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

作者：André G. Mendonça, Jan Drugowitsch, M. Inês Vicente, Eric DeWitt, Alexandre Pouget, Zachary F. Mainen · 发表于：Nature Communications · 年份：2020 · DOI：10.1038/s41467-020-16196-7 · 被引用次数：64 · 研究领域：Olfactory and Sensory Function Studies、Visual perception and processing mechanisms、Neural dynamics and brain function

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.