..., mit dem Flugsimulator..., mit der Videokamera..., mit der 747... fliegen.“

Syntactic analysis (parsing) must resolve ambiguities m

If parsing fails completely, we get a ‘garden-path effect’, as with the classic ex. “The horse raced past the barn fell.” (T. Bever, 1970)

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An Example of Parsing Principles: „Right Association“ (late closure) o

Prinzip LC (Late Closure):

S’

Comp Try to link a new unit (word, constituent) to the unit that was constructed last.

S NP

VP ? V

o

Example: “Since Jerry jogs a mile seems a short distance to him.”

G. Strube, Univ. Freiburg

Introduction to Cognitive Science

Since

Jerry jogs

Introduction to Cognitive Science

NP Det

N

a

mile 70

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Reanalysis in Parsing o

Based on a vast body of empirical research (mostly eye movement studies) we assume: A first analysis is constructed on the basis of purely lexical and syntactic knowledge. m If problems arise in later parsing, or during semantic interpretation (e.g., a contradiction), a (partial) structural reanalysis is executed. m Parsing is an automatic process (try to NOT understand what I tell you!). Normally, re-analysis is so fast and efficient that listeners/readers are not even aware of it. m

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How to Measure Re-Analysis m

o

In the eye-movement lab, you can observe: m m

o

Text is presented on a computer screen. Ss read the text; their eye movements are registered on-line. At the end of ambiguous regions, i.e., when disambiguation happens, significantly elevated reading times (i.e., fixations) are observed. No elevated reading times occur during ambiguous regions.

„The boy observes the girl with binoculars.“ m m m

semantically unambiguous: PP = Instrument (a) „The boy observes the river with binoculars.“ semantically unambiguous : PP = Attribute (b) „The boy observes the girl with the rucksack.“ We observe ca. 100 ms longer reading times for the PP in (b) as compared to (a). We conclude that PP = instrument is the preferred reading (i.e., the reading constructed during first analysis, based on non-semantic information).

G. Strube, Univ. Freiburg

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Effects of Reanalysis in ERP Event-related potentials m

m m m m

m

Presentation is acoustic or visualstationary (word by word, no eye movements), ext. paced 300-600 ms Pro: Online-Technique, EEG during processing Pro: excellent temporal resolution (ca. 10-20 msec) Con: bad spatial resolution (in spite of up to 128 electrodes) Con: 30-50 trials with the same sentence (!) are needed for averaging the signal Problem: Is the maximum, or the beginning of the effect the time when re-analysis happens?

G. Strube, Univ. Freiburg

„The pizza was too hot to ...“

N 400, Abb. aus Kutas et al., 1984 Introduction to Cognitive Science

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Semantic Interpretation Deals With: o

Resolving reference v v

o

Assigning scope v v

o

Negation: „hat nicht gehört, daß“ v. „hat gehört, daß nicht“ Quantors: „das Etikett auf den Flaschen gefällt mir“

De-indexing v v

o

defining reference objects ( „Martin Schmitt“, „unser Martin“) resolving anaphoric references („er“, „der Weltmeister“)

deictic expressions: “hier”, “morgen”, “du” distinguishing between utterance situation and told-about situation

Translation into knowledge representation v

Trade-off between expressiveness and tractability (re. inferences)

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stabbed + weapon = knife

(Garrod, O‘Brian, Morris & Rayner, 1990) o

gaze duration (ms)

Material: All the mugger wanted was to steal the woman‘s money. But when she screamed, he {stabbed|assaulted} her with his {knife|weapon} in an attempt to quiet her down. He looked to see if anyone had seen him. He threw {the|a} knife into the bushes, took her money, and ran away.

o

Experimental factors restrictivity (stabbed v. assaulted) explicit v. implicit reference (knife v. weapon) m definitness (a v. the) m m

o o

Dependent variable: reading time knife Result: Immediate interpretation of anaphoric reference

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Analogue Mental Models in Story Comprehension ... He walked from the laboratory to the washroom. m Then the target sentence: (goal room, named): He thought that the toilet in the wash room still looked like an awful mess m other conditions: path room, source room, other m

o o

Exp. by Glenberg et al. (1987) Ss learn the floor plan by heart. They then read a story.

