Abstract

People form moral impressions rapidly, effortlessly and from a remarkably young age1,2,3,4,5. Putatively ‘bad’ agents command more attention and are identified more quickly and accurately than benign or friendly agents5,6,7,8,9,10,11,12. Such vigilance is adaptive, but can also be costly in environments where people sometimes make mistakes, because incorrectly attributing bad character to good people damages existing relationships and discourages forming new relationships13,14,15,16. The ability to accurately infer the moral character of others is critical for healthy social functioning, but the computational processes that support this ability are not well understood. Here, we show that moral inference is explained by an asymmetric Bayesian updating mechanism in which beliefs about the morality of bad agents are more uncertain (and therefore more volatile) than beliefs about the morality of good agents. This asymmetry seems to be a property of learning about immoral agents in general, as we also find greater uncertainty for beliefs about the non-moral traits of bad agents. Our model and data reveal a cognitive mechanism that permits flexible updating of beliefs about potentially threatening others, a mechanism that could facilitate forgiveness when initial bad impressions turn out to be inaccurate. Our findings suggest that negative moral impressions destabilize beliefs about others, promoting cognitive flexibility in the service of cooperative but cautious behaviour.

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The data that support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

We thank D. Carlston, E. Boorman, C. Summerfield and T. Behrens for helpful feedback. We thank T. Tyurkina and L. Caviola for developing the web applications utilized in studies 2–7 for data collection. J.Z.S. was supported by a Clarendon and Wellcome Trust Society and Ethics award (104980/Z/14/Z). R.B.R. was supported by a MRC Career Development award (MR/N02401X/1). This work was supported by a Wellcome Trust ISSF award (204826/Z/16/Z), the John Fell Fund and the Academy of Medical Sciences (SBF001/1008). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Affiliations

  1. Department of Experimental Psychology, University of Oxford, Oxford, UK

    • Jenifer Z. Siegel
    •  & Molly J. Crockett
  2. Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy

    • Christoph Mathys
  3. Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK

    • Christoph Mathys
    •  & Robb B. Rutledge
  4. Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland

    • Christoph Mathys
  5. Wellcome Trust Centre for Neuroimaging, University College London, London, UK

    • Robb B. Rutledge
  6. Department of Psychology, Yale University, New Haven, CT, USA

    • Molly J. Crockett

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Contributions

M.J.C. and J.Z.S. conceived the studies. J.Z.S., C.M., R.B.R. and M.J.C. designed the studies. J.Z.S. collected the data. J.Z.S., C.M. and M.J.C. analysed the data. J.Z.S. and M.J.C. wrote the manuscript with edits from R.B.R. and C.M.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Molly J. Crockett.

Supplementary information

  1. Supplementary Information

    Supplementary Methods, Supplementary Results, Supplementary Tables 2–4, 6–12 and Supplementary Figures 1–7

  2. Reporting Summary

  3. Supplementary Table 1

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DOI

https://doi.org/10.1038/s41562-018-0425-1

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