introduction to cognitive science (lecture 1)...introductioncognitive modelingselection of problems...

Introduction Cognitive Modeling Selection of Problems

Introduction to Cognitive Science(Lecture 1)

Marco Ragni

Albert-Ludwigs-Universitt FreiburgFoundations of AI

[email protected]

11. September 2015

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What is the goal of Cognitive Science?

▸ Ultimate goal is to understand the humanmind by

▸ using any scientific method(→ Multidisciplinary!)

▸ replicating and predictingbehavioral/neural regularities

▸ using a neuro-cognitive computationalmodel.

▸ Of course it covers question like: Is there free will? What is themind-brain gap? What is and can we improve intelligence? How dowe learn and why do we forget? How do we perceive the world? . . .

Picture from National Institute of Health commons.wikimedia.org/wiki/File%3AHuman_brain_female_side_view.png

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commons.wikimedia.org/wiki/File%3AHuman_brain_female_side_view.png

What is the goal of Cognitive Science?

▸ Ultimate goal is to understand the humanmind by

▸ using any scientific method(→ Multidisciplinary!)

▸ replicating and predictingbehavioral/neural regularities

▸ using a neuro-cognitive computationalmodel.

▸ Of course it covers question like: Is there free will? What is themind-brain gap? What is and can we improve intelligence? How dowe learn and why do we forget? How do we perceive the world? . . .

Picture from National Institute of Health commons.wikimedia.org/wiki/File%3AHuman_brain_female_side_view.png

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commons.wikimedia.org/wiki/File%3AHuman_brain_female_side_view.png

It is not Artificial Intelligence

▸ AI aims at many human abilities

▸ with impressive results, Deep Bluebeating chess champion, Watsonbeating Jeopardy-Winners,Self-Driving Cars and so on ...

▸ However, most systems are domain-dependend, have noinsights, no consciousness, cannot decide for themselves, andso on - they are not human-like!

Picture by Clockready (Own work) [CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0) or GFDL(http://www.gnu.org/copyleft/fdl.html)], via Wikimedia Commons

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It is not Artificial Intelligence

▸ AI aims at many human abilities

▸ with impressive results, Deep Bluebeating chess champion, Watsonbeating Jeopardy-Winners,Self-Driving Cars and so on ...

▸ However, most systems are domain-dependend, have noinsights, no consciousness, cannot decide for themselves, andso on - they are not human-like!

Picture by Clockready (Own work) [CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0) or GFDL(http://www.gnu.org/copyleft/fdl.html)], via Wikimedia Commons

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A simple problem

▸ Ann is blood-related to Bella.

▸ Bella is blood-related to Charly.

▸ What follows?

▸ How have you solved this problem?▸ What have you used?

▸ Certainly your brain!▸ Language (to understand)▸ Visual system (to read)▸ Reasoning abilities (to infer)▸ Working Memory (to represent)▸ Attention (to focus)▸ Speech/Motor system (to answer)

▸ How have you represented what?

▸ Give an algorithm accurately reflecting mental processes

▸ How can you learn how humans solve such problems?

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A simple problem

▸ Ann is blood-related to Bella.

▸ Bella is blood-related to Charly.

▸ What follows?

▸ How have you solved this problem?▸ What have you used?

▸ Certainly your brain!▸ Language (to understand)▸ Visual system (to read)▸ Reasoning abilities (to infer)▸ Working Memory (to represent)▸ Attention (to focus)▸ Speech/Motor system (to answer)

▸ How have you represented what?

▸ Give an algorithm accurately reflecting mental processes

▸ How can you learn how humans solve such problems?

4 / 22

What do we know?

▸ We may use our brain (sorry Aristotle) ...▸ Neural processes compute answers

▸ We may use symbolic representations

▸ We may use knowledge (insights!)