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What Do We Infer During Text Comprehension? o

Spontaneously, we draw only inferences that help to keep the story coherent (“bridging inferences”). v

o

Online-Methods: Priming, reading times, answer times to questions

If we read expository text for knowledge acquisition, it is advisable to draw as many inferences as possible (esp. causal inferences serving to explain: elaboration). useful: “advance organizers” and story grammars, etc. v useful: domain-specific knowledge (expertise) v

o

Inferences may also be based on analogical representations (e.g., distances in spatial mental models).

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Immediacy and Incrementality of Comprehension o

o o o

Put the saltshaker on the envelope in the bowl. alternatively: Put the saltshaker that‘s on the envelope in the bowl. G. Strube, Univ. Freiburg

Introduction to Cognitive Science

o

Spivey-Knowlton, Tanenhaus, Eberhard & Sedivy (CogSci 96) 6 naive Ss each listen to commands as in the fig.; they sit in front of the pictured arrangement of objects eye movements are measured via a head-mounted camera At saltshaker the gaze moves immediately to the object (1) in the ambiguous case (2 envelopes, reduced relative clause) , the other possible object is fixated as well at the word envelope A very nice demonstration of the incrementality of comprehension

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Part 6: Acquiring & Using Knowledge o

Problem solving, based on general principles (heuristic search) v memory & domain-specific knowledge (rules, schemas, cases) v analogy v

o

Learning & Knowledge Acquisition v v

o

associative learning & skill development knowledge acquisition & expertise

Reasoning: Deduction and probabilistic inference flawed in knowledge-lean domains guided by domain-specific knowledge v bounded rationality v v

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Problem Solving As Heuristic Search (Newell & Simon, 1972) o

Heuristic search in problem space v v

o

Systematic backtracking v. human capacity Why can Tic-Tac-Toe be interesting at all?

Means-end analysis actual state & goal state, operator selection, heuristic function v Example: Hobbits & Orcs v The authoritative implementation: GPS (General Problem Solver) v

o

Limits of application confined to knowledge-lean problems v whenever possible, people make use of (LTM) knowledge v

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Experience-Based Problem Solving o

frequently, similar problems are encountered m m m

o

It helps to remember how similar problems were successfully dealt with in the past – plus some adjustment to the current situation Case-based reasoning: find most similar case, adapt solution Schema-based reasoning: from several similar cases, a general abstract schema is generated

mixed forms of reasoning m

Knowledge acquisition works best from a mixture of general (rule-based) knowledge and individual examples (cases)

m

General, fundamental knowledge (model-based reasoning) helps whenever heuristic rules fail

©

G. Strube, Univ. Freiburg

e.g., in American business schools

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Case-Based Reasoning (CBR) o

Makes use of experience m m

m

m m

G. Strube, Univ. Freiburg

Introduction to Cognitive Science

Case-base is constructed from real cases Given a new problem, the most similar case is retrieved Its solution can be reused as is, or must be adapted by means of general knowledge. The new case is then stored in the case-base: learning Fig. after Aamodt & Plaza, 1994

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Reasoning by Analogy o

We often draw analogies e.g., whenever we understand metaphors m but in problem solving, it may not be easy, and need help (Gick & Holyoak, 1980) m

o

Ex.: solar system ð atom rotation sun

planet

Mechanism Superficial similarity is important, but not sufficient. m Structural similarities are decisive and must be respected (structure-mapping: Falkenhainer, Forbus & Gentner, 1989).

gravitation

m

G. Strube, Univ. Freiburg

rotation core

elektric attraction electron

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How Do We Acquire Knowledge? Varieties of Human Learning o

We command many ways of learning, among others: m m m m m m m m

conditioning (it happens all the time, but goes mostly unnoticed), it‘s a kind of associative learning learning from exploration imitation – it is one of the most important and powerful ways, observable even in newborns learning from observation (e.g., intentional imitation, or learning from the observation of others without imitating them) explicit, intentional learning from other people‘s instructions (e.g., teachers), or from textbooks incidental learning (e.g., news from gossip) learning through elaboration, i.e., linking new information with knowledge (devising explanations, applying knowledge) learning in cooperation (in problem-solving, teaching, etc.)

