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A confirmation bias in perceptual decision-making due to hierarchical approximate inference.

Publication ,  Journal Article
Lange, RD; Chattoraj, A; Beck, JM; Yates, JL; Haefner, RM
Published in: PLoS computational biology
November 2021

Making good decisions requires updating beliefs according to new evidence. This is a dynamical process that is prone to biases: in some cases, beliefs become entrenched and resistant to new evidence (leading to primacy effects), while in other cases, beliefs fade over time and rely primarily on later evidence (leading to recency effects). How and why either type of bias dominates in a given context is an important open question. Here, we study this question in classic perceptual decision-making tasks, where, puzzlingly, previous empirical studies differ in the kinds of biases they observe, ranging from primacy to recency, despite seemingly equivalent tasks. We present a new model, based on hierarchical approximate inference and derived from normative principles, that not only explains both primacy and recency effects in existing studies, but also predicts how the type of bias should depend on the statistics of stimuli in a given task. We verify this prediction in a novel visual discrimination task with human observers, finding that each observer's temporal bias changed as the result of changing the key stimulus statistics identified by our model. The key dynamic that leads to a primacy bias in our model is an overweighting of new sensory information that agrees with the observer's existing belief-a type of 'confirmation bias'. By fitting an extended drift-diffusion model to our data we rule out an alternative explanation for primacy effects due to bounded integration. Taken together, our results resolve a major discrepancy among existing perceptual decision-making studies, and suggest that a key source of bias in human decision-making is approximate hierarchical inference.

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Published In

PLoS computational biology

DOI

EISSN

1553-7358

ISSN

1553-734X

Publication Date

November 2021

Volume

17

Issue

11

Start / End Page

e1009517

Related Subject Headings

  • Perception
  • Models, Psychological
  • Humans
  • Decision Making
  • Bioinformatics
  • Bias
  • 08 Information and Computing Sciences
  • 06 Biological Sciences
  • 01 Mathematical Sciences
 

Citation

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Lange, R. D., Chattoraj, A., Beck, J. M., Yates, J. L., & Haefner, R. M. (2021). A confirmation bias in perceptual decision-making due to hierarchical approximate inference. PLoS Computational Biology, 17(11), e1009517. https://doi.org/10.1371/journal.pcbi.1009517
Lange, Richard D., Ankani Chattoraj, Jeffrey M. Beck, Jacob L. Yates, and Ralf M. Haefner. “A confirmation bias in perceptual decision-making due to hierarchical approximate inference.PLoS Computational Biology 17, no. 11 (November 2021): e1009517. https://doi.org/10.1371/journal.pcbi.1009517.
Lange RD, Chattoraj A, Beck JM, Yates JL, Haefner RM. A confirmation bias in perceptual decision-making due to hierarchical approximate inference. PLoS computational biology. 2021 Nov;17(11):e1009517.
Lange, Richard D., et al. “A confirmation bias in perceptual decision-making due to hierarchical approximate inference.PLoS Computational Biology, vol. 17, no. 11, Nov. 2021, p. e1009517. Epmc, doi:10.1371/journal.pcbi.1009517.
Lange RD, Chattoraj A, Beck JM, Yates JL, Haefner RM. A confirmation bias in perceptual decision-making due to hierarchical approximate inference. PLoS computational biology. 2021 Nov;17(11):e1009517.

Published In

PLoS computational biology

DOI

EISSN

1553-7358

ISSN

1553-734X

Publication Date

November 2021

Volume

17

Issue

11

Start / End Page

e1009517

Related Subject Headings

  • Perception
  • Models, Psychological
  • Humans
  • Decision Making
  • Bioinformatics
  • Bias
  • 08 Information and Computing Sciences
  • 06 Biological Sciences
  • 01 Mathematical Sciences