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

• Published in: Nature communications Vol. 11; no. 1; p. 2757
• Main Authors: Mendonça, André G., Drugowitsch, Jan, Vicente, M. Inês, DeWitt, Eric E. J., Pouget, Alexandre, Mainen, Zachary F.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02-06-2020; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378/116; 631/378/1595; 631/378/2624; 631/378/3917; 64/86; Accumulation; Accuracy; Animals; Bayes Theorem; Bayesian analysis; Behavior, Animal - physiology; Choice Behavior - physiology; Computational Biology; Decision making; Decision Making - physiology; Humanities and Social Sciences; Learning; Learning - physiology; Machine learning; Mathematical models; Memory - physiology; Mental task performance; Model accuracy; Models, Theoretical; multidisciplinary; Psychomotor Performance - physiology; Rats; Reaction Time; Reinforcement; Reinforcement, Psychology; Science; Science (multidisciplinary); Tradeoffs; Uncertainty
• ISSN: 2041-1723; 2041-1723
• 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.
Here, the authors show that rats’ performance on olfactory decision tasks is best explained by a Bayesian model that combines reinforcement-based learning with accumulation of uncertain sensory evidence. The results suggest that learning is a critical factor contributing to speed-accuracy tradeoffs.