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Associative Learning

(remember: the middle layer of action control) o

o

o

Associative learning is one of the evolutionary oldest cognitive processes Learning results in stable associations between actions and effects (or stimuli and consequent stimuli) Necessary preconditions: m m

ability for categorial perception ability to evaluate stimuli

G. Strube, Univ. Freiburg

Classical conditioning m

m

o

Stimuli close in time are associated, if the later S is biologically important. ex.: Pawlow‘s dogs, Little Albert (Watson & Rayner, 1920)

Operant conditioning m

m

Consequences of spontaneous behaviour serve to shape behaviour ex.: pigeons and rats in a Skinner box

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Developing Skills o

Motor training is an important learning mechanism. m

o

Training skilled movements profits from cognitive control: v Training without knowledge of results is doomed. v Imitation, and also mental simulation (execution in the imagination) are effective means of motor learning.

Associative learning and sensorimotor training m m m

Both lead to action schemas for rapid, skilled action. Execution of action schemas does not need cognitive control. During learning, both profit from intentional exercise, during execution, they are in potential conflict with deliberation.

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Explicit Knowledge Acquisition o

What kind of learning is it? m m

o

not conditioning, nor imitation, nor motor training, but: it‘s learning facts & rules through instruction in natural language (spoken by a teacher, or by reading a textbook)

From declarative knowledge to procedures m m m m

Initially, facts & rules are stored in LTM (declarative memory) If that knowledge is applied, a process of ‘knowledge compilation’ (Anderson, 1982) or ‘chunking’ (Newell et al., ca. 1980) happens: A package of rules that have often been executed together is compiled into one unit of procedural knowledge, an action schema. Extended further training may lead to further, smaller improvements, fine-tuning (Anderson: ‘honing’) the schema.

G. Strube, Univ. Freiburg

Kognitive Entwicklung

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What is Expertise? o

Everyone is an expert in at least a few areas m m

o

e.g., pop groups, tennis... and in one‘s profession It needs 8-10 years to become a true expert in any field

The hallmark of expertise m m m m m m

Experts know more (in terms of declarative AND procedural knowl.) They need not think about routine problems, they just solve them (Patel & Groen, 1986) They classify problems according to solution principles, not according to superficial similarity (Chi et al., 1983) In problem solving, they know well whether they are near the solution or not (metacognition: Gruber & Strube, 1988) General intelligence can only partly compensate for expertise Bottom line: Experts just outperform non-experts

G. Strube, Univ. Freiburg

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Chess Experts o

The task devised by de Groot (ca. 1940): Memory for Chess Positions m

m m m m m

Ss are shown a chessboard with an actual midgame position for 20 sec, then are set before an empty board, trying to reconstruct the position from memory. Masters remember 15-25 pieces, novizen 4-5 pieces correctly. Expertise is no help for random chess positions, however (Chase & Simon, 1973) Chess experts analyse positions in ‘chunks’ of 3-6 pieces In the literature, various researchers estimate that chess experts have stored 30,000 to 70,000 of those chunks in LTM Chess chunks are like concepts!

G. Strube, Univ. Freiburg

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Uncommon Expertise: Experts at the Races (Ceci & Liker, 1986) o

Performance and experience All Ss had min. 10 yrs. of experience (at least twice a week at the horse races). However: only some were true experts! v Prediction of race outcomes split the group in two non-overlapping samples. v

o

Expertise and general intelligence v v

o

Ss varied according to age, background, IQ, and many other factors Analysis of regression showed: No influence of these factors

So what is it that makes an expert? not declarative knowledge (all Ss know almost everything about the horses, past races, etc.) v Only the experts were able to integrate all this factual knowledge into mental simulations of future races v

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Logical Thinking: WASON’s Task

A

7

G

(Wason, 1964)

4

Rule: “If the front shows a vowel, the back side displays an even number” Problem: In order to check whether the above rule is valid, which cards must you turn around (to inspect the other side of these cards)?

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Results & Theories For Wason’s Task o

Summary of many studies (Oaksford & Chater, 1994): m m m m

o

89% of all Ss turn the card with the vowel (A): Right! 62% of all Ss turn the card with the even number (4): Wrong! 25% of all Ss turn the card with the odd number (7): Right! 16% of all Ss turn even the card with the consonant (G): Wrong! (Das sind meistens die Leute, die alle Karten umdrehen.)

