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Biased evaluations emerge from inferring hidden causes



How do we evaluate a group of people after a few negative experiences with some members but mostly positive experiences otherwise? How do rare experiences influence our overall impression? We show that rare events may be overweighted due to normative inference of the hidden causes that are believed to generate the observed events. We propose a Bayesian inference model that organizes environmental statistics by combining similar events and separating outlying observations. Relying on the model’s inferred latent causes for group evaluation overweights rare or variable events. We tested the model’s predictions in eight experiments where participants observed a sequence of social or non-social behaviours and estimated their average. As predicted, estimates were biased toward sparse events when estimating after seeing all observations, but not when tracking a summary value as observations accrued. Our results suggest that biases in evaluation may arise from inferring the hidden causes of group members’ behaviours.

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Fig. 1: Hypothetical latent structure and experimental designs.
Fig. 2: Results of experiment 1A.
Fig. 3: Results of experiment 1B.
Fig. 4: Experimental design and results of experiment 2.

Data availability

The data that support the findings of this study are available at

Code availability

Custom code that supports the findings of this study is available from the corresponding author upon request.


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This work is supported by grant number W911NF-14-1-0101 from the Army Research Office and grant R01DA042065 from the National Institute of Drug Abuse. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors are grateful to S. DuBrow and A. Radulescu for comments on an earlier draft.

Author information




Y.S.S. and Y.N. designed the study. Y.S.S. ran the experiment. Y.S.S. and Y.N. analysed the data and wrote the manuscript.

Corresponding author

Correspondence to Yeon Soon Shin.

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The authors declare no competing interests.

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Peer review information Primary Handling Editors: Marike Schiffer; Mary-Elizabeth Sutherland.

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Supplementary Information

Supplementary Figs. 1–3.


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Shin, Y.S., Niv, Y. Biased evaluations emerge from inferring hidden causes. Nat Hum Behav (2021).

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