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Efficient stabilization of imprecise statistical inference through conditional belief updating

Nature Human Behaviour volume 6, pages 1691–1704 (2022)Cite this article

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Abstract

Statistical inference is the optimal process for forming and maintaining accurate beliefs about uncertain environments. However, human inference comes with costs due to its associated biases and limited precision. Indeed, biased or imprecise inference can trigger variable beliefs and unwarranted changes in behaviour. Here, by studying decisions in a sequential categorization task based on noisy visual stimuli, we obtained converging evidence that humans reduce the variability of their beliefs by updating them only when the reliability of incoming sensory information is judged as sufficiently strong. Instead of integrating the evidence provided by all stimuli, participants actively discarded as much as a third of stimuli. This conditional belief updating strategy shows good test–retest reliability, correlates with perceptual confidence and explains human behaviour better than previously described strategies. This seemingly suboptimal strategy not only reduces the costs of imprecise computations but also, counterintuitively, increases the accuracy of resulting decisions.

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Fig. 1: Description of experiment 1 (n = 30).
Fig. 2: Candidate response stabilization strategies.
Fig. 3: Predicted effects of response stabilization strategies.
Fig. 4: Belief stabilization through conditional inference (n = 30).
Fig. 5: Description of experiment 2 (n = 30).
Fig. 6: Validation of specific predictions of conditional inference (n = 30).
Fig. 7: Interindividual variability in conditional inference (n = 60).
Fig. 8: Increased decision accuracy through conditional inference.

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Data availability

The datasets generated during and analysed during the current study are freely available online on figshare: https://figshare.com/projects/Efficient_stabilization_of_imprecise_statistical_inference_through_conditional_belief_updating/140170.Source data are provided with this paper.

Code availability

The analysis code supporting the reported findings is freely available online on github: https://github.com/juliedrevet/CONDINF.

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Acknowledgements

We thank B. De Martino and M. Usher for their insightful comments and suggestions during peer review. This work was supported by the European Research Council (starting grant No. ERC-StG-759341 to V.W.), the National Institute of Mental Health (US–France collaborative research grant No. 1R01MH115554-01 to J.Drugowitsch and V.W.) and the Agence Nationale de la Recherche (grant No. ANR-17-NEUC-0001-02 to J.Drugowitsch and V.W., and a department-wide grant No. ANR-17-EURE-0017). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France

    Julie Drevet & Valentin Wyart

  2. Département d’Études Cognitives, École Normale Supérieure, Université PSL, Paris, France

    Julie Drevet & Valentin Wyart

  3. Department of Neurobiology, Harvard Medical School, Boston, MA, USA

    Jan Drugowitsch

Authors
  1. Julie Drevet
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  3. Valentin Wyart
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Contributions

J.Drevet contributed to conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft, writing—review and editing, and visualization. J.Drugowitsch contributed to conceptualization, methodology, writing—review and editing, supervision, project administration and funding acquisition. V.W. contributed to conceptualization, methodology, software, validation, formal analysis, resources, writing—original draft, writing—review and editing, visualization, supervision, project administration and funding acquisition.

Corresponding authors

Correspondence to Julie Drevet or Valentin Wyart.

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The authors declare no competing interests.

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Nature Human Behaviour thanks Benedetto De Martino and Marius Usher for their contribution to the peer review of this work. Peer reviewer reports are available.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–12.

Reporting Summary

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Supplementary Data 1

Source data for Supplementary Fig. 1.

Supplementary Data 2

Source data for Supplementary Fig. 2.

Supplementary Data 3

Source data for Supplementary Fig. 3.

Supplementary Data 4

Source data for Supplementary Fig. 4.

Supplementary Data 5

Source data for Supplementary Fig. 5.

Supplementary Data 6

Source data for Supplementary Fig. 6.

Supplementary Data 7

Source data for Supplementary Fig. 7.

Supplementary Data 8

Source data for Supplementary Fig. 8.

Supplementary Data 9

Source data for Supplementary Fig. 9.

Supplementary Data 10

Source data for Supplementary Fig. 10.

Supplementary Data 11

Source data for Supplementary Fig. 11.

Source data

Source Data Fig. 1

Psychometrics curves, reversal curves, switch curves and overall metrics.

Source Data Fig. 3

Predicted switch curves, predicted reversal curves and predicted before after reversal.

Source Data Fig. 4

Ex ante recovery matrix, Bayesian model selection, simulated switch curves and simulated reversal curves.

Source Data Fig. 5

Luminance accuracy, luminance confidence, reversal curves, switch curves and overall metrics.

Source Data Fig. 6

Independent reliability thresholds, decreasing discard rates, regression confidence reliability and variance explained.

Source Data Fig. 7

Model parameters, gradient hazard rate, gradient inference noise and gradient reliability threshold.

Source Data Fig. 8

Simulated accuracy and effects accuracy parameters.

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Drevet, J., Drugowitsch, J. & Wyart, V. Efficient stabilization of imprecise statistical inference through conditional belief updating. Nat Hum Behav 6, 1691–1704 (2022). https://doi.org/10.1038/s41562-022-01445-0

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