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

Nat Commun. 2020 Jun 2;11(1):2757. doi: 10.1038/s41467-020-16196-7.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Bayes Theorem
  • Behavior, Animal / physiology
  • Choice Behavior / physiology*
  • Computational Biology
  • Decision Making / physiology*
  • Learning / physiology*
  • Memory / physiology*
  • Models, Theoretical
  • Psychomotor Performance / physiology
  • Rats
  • Reaction Time
  • Reinforcement, Psychology
  • Uncertainty