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PSYC*2650 Ch 12.doc

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PSYC 2650
Anneke Olthof

Chapter 12: Judgment: Drawing Conclusions From Evidence - we draw conclusions from out experiences - the focus of this chapter will be on Induction - a process in which you try to go beyond the available information, - we will want to compare a Descriptive Account of human induction telling us how the process ordinarily proceeds, with a Normative Account, how things ought to go - in this way, we will be able to ask whether our day-to-day judgments are foolish or sen- sible and whether our commonsense conclusions are justified or not Judgment Heuristics - experiences can be an extraordinary teacher - the process of education doesn’t depend just on the teacher, it also depends on the student - the capacity to learn depends on the person who has the experience and their memory - virtually any factor that influences memory will influence the pattern of conclusions and the quality of our judgments - we often draw conclusions based on experiences and memories gathered over months and years, if these memories are selective, incomplete or distorted then the validity of the conclusions is questionable Attribute Substitution - attribute substation is a strategy when you’re trying to evaluate some point but don’t have easy access to the target information, you therefore rely instead on some other as- pect of experience that’s more accessible and that is a plausible substitute for the infor- mation you seek - e.g. trying to predict how well you will do in a course next semester do you ask how your friends have done, how many have done well, how many haven’t done well - in this case you are seeking information about frequencies - assessments of how often various events have happened in the past - you may not be able to recall all of the friends that have taken the course and their ex- act grades so you do a quick scan through memory for relevant cases - if you remember 4 friends with good grades, you’ll conclude this is frequent - you are relying on availability - the ease with which things come to mind, as an index for frequency Availability Heuristic - the reliance on availability as a substitute for frequency - a different example is if you are trying to judge if your friend is lying, you could try to re- member everything you know about your friend and everything you know about liars Representativeness Heuristic - using resemblance in place of information about cate- gory membership The Availability Heuristic - heuristics are reasonably efficient strategies that usually lead us to the right answers - they allow errors in order to gain efficiency - to avoid errors, you need a slower and effortful strategy The Wide Range of Availability Effects - people regularly overestimate the frequency of events that are quite rare - e.g. buying lottery tickets - events that are unusual are likely to catch your attention, you notice them ensuring they are well recorded in memory - in a study, participants are asked to recall 6 or 12 examples of themselves being as- sertive, those in the 6 group believed they were assertive people because it was easy to come up with examples, those in the 12 group thought the opposite - participants who recalled more examples had more evidence of their assertiveness - it’s not the quantity of evidence that matters, it’s the ease of coming up with examples - availability can also be influenced by many other factors - imagine you are asked to vote how much money the government should spend on re- search projects all aimed at saving lives - people assert that motor vehicle accidents and homicide are more frequent then stom- ach cancer so they vote for those even though that is untrue - people are heavily influenced by the pattern of media coverage The Representativeness Heuristic - this amounts to an assumption that the categories we encounter are relatively homo- geneous - that leads us to act as if each member of a category is “representative” of the category - e.g. if someone is a lawyer you give them a stereotype and expect them to hair traits associated with lawyers, also if someone looks like a lawyer and has lawyer traits, we conclude they are a lawyer - this leaves us quite willing to draw conclusions from a relatively small sample - these reasoning patterns often lead us to correct conclusions because many cate- gories are homogeneous - there can be error of course Reasoning From the Population to an Instance - gambler’s fallacy - if a coin has come up tails 6 times in a row, it is more likely to be heads on the next toss - the “logic” leading to this fallacy seems to be that is the coin is fair, then a series of tosses should contain equal numbers of heads and tails - the coin is “overdue” for heads to bring out balance - the explanations for this lies in our assumption of category homogeneity - we know that over the long haul a fair coin will produce equal numbers of heads and tails - we expect any “representative” of the category with show a 50-50 splits Reasoning From a Single Case to the Entire Population - people expect each