Week 10 - Problem solving, reasoning, decision making Flashcards
Patient PF
- His right anterior prefrontal cortex was damaged due to a stroke
- Successful architect before the stroke
- He seemed perfectly normal and intelligent
- However, he lost his ability in architectural design
Operator
An action that will transform the problem state into another problem state
start state -> operators -> goal state
problem space
problem space consists of various states of the problem
problem state
A representation of the problem in some degree of solution
problem solving
searching a sequence of states in a problem space that goes from the start state to the goal state
Three ways to acquire new operators?
- discovery
- direct instruction
- analogy/imitation
Operator selection
- backup avoidance
- difference reduction
- means-ends analysis
backup avoidance
The tendency in problem solving to avoid operators that take one back to a
state already visited
difference reduction
- The tendency in problem solving to select operators that eliminate a difference between the current state and the goal state
- It is a useful method, but not always optimal
- It only considers whether the next step is an improvement and not whether the larger plan will work
Means-ends analysis
- creates a new subgoal to enable an operator to apply (an operator is not abandoned even if it cannot be applied immediately)
- identifies the biggest difference between the current state and the goal state and try to eliminate it first
- tower of hanoi problem
Observations of Tower of Hanoi problem
- Difference reduction doesn’t allow you to solve the Tower of Hanoi problem
- People tend to adopt the difference reduction strategy first and then start using
means-ends analysis when they try to solve the Tower of Hanoi problem - Patients with prefrontal damage often have difficulty in making backward moves
in the Tower of Hanoi problem - They cannot maintain the goal in working memory very well
problem presentation
- How states of a problem are represented has significant effects
- Successful problem solving depends on representing problems in a way appropriate operators can be applied
Incubation effects
- The phenomenon that sometimes solutions to a particular problem come easier after a period of time in which one has ignored trying to solve the problem
- Incubation effects occur because people forget inappropriate ways of solving
problems
Silveira’s (1971) cheap-necklace problem
Three groups of participants (they all worked on the problem for 30 min)
* Control: continuous 30 min (55%)
* Group 1: interrupted by 30-min of other activities (64%)
* Group 2: interrupted by a 4-hour break (85%)
four areas of human irrationailty
- Reasoning about conditionals
- Reasoning about quantifiers
- Reasoning about probabilities
- Decision making
What is a conditional statement
- If A, then B
- An assertion that if an antecedent (A) is true, then a consequent (B) must be true
Wason selection task
- If a card has a vowel on one side, then it has an even number on the other side
- Neither a vowel nor a consonant on the other side of 4 will falsify the rule
- only 10% of participants made the right combination choices
Permission schema
performance on a selection task can be enhanced when the material has meaningful content
Griggs and Cox (1982)
- if a person is drinking a beer, then the person must be over 19
- 74% of participants selected the logically correct combination
Probabilistic interpretation
- people tend to select cards that will be informative under a probabilistic model, not a strict logical model
- If A, then B (B will probably occur when A occurs)
Oaksford and Chater (1994) car task
- “If a car has a broken headlight, it will have a broken taillight.”
Given four choices: - cars with broken headlights
- cars without broken taillights
- cars without broken headlights
- cars with broken tailights
first two are the logically correct choices
probabilistic interpretation of the car task
- It is not logical, but informative choice
- We tend to interpret conditional statements on the basis of a probabilistic model, not a strict logical model
- because doing so actually makes sense in many situations in real life
- This might be one reason why making the correct (=logical) choice in the original
Wason’s selection task is so difficult
Prior probability
The probability that a statement is true before consideration of the evidence
Posterior probability
- The probability that the statement is true after consideration of the evidence
- To calculate the posterior probability, you need to take into account:
- Prior probability (base rate)
- Evidence
- How reliable the evidence is
Bayes’s theorem computes the posterior probability