Lecture 19
Problems With Modern Research On
Cognition
Donald’s Observations About Cognitive Science
Based on the study of two kinds of minds
1) Literate young adults
2) Computer simulations
Depressing View of Human Abilities
People are bad statisticians
People are not logical
People are bad decision makers
…
The Standard Social Science Model
Content Free Models of Mind, e.g., Logic, Probability,
etc.
Information Processing Models of Cognition
The Problem Space Hypothesis
Rule-Based Models of Skill
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Lecture 19
Evolutionary Psychology
Discover & understand the design of the human mind
An approach to psychology
A way of thinking about psychology that can be applied to
any topic with in it
Mind is a collection of IP’s, designed by natural selection to
solve adaptive problems faced by our hunter-gatherer
ancestors
Levels of Explanation
Adaptive problem
Cognitive Program
Neuropsychological basis
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Lecture 19
Where Social and Cognitive Sciences
Went Astray
The Standard Social Science Model (SSSM)
All Specific Content Of Human Mind Originally Derives
From The "Outside"
Small Number Of General Purpose Mental Mechanisms
have no pre-existing content built-in to their
procedures
not designed to construct certain contents more
readily than others
have no features specialized for processing
particular kinds of content.
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Lecture 19
Evolutionary Psychology View
SSSM Is WRONG!!!!!
Human minds have a standard collection of reasoning and
regulatory circuits that are
Functionally specialized
Frequently, domain-specific
Modules that are analogous to organs
Design by evolution
Designed to solve problem faced by our hunter-gatherer
ancestors
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Lecture 19
Bask To Basics (C&T)
Principle 1.
Brain Is A Physical System That Can Be Described As
An Information Processing System
Designed To Generate Behavior Appropriate To
Environmental Circumstances
Must Understand the Structure of Environment
(Anderson, 1990)
Principle 2.
Brain Designed By Natural Selection To Solve Problems
Faced During Our Species' Evolutionary History
Disgust
Our Ability To Learn Math, Drive Cars, Use Computers
Is A Side-Effect Or By-Product Of Circuits That Were
Designed To Solve Adaptive Problems
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Lecture 19
Bask To Basics (C&T) (Cont.)
Principle 3.
Consciousness is just the tip of the iceberg; most of
what goes on in your mind is hidden from you.
Perception, Motor Control, Language Generation and
Comprehension Are Extremely Complex
Example: To see your mother walk, you employ
specialized circuits that:
(1) analyze the shape of objects
(2) detect the presence of motion
(3) detect the direction of motion
(4) judge distance
(5) analyze color
(6) identify an object as human
(7) recognize that the face you see is Mom's face
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Lecture 19
Bask To Basics (C&T) (Cont.)
***Principle 4.
Different Neural Circuits Are Specialized For Solving
Different Adaptive Problems
Have Specialized Neural Circuits Because The Same
Mechanism Is Rarely Capable Of Solving Different
Adaptive Problems
Mind Consist Of A Large Number Of Circuits That Are
Functionally Specialized.
vision
hearing
sexual attraction
choose nutritious food on the basis of taste and
smell
executive functions
language
reasoning
problem solving
Brain Is A Collection Of Specialized Modules
No Disagreements About
Vision, Hearing, Motor Control, …
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Lecture 19
But Language, Reasoning, Learning, …, ?
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Lecture 19
Principle 4 (Cont.) and 5
Reasoning Circuits And Learning Circuits Discussed Above
Have The Following Five Properties:
(1) Complexly Structured For Solving Specific Type Of
Adaptive Problem
(2) Reliably Develop In All Normal Human Beings,
(3) Develop Without Any Conscious Effort And In Absence
Of Any Formal Instruction
(4) Applied Without Any Conscious Awareness Of Their
Underlying Logic
(5) Distinct From More General Abilities To Process
Information Or Behave Intelligently
Principle 5
Our modern skulls house a stone age mind.
Computer age
Industrial revolution
Agricultural age
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Lecture 19
Examples of Empirical Results Supporting
Evolutionary Psychology
Facial Expressions
Language
Chomsky: Universal Grammer
“Poverty of the simulus”
Human languages have such a complex structure that
they cannot be learned just from the information
available to a child
Detecting Violations of Rules
Probabilistic Reasoning
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Lecture 19
Detecting Violations of Rules
Logic and Reasoning
General Content Free Mechanisms
People are very bad at …
Wason Selection Task
IF a person goes into Boston,
then that person takes the subway
Boston Arlington subway cab
If P, then Q
Test for P(Q?) and ~Q(~P?)
Huge Number of Other Examples
Social Exchange (Reciprocal Altruism)
Cheater detection
If you take benefit B,
then you must satisfy requirement R
Can detect violations of If-Then rules if task is cheater
detection
If you are drinking beer, you must be 21 or older
Beer Coke
21 or over
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younger that 21
Lecture 19
Probabilistic Reasoning
Company suspects 2% of its employees use illicit drugs.
Company institutes random drug tests
Drug test is 95% accurate; that is,
P[positive test| drug use] = .95
P[negative test| no drug use] = .95
Mary Jane is selected at random; her test is positive. What
is probability that Mary Jane uses illicit drugs?
Gerd Gigerenzer
Gigerenzer, G., Todd, P. M., & the ABC Group (1999).
Simple heuristics that make us smart. New York: Oxford
University Press.
Probabilities verses Frequencies
Fast and frugal heuristics fill part of our mind's "adaptive
toolbox" of decision strategies. Together, these
heuristics produce a rationality which is ecological,
rather than merely logical - decision making that is welladapted to specific environmental settings or domains
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Lecture 19
and specific classes of problems, rather than being
universally applicable to all situations and problems.
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Lecture 19
Mary Jane's Probability
Test Says:
"Positive"
"Negative"
Total
Truth
Clean
Drug User
Total
49
19
68
(5% of Col)
(95% of Col)
931
1
(95% of Col)
(5% of Col)
980
20
932
1000
(98% of Total) (2% of Total)
For 1000 employees there would be 68 "positive" test
results.
But 49 of these "positive" tests would be false alarms and
only 19 would be hits.
P[Mary Jane is drug user | "positive" test] = 19/68 = .28
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Lecture 19
If company fired all employees with "positive" test results, for
every 1000 employees they would fire 49 innocent people
and only 19 guilty people.
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