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Introduction Cognitive Modeling Selection of Problems
Introduction to Cognitive Science(Lecture 1)
Marco Ragni
Albert-Ludwigs-Universitt FreiburgFoundations of AI
11. September 2015
1 / 22
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Introduction Cognitive Modeling Selection of Problems
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
2 / 22
commons.wikimedia.org/wiki/File%3AHuman_brain_female_side_view.png
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Introduction Cognitive Modeling Selection of Problems
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
2 / 22
commons.wikimedia.org/wiki/File%3AHuman_brain_female_side_view.png
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Introduction Cognitive Modeling Selection of Problems
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
3 / 22
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Introduction Cognitive Modeling Selection of Problems
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
3 / 22
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Introduction Cognitive Modeling Selection of Problems
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
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Introduction Cognitive Modeling Selection of Problems
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
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Introduction Cognitive Modeling Selection of Problems
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!
5 / 22
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Introduction Cognitive Modeling Selection of Problems
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)▸ . . .
6 / 22
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Introduction Cognitive Modeling Selection of Problems
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
7 / 22
act-r.psy.cmu.eduwww.nengo.ca
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Introduction Cognitive Modeling Selection of Problems
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?
8 / 22
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Introduction Cognitive Modeling Selection of Problems
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?
8 / 22
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Introduction Cognitive Modeling Selection of Problems
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?
8 / 22
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Introduction Cognitive Modeling Selection of Problems
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.
9 / 22
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Introduction Cognitive Modeling Selection of Problems
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.
9 / 22
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Introduction Cognitive Modeling Selection of Problems
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.”
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
10 / 22
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Introduction Cognitive Modeling Selection of Problems
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.”
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
10 / 22
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Introduction Cognitive Modeling Selection of Problems
Is our reasoning process only the application of classicallogic?
▸ 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!
11 / 22
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Introduction Cognitive Modeling Selection of Problems
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.
12 / 22
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Introduction Cognitive Modeling Selection of Problems
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:
13 / 22
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Introduction Cognitive Modeling Selection of Problems
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
14 / 22
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Introduction Cognitive Modeling Selection of Problems
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
15 / 22
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Introduction Cognitive Modeling Selection of Problems
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
16 / 22
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Introduction Cognitive Modeling Selection of Problems
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
17 / 22
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Introduction Cognitive Modeling Selection of Problems
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
18 / 22
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Introduction Cognitive Modeling Selection of Problems
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
19 / 22
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Introduction Cognitive Modeling Selection of Problems
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
19 / 22
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Introduction Cognitive Modeling Selection of Problems
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
19 / 22
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Introduction Cognitive Modeling Selection of Problems
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
19 / 22
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Introduction Cognitive Modeling Selection of Problems
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
20 / 22
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Introduction Cognitive Modeling Selection of Problems
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
20 / 22
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Introduction Cognitive Modeling Selection of Problems
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
20 / 22
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Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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
21 / 22
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Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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
21 / 22
-
Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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
21 / 22
-
Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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
21 / 22
-
Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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
21 / 22
-
Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
D DD
D
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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
21 / 22
-
Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
D DD
D
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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
21 / 22
-
Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
D DD
D
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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
21 / 22
-
Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
D DD
D
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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
21 / 22
-
Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
D DD
D
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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
21 / 22
-
Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
D DD
D
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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
21 / 22
-
Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
D DD
D
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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
21 / 22
-
Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
D DD
D
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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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Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
D DD
D
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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
21 / 22
-
Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
D DD
D
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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
21 / 22
-
Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
D DD
D
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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
21 / 22
-
Introduction Cognitive Modeling Selection of Problems
Example 3: Class of Deductive Reasoning ProblemsAn air traffic controller knows:
A AA
A
west ofB B
B
B
C CC
C
east ofB B
B
B
B B
B
B
west ofD D
D
D
D DD
D
west ofE E
E
E
▸ Can the AC easily infer thatin every case
C CC
C
west ofE E
E
E
?
Finding: Most participants say yes . . .
▸ which is wrong! Why?
A AA
A
B B
B
B
C CC
C
D DD
D
E E
E
E
A AA
A
B B
B
B D DD
D
C CC
C
E E
E
E
A AA
A
B B
B
B D DD
D
E 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
21 / 22
-
Introduction Cognitive Modeling Selection of Problems
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