ch 16 thinking and reasoning

8/2/2019 Ch 16 Thinking and Reasoning

1/23

Christopher Sterling Complete Psychology published by Hodder Education

Thinking and reasoning


2/23


Problem solving overview

Problem solving finding a method of gettingfrom where you are to where you want to be.

Two kinds of problem:

Well-defined problems, e.g. anagrams,crosswords You (1) have all the information you need and (2)

know what has to be done. The problem is doing it

Ill-defined problems some aspect of theproblem is not well-defined Most real-life problems are ill-defined.


3/23


Behaviourist approach

Behaviourist approaches to problem solving: Reproductive strategies (Hull, 1920):

where possible the problem solverreproduces (parts of) old solutions, e.g.

making a cold car start; writing an essay Trial-and-error strategy (Thorndike, 1911):

when old solutions wont work, problemsolver (1) makes random attempts which

(2) lead to correct action by chance whichleads to (3) learning the correct action

e.g. cat escaping from cage.


4/23


Gestalt approach

Emphasis on:

Productive strategies past experience may

certainly help solve many problems but

some situations demand a new solution.These are arrived at through a process of

insight. Insight involves re-configuring or

restructuring the problem

Example of Sultan the chimpanzee reachingsuspended food by climbing on boxes.


5/23


Gestalt approach Characteristics of thinking which are

barriers to success:

1. Functional fixedness

The pendulum problem. Two strings hangingfrom the ceiling have to be tied together but

solver cant reach both at the same time.

Room contains objects such as paintbrush,

hammer, can of paint, nails. How can problem

be solved?

The candle problem (Duncker, 1926, 1945)


6/23


Gestalt approach

2. Mental set: learn a particular way ofsolving a problem which often producessuccess, but continue to use it even wheninappropriate Luchins (1942, 1959) water jars problem

Problems 16 can be solved by formula b-a-2c

Participants continue to use this solution toattempt all the other problems, even when

wrong or when better solutions exist. Theyacquire a mental set for solving water jarproblems which they find difficult to abandon.


7/23


Approaches Behaviourist objections to insight:

What psychological processes are occurring

during insight? Why and how does

restructuring occur? Might just as well callinsight aha

Epstein (1984) showed that pigeons could

solve Sultans problem if theyd been

previously trained to (1) move a box tocorrect location and (2) climb the box.


8/23


Analogy

Analogy: problem solver tries to solve problem on thebasis of its similarity to another problem which s/he knowshow to solve

Glick and Holyoak (1983) does knowing how to solvethe fortress problem help solve the radiation problem?

Radiation problem: a person has a malignanttumour in middle of their body. How can you radiatethis to kill it without killing healthy tissue?

The fortress problem: a good king wants to attack a

tyrant in his fortress, but roads to the fortress aremined large numbers of troops would be killed butsmall numbers could get through. How can thegood king invade the fortress?


9/23


Analogy Results:

10% solved the radiation problem without help

30% solved problem after exposure to fortressproblem and its solution (only 20% more)

75% solved problem after solving fortress problemand being told it would help in solving radiationproblem

25% never saw any connection between the two

problems. Analogies may not be that helpful in solving problems.

The relationship between two problems doesnt seem tobe that evident to people, certainly not spontaneous.


10/23


General problem solver

The general problem solver (Newell and Simon, 1963): Solving a problem consists of a number of states:

initial state, intermediate states, end state

Solver moves through these states using appropriate

physical or mental operations Describes: maze finding; proving a theorem; solving

an anagram; winning at chess; finding the partner of

your dreams; becoming prime minister

But, what are these operations? How are theyacquired? How do we know when to apply them?


11/23


General problem solver

Prior experience and formal instruction play a role: Learn rules of game

Acquire heuristics (rules of thumb) which provide guidelines orstrategies

Meansend analysis is a common heuristic involves

breaking problem down into subgoals. Achieving eachsubgoal may also involve using a heuristic

Consider winning at chess: There are rules which specify each pieces movements learn

these formally

Playing involves achieving subgoals, each of which is aheuristic in its own right, e.g. control the centre of the board,protect the king, get the opponents queen asap, etc.


12/23


Concept learning

Concepts are mental representations of classes of objects: Table concept consists of all things considered to be a

table Animal concept consists of all things considered to be

an animal. Logical concepts are identified by clear and unambiguous

rules, e.g. a square has four equal sides and all angles are90 degrees.

Natural concepts: there are no clear rules which identifyinstances, e.g. boundaries between animals are fuzzy compare cats, pekes, Alsatians, wolves.

How are concepts learned? The type of concept may well determine how its learned.


13/23


Concept learning

Associative learning: Hull (1920) argued thatconcepts are acquired through associativelearning; members of the category becomeassociated with each other (and with their name)

Experiment using Chinese characters; participantshad to learn which patterns were members of thecategory (those containing the radical) and whichwerent (those that didnt)

Took participants many, many trials but even then

couldnt explicitly distinguish members from non-members

Consistent with building up of associations over aperiod of time.


