Cognitive Psychology: Thinking Flashcards
What is a problem? 3 crucial elements
- “A problem arises when a living organism has a goal, but does not know how this goal is to be reached” (Duncker, 1945)
- A problem could therefore have three crucial elements:
- Starting state
- A set of processes that can transform the starting state into the goal state
- Goal state
Problem solving: The Behaviourist Approach
(Problem solving occurs through an incremental process of trial and error)
- Behaviours are learnt from interactions with the environment
- All behaviour can be reduced to simple stimulus-response behaviours
- Behaviourism is primarily concerned with observable behaviours and should be studied scientifically
- Thorndike’s Puzzle Box (1898) put cat in box and timed how long it took them to escape the box (by pushing a lever) and then he put the cat back in the box and recorded the time again
- Results: the cats learnt that pressing the lever had favourable consequences and therefore adopted this behaviour and became increasingly quick at pressing the lever (supports the trial and error)
- Thorndike proposed the ‘Law of Effect’: any behaviour followed by pleasant consequences is likely to be repeated & any behaviour followed by unpleasant consequence is likely to be stopped
Problem solving: The Gestalt Approach
(Problem solving occurs through a process of restructuring and insight)
- Reproductive thinking: involves re-uses of previous experience
- Productive thinking: involves a novel restructuring of the problem
- Insight occurs during productive thinking when the problem is restricted, and the solution becomes clear
- Kohler (1925) studied a colony of chimps during WW1. He put them in a cage with 2 sticks not long enough to reach the bananas. Overtime, he placed the 2 sticks together to reach the bananas
- Conclusions: problem solving is viewed to be the result of sudden insight and the moment the correct solution is found, performance is perfect
Four stages of creative thinking: Wallas (1926)
- Preparation: a problem is formulated, and initial attempts are made to solve the problem
- Incubation: the problem is set aside, and no conscious work is done on it
- Illumination: a sudden inspiration provides a new insight into the way in which the problem might be solved
- Verification: conscious work on the problem develops and tests the inspiration to provide a full solution to the problem
Existence of incubation in problem solving: The cheap necklace problem: Silveria (1971)
- Starting state: given 4 chain, each consisting of three links and it costs 2p to open a link and 3p to close a link. All links are closed at the beginning of the problem
- Goal state: your goal is to join all 12 links into a single circle for less than 15p
- Solve the problem: find the set of processes that transform the starting state into the goal state
- Results: 55% solved when given 30 mins (control group), 64% solved when worked for 30 mins then interrupted by 30 mins break (G1), 85% solved when worked for 30 mins then interrupted by a 4 hr break (G2)
Existence of incubation in problem solving: Murray & Denny (1969)
- Pp’s were divided into high and low ability groups on their performance on a ‘use of objects test’ of creativity
- Pp’s were given 20 mins to solve a complex practical problem (Control group = not given 5 min break, ‘Incubation’ group = given a 5 min break)
- Results: the break interfered with the performance for high ability pp, but aid the low ability pp. Individual differences in creativity determine whether an incubation period is effective
What are some barriers to successful problem solving? 2 factors
- Functional fixedness: People fixate on one property/function of an object and cannot think about it in a different way
- Duncker (1945): pp tried to nail the candle to the wall and the task becomes easier when the box of nails is emptied onto the table as the pp’s are no longer fixated on the nails
- Scheerer (1963) overcoming fixedness: The Nine Dot Problem - The Einstellung Effect: People learn a particular strategy for solving a problem which has produced success in the past and continue to use it even when it’s inappropriate
- The Luchins (1942): work out how to use the jugs to measure the final required quantity. The negative effect of previous experience when solving new problems as people become biased by previous experience to use all three jugs to solve this problem
Feeling of knowing: Brown & McNeil (1966)
- Even when we cannot solve a problem immediately, we sometimes feel closer to the solution than at other times
- A classic example is the “tip-of-the-tongue phenomenon” When people claim the answer is “on the tip of their tongue” they are 57% correct in identifying the first letter of the word
Feeling of warmth: Metcalfe (1986)
(sometimes predict how close to solution you are)
- Metcalfe (1986) compared feelings-of-warmth for incremental and insight- based problems.
- Feelings-of-warmth predicted performance on incremental problems (e.g., Tower of Hanoi)
- Feelings-of-warmth did not predict performance on insight problems (e.g., Reversing Triangle problem)
What is normative reasoning?
- Real decision-making scenarios often only include probably information (e.g. the likelihood of something happening
- Probability theories can therefore be used to define the best possible decision in these scenarios
- One example is Bayes’ Theorem
Mathematical rule for inverting conditional probabilities allowing us to find the probability of a cause given its effect - Reasoning according to such mathematical theories provides normative answers to decision-making scenarios
What is human reasoning? 2 heuristics
- Kahneman & Tversky systematically investigated some of these situations where human reasoning is biased
- They proposed that these biases occur because people often use heuristics to answer complex probabilistic heuristic
- The Representative Heuristic (This is the assumption that representative or typical members of a category are encountered more frequently)
- The Availability Heuristic (Judging the frequency of an event based on how easily relevant examples or instances come to mind)
The Representative Heuristic: Kahneman & Tversky (1973)(1981)(1982)
- (1973) Pp’s were asked to judge professionals from bring character descriptions (30 were engineers, 70 were lawyers). When presented with a blank card with no description, 30% engineer. Results: Pp’s ignore the base rate probabilities and instead base their judgement on whether the description sounds like engineer
- (1981) Results: 90% of subjects feel that Linda is more likely to be a feminist bank teller rather than just a bank teller. THIS IS WRONG!
- The Conjunction Fallacy: a reasoning error that people think the chances of two things happening together is greater than the chance of one of those things happening alone
- (1982) proposed that this fallacy occurs because specific scenarios appear more likely than general ones because they are more representative of how we imagine them
The Representative Heuristic: The gambling fallacy
- The gambler’s fallacy: the mistaken belief that future tosses of a coin are influenced by past events
- Kahneman & Tversky (1972) proposed that some sequences of events ‘represent’ our conception of ‘randomness’ better than others
The Availability Heuristic: Tversky & Kahneman (1974)
- Most people get these wrong because more info is available about the wrong answer, largest because of media coverage (e.g. what’s more likely: getting killed by a shark or falling airplane parts?)
- This is a Memory effect
- Tversky & Kahneman (1974) asked pp’s which of the following is more frequent: ‘a word in English that has K as the 1st letter, or a word in English that has K as the 3rd letter”
- 69% answered A incorrectly
- They argued that because our lexicon is organised by spelling more words beginning with K are available for retrieval
The Availability Heuristic: Lichtenstein et al *1978)
investigated the judged frequency of lethal events:
- Research has shown that when people apply this heuristic they: Underestimate the probabilities of high frequency events & Overestimate the probabilities of low frequency events