These distinctions are called Marrs Level or the Trilevel Hypothesis:

▸ The physical or biological level

▸ The symbolic or syntactic level

▸ The knowledge or semantic level

Conclusio

▸ Cognitive models need to capture representations on all levels!

▸ (Beyond behaviorism → Skinner): Not the world and not thestimuli are relevant but internal representations!

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How can we learn about behavioral regularities?

▸ We are interested in behavioral regularity

▸ learning about it is limited only by the imagination ofexperimenter, e.g.,

▸ Relative difficulty (error rates, latencies, . . . )▸ Eye-tracking▸ Brain signals: EEG, fMRI▸ Behavioral changes due to brain damage▸ Transcranial Magnet Stimulation (TMS)▸ . . .

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How can we model?

VisualBuffer

ManualBuffer

ManualcModuleBA:c2,c4

ImaginalcBuffer

ImaginalcModuleBA:c7,c39,c40

RetrievalcBuffer

DeclarativecModuleBA:c45,c46

GoalcBuffer

ccccccGoalcModuleBA:c24,c32

ProceduralcModuleVisual

ModuleBA:c37

Visual-locationcBuffer

▸ Use a cognitive architecture:ACT-R (act-r.psy.cmu.edu)or Nengo (www.nengo.ca)

▸ ACT-R uses productionrules: preconditions, actions

▸ Modular structure

▸ Symbolic parts

▸ Subsymbolic partse.g., memory decay

▸ Mind-brain mappinghypothesis

▸ Computes error rates, response times, and brain activationsfor an implemented model

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act-r.psy.cmu.eduwww.nengo.ca

Is our representation unique?

▸ What we see depends on our knowledge and our internalrepresentation

▸ Say ten times: A rabbit watches a couple kissing in a window

▸ What have you seen?

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Is our reasoning process only the application of classicallogic?

▸ There are four cards: Each has a letter printed on one sideand a number on the other side

A 7 2 K

▸ The following rule is given: “‘If there is a vowel on one side,then there is an even number on the other side.”

▸ Which cards only must be turned to test whether theconditional holds.

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A 7 2 K

▸ The following rule is given: “‘If there is a vowel on one side,then there is an even number on the other side.”

▸ Which cards only must be turned to test whether theconditional holds.

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A 7 2 K

▸ The following rule is given: “If there is a vowel on one side,then there is an even number on the other side.”

Card % of Ss Correct ExplanationA 89% Correct An uneven nr on other side falsifies2 62% Incorrect No relevant information can be found7 25% Correct If other side has a vowel it falsifiesK 16% Incorrect Tasks says nothing about consonants

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A 7 2 K

▸ The following rule is given: “If there is a vowel on one side,then there is an even number on the other side.”

Card % of Ss Correct ExplanationA 89% Correct An uneven nr on other side falsifies2 62% Incorrect No relevant information can be found7 25% Correct If other side has a vowel it falsifiesK 16% Incorrect Tasks says nothing about consonants

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▸ If a person is drinking beer, then the person must be over 21.How to test whether somebody is abiding by this rule?

Drinking beer

Drinking Coke

16 years of age

22 years of age

▸ 74% answered correctly

▸ Isomorphic problem, but the content made the difference!

▸ Problem for a pure syntactic rule-based theory!

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What kind of cognitive theories exist?

▸ Difference between reconstructive and generative aspects (cp.Lüer & Spada, 1990):

▸ reconstructive: Conceptualising structures and processesthat underly mental activity

▸ generative: The execution of a model not only describespsychological phenomena but also generates them. Thisallows for a comparison of model predictions withempirical data.

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What does a cognitive model?

Experiment:

Human

Agent

Environment

(Task-specific)

Action

Perception

Model:

Artificial

Agent

Environment

(Task-specific)

Action

Perception

Response Times

Eye

Movements

fMRI

...

PredictedCollected

Data:

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How can we evaluate cognitive theories?