subset of the category and each individual within the category to have the properties of the category overall - people also expect the overall category to have the properties of the individuals so they are willing to extrapolate from a few instances to the entire set - you regularly hear “man who” or “woman who” arguments” - e.g. don’t by that brand of car, I know a man whose broke down last week - e.g. what do you mean cigarette smoking causes cancer? I have an aunt who smoked for 50 years and she runs marathons! - we are willing to take that as seriously as a larger sample Anchoring - we use shortcuts in a wide range of settings including ones with important stakes - anchoring - often we don’t know the answer to a particular question, but we do have an idea about what “ballpark” the answer is in so we use that as an anchor and reach our answer by making some suitable adjustment to that anchor - the problem is we usually adjust too little so we are more influenced by the initial an- chor than we should be - e.g. asked if Ghandi lived past 50 and then asked to guess his age (average guess was 67), others asked if Ghandi lived past 9 and then asked to guess his age and guessed much lower because the anchor was lower - when judgment errors arise for other reasons, anchoring serves to cement them in place - judging whether death by homicide or diabetes is more frequent, you know that the media is biasing your judgment and so you might seek to adjust the estimate but an- choring will make it difficult to undo the error and you will pick homicide Detecting Covariation - it is worrisome that (thanks to anchoring) the errors produced by shortcuts can be long-lasting - people are sometimes influenced by availability even when there’s an obvious bias in what’s available - covariation - X and Y covary if X tends to be on the scene whenever I is and if X tends to be absent whenever Y is absent - e.g. exercise and stamina covary - covariation is a matter of degree and can be strong or weak, negative or positive - covariation is important for cause and effect - e.g. does eating a good breakfast make you feel good? Illusions of Covariation - do Rorschach test answers really covary with personality traits? - a study created fictional Rorschach protocols and functional descriptions of people who had supposedly offered these responses - they randomly paired the protocols and personality descriptions - they were then shown to undergraduate students who were asked to examine the pairs and determine what responses covaried with what traits - because it was random, there was no real covariation - the students reported seeing a pattern of covariation e.g. perceiving buttocks in the inkblots was an indicator of homosexuality - the covariation “perceived” by the students was identical to that alleged at the time by professional clinicians - is it just a coincidence that the illusory covariation detected by the participants matched the pattern observed by the clinicians? no because the pattern observed by clinicians is also illusory - there is no covariation between sexual orientation and the response Theory-Driven and Data-Driven Detection of Covariation - we should be wary of “professional wisdom” - professional training does not make someone immune to illusions - non-experts also fall into this pattern of projecting their own biases onto the evidence they observe, seeing only the patterns of covariation we expect to see - e.g. people who believe they can predict the weather by paying attention to their arthri- tis pain - no evidence of this - in a study, participants were asked to make covariation judgments in 2 types of situa- tions - with no prior expectation/biases and with expectations/biases - in the cases where participants held no prior beliefs, the covariations are reasonably regular - the stronger the covariation, the stronger the estimate - the estimates were also rather conservative (not usually more than +30/100) - in cases with prior beliefs, the participants tended to be far more extravagant (as high as +60 and +80) - there was only a weak relation between the magnitude of the estimated covariation and the magnitude of the actual covariation What Causes Illusory Covariation? - people can judge covariation in the absence of prior beliefs - they perform poorly when they have beliefs because they the evidence they consider - people consider only a subset of the evidence, and that subset is shaped by their prior expectations - this rests on a tendency known as Confirmation Bias - a tendency to be more alert and more responsive to evidence that confirms one’s beliefs, rather than to evidence that might challenge one’s beliefs - if your data is biased, so will be your judgment - social stereotypes are based on illusory cavorting e.g. anti-semitic illusion that being overly concerned with money “covaries” with being Jewish Base Rates - another problem that can effect our assessment of covariant is our neglect of base- rate information - e.g. my friend is a professor and likes to write poetry, is shy and small in stature. Does he study Chinese studies or
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