Attempts to explain these results m m m

Misunderstanding of implication: ‘if – then’ is understood by many Ss as a biconditional: ‘iff – then’ Some Ss may not know (or not believe in) modus tollens Ss turn cards not according to logic, but to statistical informativity (Oaksford & Chater) in order to check whether there is at least some relation between letters and numbers.

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Enriching Wason’s Task by Everyday Knowledge Helps! o

Letters (Legrenzi, Legrenzi & Johnson-Laird, 1972) “Letters with open envelopes need less expensive stamps.” v Result: Context was helpful only for people with specific experience v

o

Euro-Cheques v

o

“Schecks über 400 Mark sind auf der Rückseite vom Abteilungsleiter abzuzeichnen”

Cheating Detection (Gigerenzer & Hug, 1990) The Union perspective: “Have all who didn’t miss a day of work got the bonus?” v The Company perspective: “Have all who got the bonus not missed a single day of work?” v

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Probabilistic Reasoning m

Monty Hall again: You are the candidate, facing 3 doors. The prize is behind one (and only one) of these 3 doors. v You choose the door that you believe to hide the prize. v Monty Hall (who knows where the prize is) now opens not the door you chose, but another one – and shows you that the prize is not there. He offers you to switch your choice from the door you have chosen to the other, still closed door, for a payment of ten dollars. v v

m

Would it be wise to accept Monty’s offer?

m

Explaining the problem with big absolute numbers helps (usually):

v

Yes, it would in fact double your chances.

If there are 1000 doors, your probability of succes is: 0.001 Now imagine Monty opens 998 of the other 999 doors. v Is it more likely that the prize is behind the 999th door, or behind the one you originally chose with 0.001 probability of success? v The point is that you can now make use of Monty’s knowledge! v v

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Kontexteinflüsse beim probabilistischen Schließen o

o

„Linda is 31 years old, single, outspoken and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.“

o

Probability versus representativity: „Linda“

Please rank the following statements by their probability: m Linda is a bank teller. (rank 6.2) m [ ... ... ] m

Linda is a bank teller and is active in the feminist movement. (rank 4.1)

G. Strube, Univ. Freiburg

Introduction to Cognitive Science

Bounded rationality o

m

o

95

(Simon, 1957)

Is our thinking erroneous? (Kahneman & Tversky, seit ca. 1970) m

o

Exp. v. Tversky & Kahneman (1983) m Result: More than 80 % of Ss rank Linda is a bank teller and is active in the feminist movement as being more probable than Linda is a bank teller alone. m According to probability calculus, p(a ∧ b) is always equal, or less than either p(a) or p(b). m It follows that most people are wrong with respect to probability. m

Numerous demonstraqtions of fallible heuristics, e.g., representativeness, or availability (How many English words have an R in third position? More or less than those words starting with R?) HOWEVER: We have surprising success in everyday life!

or: is it ‘reasoning the fast and frugal way’? (Gigerenzer & Goldstein, 1995) m m m

Their task: Guess which one of two cities is bigger. Model: Take the best / the last Result: The Take-the-best model is not only faster, but in the ecologically most realistic range ( 30-40% of features unknown) even better than a full regression model.

G. Strube, Univ. Freiburg

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Part 7: Applied Cognitive Science o o o o o o

Application fields for CogSci Focusing on HCI Basic and cognitive ergonomics Naming things: commands, menus, queries User dialogues A success story: Project Ernestine

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CogSci Application Areas o o o o o o

Education & training, using the ‘new media’ Design & usability testing for software, computers, and information systems Web design, organising content (ontologies) Knowledge management in large companies, institutions, and research projects New forms of working together (CSCW, etc.) Neuropsychological diagnosis & rehabilitation

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HCI: Human-Computer Interaction o

o o

‚Hit any key to continue‘ G. Strube, Univ. Freiburg

HCI is the most important application area for cognitive science these days. In the US, CogSci majors sell like crazy! (Because, in the internet age, the competition is just one mouse click away.)

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Components of Human-Computer Interaction screen design O presentation

output

system

ergonomics observation

user

core

task performance

I

articulation

input

dialogue design

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Simple Ergonomics: The Visual System o o o

o

Fonts have been designed for specific applications in mind. Here are some examples: Don‘t use a fancy font for information. Use a clear font instead. Be careful when you use a font that looks good on white paper in your Powerpoint presentations on dark backgrounds. Only clear bold fonts perform well!

o

o

Switching to a brightly coloured or fancy background, looks just awful and will cause eyestrain to any user.

