Transcript Reasoning and Rationality

Reasoning and Rationality
Emily Slusser
February 13th 2006
Charter, N. &Oaksford, M. (1999). Ten years of the rational analysis
of cognition. Trends in Cognitive Science, 3, 57-65.
Rational Analysis
 Style of explanation in cognitive sciences
(J.R. Anderson and Milson)

But what is rational analysis exactly?

How does it relate to other approaches in
cognitive sciences?

How does it apply in practice?
Mechanistic & Purposive Explanation
Mechanistic
Internal causal structure
Purposive
What problem does it solve?
What is its function?
Methodology
(Anderson, J.R.)
1)
Goals of the cognitive system
2)
Environment to which the system has adopted
(formal model)
3)
Computational Limitations (minimal assumptions)
4)
Derive optimal behavior function
5)
Empirical data to see if predictions are confirmed
6)
Iteration to refine the theory
Rational Analysis and
Evolutionary Psychology
 Adaptation arises through evolution
 Adaptive throughout evolutionary history but
counteradaptive in contemporary environment
 Need-probability
 Rarity assumption
 Wason card task -> enhanced reasoning ability
 social reasoning module
The Role of Optimality
 Some things to consider…

How to compute the optimal solution?

Is this analysis necessary?

Two or more ‘good’ but very different solutions
 Note of caution but nothing more…
Memory
1) Goals - Efficient retrieval of relevant information
2) Environment - Determines need-probability
3) Computational Limitations - Memory searched sequentially
4) Optimization - Memory system should stop retrieval when
pG<C
5) Data - Need probability is a decreasing power function of time
6) Iteration - Empirical basis of ‘environment’
Need-Probability & Power Functions
S availability of memory structure
p need probability
Hs history factor
a(Qs) context factor
Relationship between retention
interval and need-probability
yuck
Wason Card Selection Task
A
K
2
7
p
not p
q
not q
‘If there is an A on one side,
then there is a 2 on the other side’
If p, then q
Wason Card Selection Task
A
K
2
7
p
not p
q
not q
?
A
?
2
Wason Card Selection Task
Borrowed
Car
Did Not
Borrow Car
Empty Gas
Tank
Full Gas
Tank
‘If you borrow my car,
then you must fill up the gas tank’
If p, then q
Reasoning –
Optimal Data Selection (ODS)
1) Goals – Greatest expected informativeness (EIg) and independence of
antecedent (p) and consequent (q)
2) Environment – When P(p) and P(q) are low then EIg(q) > EIg (not q)
(rarity assumption)
3) Computational Limitations – Cost of examining data
(as little as possible is examined)
4) Optimization – EIg(p) > EIg(q) > EIg(not q) > EIg(not p)
5) Data – Performance approximates Baysian optimal data selection
6) Iteration – Performance will change if rarity assumption is violated
Optimal Data Selection
Expected information gain
Frequency of card selection
Human performance approximates
Baysian optimal data selection
Conclusions
Question: How do arbitrary mechanisms & arbitrary
performance limitations add up to a successful system?
Answer: Rational Analysis

Identifies specific mechanisms, specific problems, and
include environment

Optimal behavior functions

Source of constraint and novel empirical predictions
Further Questions
 What are the limits of rational analysis?
 How can rational analysis be integrated with related
work in perception and motor control?
 How does rational analysis relate to proposed
cognitive architectures?
 Can learning be given a rational analysis?
 How constrained is rational analysis?
Happy Valentine’s Day (tomorrow)