Not what you were born with: people prioritize controllable cues when forming first impressions in naturalistic contexts

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

First impressions are ubiquitous and consequential. Prior work identified a rich set of cues shaping impressions by studying one cue at a time (e.g., facial structure, body shape, hair color, environmental valence). Here we ask whether integrating rich cues simultaneously offers new insights into how people make snap judgments in naturalistic contexts. This poses a technical challenge—characterizing and manipulating rich cues in naturalistic stimuli. We tackled this challenge using novel computational methods. Across three large-scale, pre-registered studies across U.S. and Chinese representative participants (N = 3,734), we found that, with rich cues, people relied on only a small subset to make snap judgments. These cues were often controllable by the targets (e.g., action, clothing, rather than facial structure). We replicated these findings in both the U.S. and China, across seven judgments, and in novel stimuli that the models were not trained on. A general mechanism underlies these rich cues and impressions: people form impressions using cues that i) carried unique information beyond other cues, which makes these cues uniquely informative, or ii) shared information with other cues, which makes these cues reliably reinforcing judgments. By computationally manipulating one cue at a time in naturalistic images, we showed that changing just one of these controllable cues was sufficient to change others’ impressions in naturalistic contexts. Together, our findings advance the mechanism of snap judgments in the real world: the human mind may leverage the structure of co-occurring cues that it learns in daily life to prioritize a small subset of cues for impressions.

Article activity feed