Learning Too Much From Too Little: False Face Stereotypes Emerge From a Few Exemplars and Persist via Insufficient Sampling.
Face stereotypes are prevalent, consequential, yet oftentimes inaccurate. How do false first impressions arise and persist despite counter-evidence? Building on the overgeneralization hypothesis, we propose a domain-general cognitive mechanism: insufficient statistical learning, or Insta-learn. This...
| Published in: | Journal of Personality & Social Psychology Vol. 128; no. 1; pp. 61 - 82 |
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| Main Authors: | , , , |
| Format: | Article |
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American Psychological Association
Jan2025
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=182534858&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 182534858 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00223514 JSS jtl: Journal of Personality & Social Psychology issn: 00223514 maglogo: N pubinfo: dt: Jan2025 vid: 128 iid: 1 pid: 34 pub: American Psychological Association artinfo: ui: 182534858 10.1037/pspa0000422 ppf: 61 ppct: 21 formats: tig: atl: Learning Too Much From Too Little: False Face Stereotypes Emerge From a Few Exemplars and Persist via Insufficient Sampling. aug: au: Bai, Xuechunzi Uddenberg, Stefan Labbree, Brandon P. Todorov, Alexander affil: Department of Psychology, The University of Chicago Department of Psychology, University of Illinois Urbana-Champaign The Institute for Social Research, University of Michigan Booth School of Business, The University of Chicago su: Social perception Race Statistical learning Sampling (Process) Statistical association sug: subj: Social perception Race Statistical learning Sampling (Process) Statistical association keyword: face stereotypes sampling social cognition statistical learning face stereotypes sampling social cognition statistical learning ab: Face stereotypes are prevalent, consequential, yet oftentimes inaccurate. How do false first impressions arise and persist despite counter-evidence? Building on the overgeneralization hypothesis, we propose a domain-general cognitive mechanism: insufficient statistical learning, or Insta-learn. This mechanism posits that humans are quick statistical learners but insufficient samplers. Humans extract statistical regularities from very few exemplars in their immediate context and prematurely decide to stop sampling, creating and perpetuating locally accurate—but globally inaccurate—impressions. Six experiments (N = 1,565) tested this hypothesis using novel pairs of computer-generated faces and social behaviors by fixing the population-level statistics of face–behavior associations to zero (i.e., no relationship). The initial sample contained either 11, five, or three examples with either a positive, zero, or negative linear relationship between facial features and social behaviors. The sampling procedure contained a free-sampling condition in which participants were free to decide when to stop viewing more examples and a fixed-sampling condition in which participants were forced to view all stimuli before making decisions. Consistent with the Insta-learn mechanism, participants learned novel face stereotypes quickly, with as few as three examples, and did not sample enough when they were given the freedom to do so. This domain-general cognitive mechanism provides one plausible origin of false face stereotypes, demonstrating negative consequences when people learn too much from too little. Statement of Limitations: Although we aim to study a general psychological phenomenon, our ideas and findings are bound by the available literature, methodological choices, and samples of participants. All of these elements add potential subjectivity. First, most references in this article come from the United States and Western Europe. This may constrain the accumulated knowledge to certain historical contexts, which may not be applicable to other contexts. Second, we used tightly controlled experiments because we needed to pin down the precise mechanism and to quantify the proposed causal effect. This design allows for high internal validity at the cost of some external validity. Third, our participants come from one standard crowdsourcing platform; therefore, they possess certain characteristics, such as being a mostly White, English-speaking sample. Although our analysis controlled for individual-level covariates such as age, race, gender, and socioeconomic status, the generalizability of the findings to other samples remains an open question. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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