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

André G. Mendonça, Jan Drugowitsch, M. Inês Vicente, Eric E. J. DeWitt, Alexandre Pouget and Zachary F. Mainen ()
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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

Nature Communications, 2020, vol. 11, issue 1, 1-15

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-16196-7

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