14/23


Concept Learning Hypothesis testing. Bruneret al. (1956): concepts are

learned by forming and then testing hypotheses aboutwhat the concept is

Experiment using multidimensional stimuli; participants had tolearn which are members of the category (identified by a rule)and which arent (those not conforming to rule)

Participants formed hypothesis about what the rule might be

after a few stimuli and then tested the rule on further stimuli if hypothesis was wrong they changed the hypothesis

Bower & Trabasso (1964) found that until the participant gotthe rule they were guessing (50% correct). Once they got therule, they jumped to 100% correct. This pattern of responding

is not consistent with buildingassociations


15/23


Concept learning

How are concepts learned? Anderson (1995) argues that a single theory may not be

enough to explain how concepts are learned:

Fuzzy concepts (e.g. dog) may be learned

predominantly by association (exclude instanceswhere specialist knowledge applies, e.g. why whales

are mammals; why bats are mammals)

Logical concepts may be learned predominantly by

hypothesis testing.


16/23


Judgements

Judgements involve drawing a conclusion from acombination of knowledge and observation: Is this person Ive just met someone I can trust to look

after my bag?

Yes, I think so. Hes got an honest face and plays cricket. Takes the form of a subjective probability, often

based on partial or incorrect information or flawedreasoning and prejudices.

Should be based on logical thinking or an objectivecalculation of probabilities.

But is often based on heuristics (rules of thumb) andnon-relevant information.


17/23


Judgement

Base rate information. Two questions:

1. Can people use objective, statistical

(base rate) information to come to an

informed judgement?2. Do people use objective, statistical (base

rate) information to come to an informed

judgement?


18/23


Judgement

The representativeness heuristic

We base our judgement more on subjective

impressions of how representative an

instance is of a particular category than onobjective information

Lawyers and engineers problem

(Kahneman and Tversky, 1973).


19/23


Judgement The availability heuristic (Tversky and Kahneman, 1974):

judgement is made on the basis of available information,e.g. recent memories:

The frequency of letters in the English language was

studied. In typical texts the relative frequency ofletters in the first and third position was tallied Is R more likely to appear in the first or third position?

Doctors are more likely to make a diagnosis of heartdisease if they have recently seen and diagnosed

several cases of heart disease than if they havent(Weberet al., 1993). Base rate information isswamped by these experiences.


20/23


Judgement

Gamblers fallacy the belief that if an event

hasnt happened for a while it must happen

soon because of the law of averages.

Illusory correlation the perception thatassociations and causeeffect relationships

exist when the evidence is flimsy or non-

existent, e.g. correlation between positive

characteristics and political party we supportand between negative characteristics and

political party we dislike.


21/23


Decision making

Expected utility theory: Calculated by multiplying the value of an alternative (how

much money will this bring me? how happy will it make me?)by the likelihood of it happening (very unlikely; quite likely):

You have a pound to spend on the lottery. You can spendit on a lottery which has one prize of 10 million, or on one

which has 100 prizes of 100,000 pounds. Which lotteryticket do you buy? You are at a party. You see two people. One is mildly

attractive and sending you a clear come on. The other isstunning and gave you a brief smile when your eyes met.Who do you approach?

Theory seems to apply when presented with two simplechoices, but most life decisions are more complex. There maybe more than one alternative and other constraints may apply,e.g. how did you make the decision to come to university?


22/23


Decision making

One reason why expected utility theory may not giveaccurate predictions is because of biases.

Kahneman and Tversky looked at how people managerisk taking and uncertainty (prospect theory) and foundbiases in reasoning.

Risk aversion. In certain circumstances people makedecisions to avoid risk (specifically loss), e.g.: Get 1000 with certainty or a 50% chance of getting 2500

people tend to chose certainty and avoid the risky option Treating 100 people for a disease. Vaccine A will definitely

save 30 people but the others will die. Vaccine B may (30%chance) save everyone, but there is a 70% chance everyonewill die. Which vaccine?

Loss aversion is synonymous with risk aversion; see alsosunk cost effect.


23/23


Decision making

Risk seeking.In other circumstances people makedecisions that seek risk:

Lose 1000 with certainty or have a 50:50 chance of losing nothing

or 2500. People chose the risky option

Treating 100 people for disease. If vaccine C is used, 70 people will

die. If vaccine D is used, there is a 30% chance no one will die and

a 70% chance everyone will die. Which vaccine?

Whether we choose risk aversion or risk seeking

depends, amongst other things, on how the choices are

framed: Decision making is susceptible to biases in reasoning, e.g. it doesnt

always follow mathematical or rational principles.

ch 16 thinking and reasoning

Documents