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Cited by 264

The DOI matches its Crossref record; 87 of the 87 references it cites were found in a registry or an index.

Accurate identification of fear facial expressions predicts prosocial behavior.

Open access
Marsh, Abigail A., Kozak, Megan N., Ambady, Nalini·2007·Emotion

Abstract

The fear facial expression is a distress cue that is associated with the provision of help and prosocial behavior. Prior psychiatric studies have found deficits in the recognition of this expression by individuals with antisocial tendencies. However, no prior study has shown accuracy for recognition of fear to predict actual prosocial or antisocial behavior in an experimental setting. In 3 studies, the authors tested the prediction that individuals who recognize fear more accurately will behave more prosocially. In Study 1, participants who identified fear more accurately also donated more money and time to a victim in a classic altruism paradigm. In Studies 2 and 3, participants' ability to identify the fear expression predicted prosocial behavior in a novel task designed to control for confounding variables. In Study 3, accuracy for recognizing fear proved a better predictor of prosocial behavior than gender, mood, or scores on an empathy scale.

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Crossref · 10.1037/1528-3542.7.2.239

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Metadata

Authors
Marsh, A. A., Kozak, M. N., & Ambady, N.
Venue
Emotion
Published
2007
Volume
7
Issue
2
Pages
239-251
ISSN
  • 1528-3542 (ISSN-L)
  • 1931-1516
OpenAlex
W2103903664
Cited by
264
References
87
FWCI
3.56
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Accurate identification of fear facial expressions predicts prosocial behavior. https://app.cochat.ai/papers/10.1037/1528-3542.7.2.239
The paper
Marsh, A. A., Kozak, M. N., & Ambady, N. (2007). Accurate identification of fear facial expressions predicts prosocial behavior. Emotion. https://doi.org/10.1037/1528-3542.7.2.239

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.