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Combining Traits: Information Integration

1 February, 2016 - 15:14

Let’s consider for a moment how people might use trait terms to form an overall evaluation of another person. Imagine that you have to describe two friends of yours, Amir and Connor, to another person, Rianna, who might be interested in dating one of them. You’ll probably describe the two men in terms of their physical features first, but then you’ll want to say something about their personalities. Let’s say that you want to make both Amir and Connor sound as good as possible to Rianna, but you also want to be honest and not influence her one way or the other. How would you do that? You would probably start by mentioning their positive traits: Amir is “intelligent” and “serious”; Connor is “fun” and “exciting.” But to be fair, you would also need to mention their negative traits: Amir sometimes seems “depressed,” and Connor can be “inconsiderate.”

You might figure that Rianna will just combine whatever information you give her, perhaps in a mathematical way. For instance, she might listen to all the traits that you mention, decide how positive or negative each one is, and then add the traits together or average them. Research has found that people do exactly that, both for strangers and for people whom they know very well (Anderson, 1974; Falconi & Mullet, 2003). Consider what might happen if you gave Rianna the following information:

  • Amir is smart, serious, kind, and sad.
  • Connor is fun, happy, selfish, and inconsiderate.

Rianna might decide to score each trait on a scale of +5 (very positive) to –5 (very negative). Once she has these numbers, she could then either add them together or average them to get an overall judgment.






























Based on this scoring, Rianna would probably decide that she likes Amir more than Connor. Of course, different people might weight the traits in somewhat different ways, and this would lead different people to draw different impressions about Amir and Connor. But there is pretty good agreement among most people about the meaning of traits, at least in terms of the overall positivity or negativity of each trait, and thus most people would be likely to draw similar conclusions.

Now imagine that you later thought of some other new, moderately positive characteristics about Amir—that he was also “careful” and “helpful.” Whether you told Rianna about them might depend on how you thought they would affect her overall impression of Amir. Perhaps these new traits would make Rianna like Amir more (after all, they do add new positive information about him). But perhaps they might make her like him less (if the new, moderately positive information diluted the existing positive impression she has already formed about him).

One way to think about this is to consider whether Rianna might be adding the traits together or averaging them. In our first example, it didn’t matter because the outcome was the same. But now it might—if she’s adding the traits together, then Rianna will probably like Amir more after she hears the new information, because new positive traits have been added to the existing sum score. If she is averaging the traits together, however, then Rianna will probably like him less than she did before, because the new, more moderate information tends to dilute the initial impressions.

It turns out that in most cases, our judgments are better predicted by mental averaging than by mental adding (Mills, 2007). What this means is that when you are telling someone about another person and you are trying to get him or her to like the person, you should say the most positive things that you know but leave out the more moderate (although also positive) information. The moderate information is more likely to dilute, rather than enhance, the more extreme information.