Logo image
Back
Error Management Theory and biased first impressions: How do people perceive potential mates under conditions of uncertainty?
Journal article   Peer reviewed

Error Management Theory and biased first impressions: How do people perceive potential mates under conditions of uncertainty?

D.M.G. Lewis, L. Al-Shawaf, A.Y. Semchenko and K.C. Evans
Evolution and Human Behavior, Vol.43(2), pp.87-96
2021
DOI: https://doi.org/10.1016/j.evolhumbehav.2021.10.001
url
Link to Published Version *Subscription may be requiredView

Abstract

People 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.

Details

Metrics

116 Record Views
6 Times Cited - Web of Science

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types
Domestic collaboration
International collaboration
Citation topics
6 Social Sciences
6.73 Social Psychology
6.73.1369 Evolutionary Psychology
Web Of Science research areas
Behavioral Sciences
Psychology, Biological
Social Sciences, Biomedical
ESI research areas
Psychiatry/Psychology
Logo image