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Cognitive Ergonomics: The Visual System o

Attention has low capacity è Group important information together!

o

The visual periphery is practically limited to noting change. Changes are likely to start an orienting reflex: The user‘s gaze moves to the place where change occurred. è Mark important information by blinking, è but don‘t use blinking otherwise, because it will distract users from what they are about to do!

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Planning & Executing Movements o o

o o

o

Fitts‘ Law (from the 1950s) states that t = a + b log2 (2d / w). In words, it takes longer to move your finger (or the mouse) to a target that is in greater distance d, and to a smaller target (w = width). On a Windows PC, the main menu is in the second line of a window, a small target indeed. On a Mac, the menu is in the top line, only a little more distant, but (if the window fills the screen) almost infinitely wide (because the mouse will stop at the top of the screen). Hence, moving to the main menu is often easier on the Mac.

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Memory For Locations o

Being animals, we have a good memory for places. Users expect things to be in place (and remain there). m Consistency means here: Equal things in the same place. m e.g., status line, buttons, icons, menu items m The consequence is an operating-system style. m

o

A major flaw: self-adapting menus u

e.g., in Windows 2000 (thank God you can block them!)

frequently chosen items percolate towards the top m Users make errors and get furious m

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User Dialogues o

Amazingly often, user dialogues m m

are not written clearly, and/or violate the simplest rules. u

o

like, in the ex. above: Give users a choice if you ask them.

At least some of these gross errors in design are due to bad design of programming languages m m

e.g., Visual Basic‘s MsgBox function: MsgBox(msg [, [type][, title] ] ), with type = sum of the following: Display question mark = 32, display OK button only = 0, display OK and cancel buttons = 1, display Yes and No button = 4, etc.

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Using Information Systems o

2 alternative ways for users of information systems m

Browsing, „Surfing“

m

Search, using keyword combinations

v

v

o

typical for web pages & hypermedia typical for databases and search machines of the WWW

2 subtasks information systems have to be good at m

Finding the relevant information v

Recall: How much of the relevant information was found? u

v

m

Easy to measure in databases, impossible with the web. How much potentially relevant information does the WWW contain?

Precision: How much of what has been found is relevant?

Presenting the information to the user

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In the beginning was the Word... o

The ‘right’ word is essential in many HCI tasks: m

Keywords & search terms for information systems v

esp. terminological hierarchies, so-called ‘ontologies’

Command languages & menu items m Abbreviations & mnemonics m Combinations of words m Query languages (e.g., SQL) m Syntax of programming languages m

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Commands o

How do you leave an application program? m

o

Quit, End, Exit, Resume, Close, Shutdown, Finish, New, Save (& quit), Bye, Terminate, Ctrl-C, Alt-Ctrl-X, Esc-Ctrl-Q, Alt-D-B

People typically generate a wealth of different terms (Carroll, 1985) m but there is only 7-18% probability that any two users agree m a single user employs up to 15 strategies in order to generate file names m

Wilson et al. (1983): Their participants generated spontaneously 1080 different (!) commands for an email system.

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Queries ex. from Landauer, 1994, p. 146: o

ABS = (information AND retrieval AND NOT about [range = 3]) AND (text OR data) AND (NOT ((quantitative AND data) OR (numbers OR numerical OR statistics) OR information AND NOT text))

o

This query is intended to find abstracts about information retrieval, rejecting articles that only discuss databases for numbers. There are two fatal errors in this query. It wouldn‘t parse – that is, the computer would reject it because the syntax prevents unambiguous interpretation – and if the syntax were corrected, it would return nothing because the logic is faulty.

o

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Query Languages The same example, now formatted more user-friendly, but still not a valid query: ABS = (information AND retrieval AND NOT about [range = 3]) AND (text OR data)

o

AND (NOT ( (quantitative AND data) OR (numbers OR numerical OR statistics) OR information AND NOT text)) G. Strube, Univ. Freiburg

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SQL v. Common Sense

SQL = Structured Query Language (1976) Experiment by Greene, Gomez & Devlin, 1986: o

o

Housewives (high-school diploma, well-off neighbourhood) got a half day‘s introduction to SQL. They then had to solve problems like the following: m m

o o

„Find all the employees who either work in the toy department or are managed by Grant, and also come from the city of London.“ Solution: SELECT name FROM employee WHERE (department = “toy“ OR manager = “grant“) AND city = “london“

Only half of these problems were solved. Distribution of scores over subjects was bimodal. Scores were highly correlated with a test of logic.

