Error Management Theory and biased first impressions: How do people perceive potential mates under conditions of uncertainty?
Abstrakt
eople must make inferences about a potential mate's desirability based on incomplete information. Under such uncertainty, there are two possible errors: people could overperceive a mate's desirability, which might lead to regrettable mating behavior, or they could underperceive the mate's desirability, which might lead to missing a valuable opportunity.
How do people balance the risks of these errors, and do men and women respond differently? Based on an analysis of the relative costs of these two types of error, we generated two new hypotheses about biases in initial person perception: the Male Overperception of Attractiveness Bias (MOAB) and the Female Underperception of Attractive Bias (FUAB). Participants (N = 398), who were recruited via social media, an email distribution list, and snowball sampling, rated the attractiveness of unfamiliar opposite-sex targets twice: once from a blurred image, and once from a clear image.
By randomizing order of presentation (blurred first vs. clear first), we isolated the unique effects of uncertainty-which was only present when the participant saw the blurred image first. As predicted, men overperceived women's attractiveness, on average.
By contrast, as predicted, women underperceived men's attractiveness, on average. Because multiple possible decision rules could produce these effects, the effects do not reveal the algorithm responsible for them.
We explicitly addressed this level of analysis by identifying multiple candidate algorithms and testing the divergent predictions they yield. This suggested the existence of more nuanced biases: men overperceived the attractiveness of unattractive (but not attractive) women, whereas women underperceived the attractiveness of attractive (but not unattractive) men.
These findings highlight the importance of incorporating algorithm in analyses of cognitive biases.