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Information Sampling, Judgment, and the Environment: Application to the Effect of Popularity on Evaluations
Author(s) -
Le Mens Gaël,
Denrell Jerker,
Kovács Balázs,
Karaman Hülya
Publication year - 2019
Publication title -
topics in cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 56
eISSN - 1756-8765
pISSN - 1756-8757
DOI - 10.1111/tops.12387
Subject(s) - popularity , resampling , psychology , social psychology , sampling (signal processing) , experience sampling method , computer science , artificial intelligence , filter (signal processing) , computer vision
If people avoid alternatives they dislike, a negative evaluative bias emerges because errors of under‐evaluation are unlikely to be corrected. Prior work that analyzed this mechanism has shown that when the social environment exposes people to avoided alternatives (i.e., it makes them resample them), then evaluations can become systematically more positive. In this paper, we clarify the conditions under which this happens. By analyzing a simple learning model, we show that whether additional exposures induced by the social environment lead to more positive or more negative evaluations depends on how prior evaluations and the social environment interact in driving resampling. We apply these insights to the study of the effect of popularity on evaluations. We show theoretically that increased popularity leads to more positive evaluations when popularity mainly increases the chances of resampling for individuals with low current evaluations. Data on repeat stays at hotels are consistent with this condition: The popularity of a hotel mainly impacts the chances of a repeat stay for individuals with low satisfaction scores. Our results illustrate how a sampling approach can help to explain when and why people tend to like popular alternatives. They also shed new light on the polarization of attitudes across social groups.