One determinant of which schemas are likely to be used in social judgment is the extent to which we attend to particular features of the person or situation that we are responding to. We are more likely to judge people on the basis of characteristics of salience, which attract our attention when we see someone with them. For example, things that are unusual, negative, colorful, bright, and moving are more salient and thus more likely to be attended to than are things that do not have these characteristics (McArthur & Post, 1977; Taylor & Fiske, 1978).
We are more likely to initially judge people on the basis of their sex, race, age, and physical attractiveness, rather than on, say, their religious orientation or their political beliefs, in part because these features are so salient when we see them (Brewer, 1988). Another thing that makes something particularly salient is its infrequency or unusualness. If Bianca is from Italy and very few other people in our community are, that characteristic is something that we notice, it is salient, and we are therefore likely to attend to it. That she is also a woman is, at least in this context, is less salient.
The salience of the stimuli in our social worlds may sometimes lead us to make judgments on the basis of information that is actually less informative than is other less salient information. Imagine, for instance, that you wanted to buy a new smartphone for yourself. You’ve been trying to decide whether to get the iPhone or a rival product. You went online and checked out the reviews, and you found that although the phones differed on many dimensions, including price, battery life, and so forth, the rival product was nevertheless rated significantly higher by the owners than was the iPhone. As a result, you decide to go and purchase one the next day. That night, however, you go to a party, and a friend of yours shows you her iPhone. You check it out, and it seems really great. You tell her that you were thinking of buying a rival product, and she tells you that you are crazy. She says she knows someone who had one and had a lot of problems—it didn’t download music properly, the battery died right after the warranty was up, and so forth, and that she would never buy one. Would you still buy it, or would you switch your plans?
If you think about this question logically, the information that you just got from your friend isn’t really all that important; you now know the opinions of one more person, but that can’t really change the overall consumer ratings of the two machines very much. On the other hand, the information your friend gives you and the chance to use her iPhone are highly salient. The information is right there in front of you, in your hand, whereas the statistical information from reviews is only in the form of a table that you saw on your computer. The outcome in cases such as this is that people frequently ignore the less salient, but more important, information, such as the likelihood that events occur across a large population, known as base rates, in favor of the actually less important, but nevertheless more salient, information.
Another case in which we ignore base-rate information occurs when we use the representativeness heuristic, which occurs when we base our judgments on information that seems to represent, or match, what we expect will happen, while ignoring more informative base-rate information. Consider, for instance, the following puzzle. Let’s say that you went to a hospital this week, and you checked the records of the babies that were born on that day (Table 2.1). Which pattern of births do you think that you are most likely to find?
List A |
List B |
||
6:31 a.m. |
Girl |
6:31 a.m |
Boy |
8:15 a.m. |
Girl |
8:15 a.m. |
Girl |
9:42 a.m. |
Girl |
9:42 a.m. |
Boy |
1:13 p.m. |
Girl |
1:13 p.m. |
Girl |
3:39 p.m. |
Boy |
3:39 p.m. |
Girl |
5:12 p.m. |
Boy |
5:12 p.m. |
Boy |
7:42 p.m. |
Boy |
7:42 p.m. |
Girl |
11:44 p.m. |
Boy |
11:44 p.m. |
Boy |
Most people think that List B is more likely, probably because it looks more random and thus matches (is “representative of”) our ideas about randomness. But statisticians know that any pattern of four girls and four boys is equally likely and thus that List B is no more likely than List A. The problem is that we have an image of what randomness should be, which doesn’t always match what is rationally the case. Similarly, people who see a coin that comes up heads five times in a row will frequently predict (and perhaps even bet!) that tails will be next—it just seems like it has to be. But mathematically, this erroneous expectation (known as the gambler’s fallacy) is simply not true: the base-rate likelihood of any single coin flip being tails is only 50%, regardless of how many times it has come up heads in the past.
To take one more example, consider the following information:
I have a friend who is analytical, argumentative, and is involved in community activism. Which of the following is she? (Choose one.)
- A lawyer
- A salesperson
Can you see how you might be led, potentially incorrectly, into thinking that my friend is a lawyer? Why? The description (“analytical, argumentative, and is involved in community activism”) just seems more representative or stereotypical of our expectations about lawyers than salespeople. But the base rates tell us something completely different, which should make us wary of that conclusion. Simply put, the number of salespeople greatly outweighs the number of lawyers in society, and thus statistically it is far more likely that she is a salesperson. Nevertheless, the representativeness heuristic will often cause us to overlook such important information. One unfortunate consequence of this is that it can contribute to the maintenance of stereotypes. If someone you meet seems, superficially at least, to represent the stereotypical characteristics of a social group, you may incorrectly classify that person as a member of that group, even when it is highly likely that he or she is not.
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