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QBE: Query-by-example (Zloof, 1975) o

Method Instead of formulating a query, the users enter what they want to find into a form, specifying the fields that must contain certain pre-specified information. m Example: m

EMP m

NAME ?

DEPTNO 50

SALARY

This is equivalent to the SQL query: SELECT name FROM employee WHERE deptno = 50

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SQL versus QBE

(Greenblatt & Waxman, 1978) o

o o o o o o o

Results of 8 Ss (QBE) and 17 Ss (SQL):

Time for training mean time for testing mean pct. of correct answers mean time per query mean subjective certainty**

QBE 1h:35min 23,3 min 75,2% 0,9 min 1,6

SQL 1h:40min 53,9 min 72,8% 2,5 min * 1,9 *

* = statistically significant (p < .001) ** 1 = absolutely sure, 5 = certainly wrong

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Hints For Software Designers o

Think like a user m

o

Try it out m

o

Get some person who is highly similar to the end user to use your interface with minimal support.

Include users into the development process m

o

When building an interface, try to see it from the perspective of the end user and imagine possibly critical situations.

Often, users have good hunches about where possible problems lie hidden.

Opt for iterative development m

Design prototypes that you can get rid of without remorse or grief.

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The Role of Cognitive Science o

Cognitive Walkthroughs m

o

Step-by-step working through a design (i.e., before it has actually been implemented) will spot gross errors.

Task Analysis & Usability Testing Systems like GOMS (Card, Moran & Newell, 1986) are good for modeling routine tasks, m Systems like ACT-R/PM (Anderson & Lebière, 1998) are good for modeling learning as well. m Usability testing with real users is a must in later stages of development. m

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An Example: Money Saved by Usability Testing o

Using a task modeling technique, CogSci researchers computed how long it would take operators in a big call center to handle a call with a new workstation at Nynex.

o

The new workstation, of course, would be much faster and was of superior ergonomic design (screen, keyboard, layout in general).

o

CogSci modeling, however, discovered that for certain calls, due to a slight change in workflow, actually more time per call would be needed with the new machine, amounting to a total cost of $ 2.4 million per year.

o

Extensive testing (3 months later, when the new machine was ready) confirmed these predictions.

o

CogSci methods had saved millions for the company. ‘Project ERNESTINE’: Gray, John & Atwood (1993)

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Project Ernestine: Analysis The new workstation was in many ways ergonomically better designed. It required, e.g., less keypresses. However, these savings were not in the critical path, i.e., they did not determine the overall time per call. o A single keystroke, however, was added to the critical path, requiring one cognitive unit (select key) plus 3 motor ones (move to key, press it, move back). This added an overall 0.8 sec per call. o These 0.8 sec summed up to $2.4 million per year. o

Fig. from Gray et al., 1993

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Literaturempfehlungen Zum Nachschlagen:

Wörterbuch der Kognitionswissenschaft, hg. von G. Strube, B. Becker, C. Freksa, U. Hahn, K. Opwis & G. Palm. Klett-Cotta, 1996. o The MIT encyclopedia of the cognitive sciences, ed. by R. A. Wilson & F. C. Keil. Cambridge, MA: MIT Press, 1999. (Gesamtanlage etwas problematisch) o

Zur vertiefenden Einführung:

J. R. Anderson, Kognitive Psychologie. Spektrum, 21996. S. Pinker, Der Sprachinstinkt. München: Kindler, 1996. o P. Thagard, Mind. MIT Press, 1997. (dt. Klett-Cotta, 1999 schlecht übersetzt) o o

ausführlicher (alle Kapitel stets einzeln lesbar):

Foundations of cognitive science, ed. M. I. Posner. MIT Press, 1989. An invitation to cognitive science (3 vols.), ed. D. N. Osherson. MIT Press, 21995. (+ vol. 4, 1999) o The cognitive neurosciences, ed. M. S. Gazzaniga. MIT Press, 22000. o o

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