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The impact of learning on perceptual decisions and its implication for speed-accuracy tradeoffs

Author

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  • André G. Mendonça

    (Champalimaud Centre for the Unknown)

  • Jan Drugowitsch

    (Harvard Medical School)

  • M. Inês Vicente

    (Champalimaud Centre for the Unknown)

  • Eric E. J. DeWitt

    (Champalimaud Centre for the Unknown)

  • Alexandre Pouget

    (University of Geneva)

  • Zachary F. Mainen

    (Champalimaud Centre for the Unknown)

Abstract

In standard models of perceptual decision-making, noisy sensory evidence is considered to be the primary source of choice errors and the accumulation of evidence needed to overcome this noise gives rise to speed-accuracy tradeoffs. Here, we investigated how the history of recent choices and their outcomes interact with these processes using a combination of theory and experiment. We found that the speed and accuracy of performance of rats on olfactory decision tasks could be best explained by a Bayesian model that combines reinforcement-based learning with accumulation of uncertain sensory evidence. This model predicted the specific pattern of trial history effects that were found in the data. The results suggest that learning is a critical factor contributing to speed-accuracy tradeoffs in decision-making, and that task history effects are not simply biases but rather the signatures of an optimal learning strategy.

Suggested Citation

  • André G. Mendonça & Jan Drugowitsch & M. Inês Vicente & Eric E. J. DeWitt & Alexandre Pouget & Zachary F. Mainen, 2020. "The impact of learning on perceptual decisions and its implication for speed-accuracy tradeoffs," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16196-7
    DOI: 10.1038/s41467-020-16196-7
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