Simon and Wallach (1999) require a generative theories to have:

▸ product correspondence: this requires that the cognitive modelshows a similar overall performance as human data

▸ correspondence of intermediate steps: this requires thatassumed processes and steps in the model parallels separablestages in human processing

▸ temporal correspondence: this requires that computationalprocess times (or assumed temporal costs) parallels reactionand answer times

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How can we evaluate cognitive theories?

▸ error correspondence: this requires that the same errorpatterns in the model emerge than in experimental data

▸ correspondence of context dependency: this is a comparablesensitivity to known external influences)

▸ learning correspondence: this requires a similar or identicallearning curve between the humans and the model

We could add:

▸ Level correspondence: explaining data on all levels

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What phases of cognitive modeling exist?

Four phases can be considered (e.g., Lewandowski & Farrell, 2011):

1. Task analysis:

▸ what knowledge is needed to solve a task?

▸ what are processes involved in generating the knowledgeto solve a task

▸ what are relevant structures an architecture used tospecify a model?

2. Empirical data

▸ Reconstruction of trace/statistical measure for oneparticipant

▸ Reconstruction of some statistical measure whichconsiders all participants

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What phases of cognitive modeling exist?

3. Model implementation

▸ Architecture selection (e.g. neural network, MPT)

▸ Process specification

▸ Parameter estimation (e.g. simulated annealing,maximum likelihood estimation)

4. Model validation

▸ Parameter uncertainty

▸ Model comparison

▸ Model interpretation

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Systems, Complexity, and Cognition

▸ To understand how humans think and reason (e.g., for AI)

▸ we need computational and cognitive models

▸ To build systems for human computer interaction

▸ e.g., digital assistants with help as needed

▸ What makes problems difficult for humans?

▸ Computational complexity characterizes problems

▸ . . . but cannot explain reasoning difficulty for humans

▸ Let us consider some examples

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Example 1: Class of Search Problems

Tower of London

Rush Hour

▸ Rearrangement of object(s) in a givenstate towards a goal state

▸ Examples:

▸ Tower of London / Tower of Hanoi

▸ Rush Hour (PSPACE-complete):rearrange cars on a parking lot

▸ Search problems: static, fully observable,known operators

▸ High-computational load: If search spaceis large human performance decreases

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Tower of London

Rush Hour

▸ Examples:

▸ Tower of London / Tower of Hanoi▸ Rush Hour (PSPACE-complete):

rearrange cars on a parking lot

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Tower of London

Rush Hour

▸ Examples:

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Tower of London

Rush Hour

▸ Examples:

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Example 2: Class of Insight Problems

1 2 3 4

5 6 7 8

▸ Fully observable, but

▸ . . . not solvable by search

▸ Operators must be identified

▸ Humans perform better onthis problem class thanstate-of-the-art techniques

▸ The human reasoningprocess is not understood ...→ Lecture 2

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1 2 3 4

5 6 7 8

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1 2 3 4

5 6 7 8

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Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:

A AA

A

west ofB B

B

C CC

C

east ofB B

B

B B

B

west ofD D

D

D DD

D

west ofE E

E

▸ Can the AC easily infer thatin every case

C CC

C

west ofE E

E

?

Finding: Most participants say yes . . .

▸ which is wrong! Why?

A AA

A

B B

B

C CC

C

E E

E

A AA

A

B B

B

B D DD

D

C CC

C

E E

E

A AA

A

B B

B

B D DD

D

E E

E

C CC

C

▸ Demands are different to construct these mental representations:Which model is preferred? Are others neglected?

▸ Can we explain human reasoning (and reasoning difficulty)?

⇒ Lecture 2

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A AA

A

west ofB B

B

C CC

C

east ofB B

B

B B

B

west ofD D

D

D DD

D

west ofE E

E

C CC

C

west ofE E

E

?

