HPS203 Lecture Notes - Lecture 12: Affective Forecasting, Prior Probability, Deductive Reasoning
HPS203 Week 12
TOPIC 9: JUDGMENT AND REASONING
Define attribute substitution
• Attribute substitution: a commonly used strategy in which someone needs one type of
information but relies instead on a more-accessible form of information
o This strategy works well if the more-accessible form of information is, in fact, well
correlated with the desired information
o An example is the case in which someone needs information about how frequent an event
is in the world and relies instead on how easily he or she can think of examples of the event
• Heuristics are efficient strategies that usually lead you to the right answers (however with
efficiency is the chance of errors)
Define the availability heuristic and give an example
• Availability heuristic: a strategy used to judge the frequency of a certain type of object or
the likelihood of a certain type of event
o The first step is to assess the ease with which examples of the object or event come to
mind; this ‘availability’ of examples is then used as an index of frequency or likelihood
• Tverny and Kahneman (1973) referred to attribute substitution as an availability heuristic
• You need to judge frequency, but you don’t have easy access to information about
frequency – so you rely on a plausible substitute: availability in memory, using this logic
The R word example
• ‘Are there more words in the dictionary beginning with the letter R or more words with R
in the third position?’
• Most people assert that there are more words beginning with R, but the reverse is true
• Why do people get this wrong? The answer lies in availability
• If you search your memory for words beginning with R, many will
come to mind
• But if you search your memory for words with an R in the third
position, fewer will emerge
• This is because your memory is organised roughly like a
dictionary, with words sharing a starting sound grouped together, therefore it’s easier to
search memory using ‘starting letter’ as your cue
• Thus organisation in memory creates a bias in what’s easily available, and this bias in
availability leads to an error in frequency judgment
Define the representativeness heuristic and how it can explain the gambler’s fallacy
• Representativeness heuristic: a strategy often used in making judgments about categories.
This strategy is broadly equivalent to making the assumption that in general, the instances
of a category will resemble the prototype for that category and, likewise, that the prototype
resembles each instance
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How does the representativeness heuristic work?
• Many of the categories you encounter are relatively homogeneous
o e.g. The category birds is homogeneous with regard to the traits of having wings, having
feathers, etc.
• The representativeness heuristic capitalises on this homogeneity
o We expect each individual to be resemble the other
individuals in the category
o Thus we expect each individual to be representative of the
category overall
o As a result, we can use resemblance as a basis for judging
the likelihood of category membership
Example
• Applying for a job, hoping the employer will carefully examine your credentials and make a
thoughtful judgment about whether you’d be a good hire
• It’s likely, though, that the employer will rely on a faster, easier strategy
• Specifically, the employer may barely glance at your resume and instead ask himself how
much you resemble other people he’s hired who have worked out well
• Do you have the same mannerisms, or the same look, as an employee he’s very happy
with?
• If so, you’re likely to get the job
• In this case, the employer wants to judge a probability and
instead relies on resemblance
Gambler’s fallacy
• Since many categories are homogeneous, reasoning in this fashion will often lead you to
sensible judgments
• Even so, the use of this heuristic can lead to error
• E.g. tossing a coin repeatedly
o The coin has landed heads up six times in a row
o Many people believe that, on the next toss, the coin is
more likely to come up tails
o But this conclusion- the gambler’s fallacy- is wrong
o The ‘logic’ leading to this fallacy seems to be that if the coin is fair, then a series of tosses
should contain equal numbers of heads and tails
o However the coin has no ‘memory’ – no way of knowing or being influenced by how long
it’s been since the last tails
• What produces the gambler’s fallacy?
o The explanation lies in the assumption of category
homogeneity
o We all know that, in the long run, a fair coin will produce
equal numbers of heads and tails
o Thus, the category of ‘all tossess’ has this property
o Some sequences of tosses will show the 50-50 split, but
some will show 75% heads, others will show 5% heads, etc. It is only when these sequences
are combined that the 50- 50 split emerges.
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Explain how and why people reason from a single case to the entire population
• The assumption of homogeneity can also lead to an expectation that the entire category
will have the same properties as the individual category members
• Thus, people will be far too willing to extrapolate from a few instances to the entire set
• “What do you mean cigarette smoking causes cancer? I have an aunt who smoked for 50
years, and she runs in marathons!”
o Often these arguments seem persuasive o But they have force only by virtue of the
representativeness heuristic, and thus your willingness to extrapolate from a tiny sample
Explain why covariation is important
• Covariation: a relationship between two variables such that the presence (or magnitude)
of one variable can be predicted from the presence (or magnitude) of the other
o Covariation can be positive or negative
o If positive, then increases in one variable occur when
increases in the other occur
o If negative, then decreases in one variable occur when
decreases in the other occur
• The errors caused by heuristics can, in turn, trigger other sorts of
errors, including errors in judgments bout covariation
• Covariation is important because it’s what you need to consider
when checking on a belief about cause and effect
Define illusions of covariation and explain why they occur
• Illusory covariation: a pattern that people ‘perceive’ in data, leading them to believe that
the presence of one factor allows them to predict the presence of another factor
o However, this perception occurs even in the absence of any genuine relationship between
these two factors
o As an example, people perceive that a child’s willingness to cheat in an academic setting is
also an indicator that the child will also be willing to cheat in athletic contests
oHowever, this perception is incorrect, and so the covariation that people perceive is
‘illusory’
• Illusions of covariation basically means seeing a relationship that doesn’t actually exist
o E.g. Many people are convinced that there’s a relationship between handwriting and
personality, but no serious study has documented this covriation
What causes illusions in covariation?
• In making these judgments, people seem to only consider a subset of the evidence, and
it’s a subset that’s skewed by their prior expectations
• This virtually guarantees mistaken judgments, since even if the judgment process were
100% fair, a biased input would lead to a biased output
• When judging covariation, your selection of evidence is likely to be guided by confirmation
bias
o A tendency to be more responsive to evidence that confirms your beliefs
oAccept information that confirms beliefs
o Rather than to evidence that might challenge your beliefs
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