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Syst. Neurosci."],"abstract":"<jats:p>Pupils can signify various internal processes and states, such as attention, arousal, and working memory. Changes in pupil size have been associated with learning speed, prediction of future events, and deviations from the prediction in human studies. However, the detailed relationships between pupil size changes and prediction are unclear. We explored pupil size dynamics in mice performing a Pavlovian delay conditioning task. A head-fixed experimental setup combined with deep-learning-based image analysis enabled us to reduce spontaneous locomotor activity and to track the precise dynamics of pupil size of behaving mice. By setting up two experimental groups, one for which mice were able to predict reward in the Pavlovian delay conditioning task and the other for which mice were not, we demonstrated that the pupil size of mice is modulated by reward prediction and consumption, as well as body movements, but not by unpredicted reward delivery. Furthermore, we clarified that pupil size is still modulated by reward prediction even after the disruption of body movements by intraperitoneal injection of haloperidol, a dopamine D2 receptor antagonist. These results suggest that changes in pupil size reflect reward prediction signals. Thus, we provide important evidence to reconsider the neuronal circuit involved in computing reward prediction error. This integrative approach of behavioral analysis, image analysis, pupillometry, and pharmacological manipulation will pave the way for understanding the psychological and neurobiological mechanisms of reward prediction and the prediction errors essential to learning and behavior.<\/jats:p>","DOI":"10.3389\/fnsys.2022.1045764","type":"journal-article","created":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T08:29:23Z","timestamp":1670488163000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Pupillary dynamics of mice performing a Pavlovian delay conditioning task reflect reward-predictive signals"],"prefix":"10.3389","volume":"16","author":[{"given":"Kota","family":"Yamada","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koji","family":"Toda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1082","DOI":"10.1038\/s41593-022-01109-2","article-title":"A gradual temporal shift of dopamine responses mirrors the progression of temporal difference error in machine learning","volume":"25","author":"Amo","year":"2022","journal-title":"Nat. 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