A AA

A

B B

B

C CC

C

E E

E

A AA

A

B B

B

B D DD

D

C CC

C

E E

E

A AA

A

B B

B

B D DD

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E E

E

C CC

C

⇒ Lecture 2

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A AA

A

west ofB B

B

C CC

C

east ofB B

B

B B

B

west ofD D

D

D DD

D

west ofE E

E

C CC

C

west ofE E

E

?

A AA

A

B B

B

C CC

C

E E

E

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A

B B

B

B D DD

D

C CC

C

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E

A AA

A

B B

B

B D DD

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E E

E

C CC

C

⇒ Lecture 2

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A AA

A

west ofB B

B

C CC

C

east ofB B

B

B B

B

west ofD D

D

D DD

D

west ofE E

E

C CC

C

west ofE E

E

?

A AA

A

B B

B

C CC

C

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E

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A

B B

B

B D DD

D

C CC

C

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A AA

A

B B

B

B D DD

D

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C CC

C

⇒ Lecture 2

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A AA

A

west ofB B

B

C CC

C

east ofB B

B

B B

B

west ofD D

D

D DD

D

west ofE E

E

C CC

C

west ofE E

E

?

A AA

A

B B

B

C CC

C

E E

E

A AA

A

B B

B

B D DD

D

C CC

C

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E

A AA

A

B B

B

B D DD

D

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C CC

C

⇒ Lecture 2

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A AA

A

west ofB B

B

C CC

C

east ofB B

B

B B

B

west ofD D

D

D DD

D

west ofE E

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C CC

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west ofE E

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?

A AA

A

B B

B

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B B

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C CC

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A AA

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B B

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⇒ Lecture 2

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A AA

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west ofB B

B

C CC

C

east ofB B

B

B B

B

west ofD D

D

D DD

D

west ofE E

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C CC

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?

A AA

A

B B

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A AA

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B B

B

B D DD

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C CC

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A AA

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B B

B

B D DD

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⇒ Lecture 2

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A AA

A

west ofB B

B

C CC

C

east ofB B

B

B B

B

west ofD D

D

D DD

D

west ofE E

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C CC

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A AA

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B B

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⇒ Lecture 2

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A AA

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west ofB B

B

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east ofB B

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B B

B

west ofD D

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west ofE E

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A AA

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B B

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⇒ Lecture 2

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A AA

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west ofB B

B

C CC

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east ofB B

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B B

B

west ofD D

D

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west ofE E

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A AA

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B B

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B B

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⇒ Lecture 2

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A AA

A

west ofB B

B

C CC

C

east ofB B

B

B B

B

west ofD D

D

D DD

D

west ofE E

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C CC

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west ofE E

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?

A AA

A

B B

B

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⇒ Lecture 2

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A AA

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west ofB B

B

C CC

C

east ofB B

B

B B

B

west ofD D

D

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D

west ofE E

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A AA

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B B

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⇒ Lecture 2

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A AA

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west ofB B

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C

east ofB B

B

B B

B

west ofD D

D

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D

west ofE E

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west ofE E

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A AA

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B B

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⇒ Lecture 2

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A AA

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west ofB B

B

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east ofB B

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west ofD D

D

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west ofE E

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A AA

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⇒ Lecture 2

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A AA

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west ofB B

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east ofB B

B

B B

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west ofD D

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west ofE E

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A AA

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⇒ Lecture 2

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A AA

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west ofB B

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east ofB B

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west ofD D

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A AA

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⇒ Lecture 2

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A AA

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west ofB B

B

C CC

C

east ofB B

B

B B

B

west ofD D

D

D DD

D

west ofE E

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C CC

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A AA

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B B

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A AA

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⇒ Lecture 2

21 / 22

Thank You for Your Attention!

I gratefully acknowledge researchfunds provided by the DFG:

▸ Heisenberg-Programm

▸ “Nonmonotonic logic”within the SPP “NewFrameworks of Rationality”

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IntroductionCognitive ModelingSelection of Problems

introduction to cognitive science (lecture 1)...introductioncognitive modelingselection of problems...

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