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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.<\/jats:p>","DOI":"10.1038\/s41467-020-16196-7","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T06:05:50Z","timestamp":1591077950000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["The impact of learning on perceptual decisions and its implication for speed-accuracy tradeoffs"],"prefix":"10.1038","volume":"11","author":[{"given":"Andr\u00e9 G.","family":"Mendon\u00e7a","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7846-0408","authenticated-orcid":false,"given":"Jan","family":"Drugowitsch","sequence":"additional","affiliation":[]},{"given":"M. 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