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Theoretical accounts suggest that when reporting on our mood we integrate over the history of our experiences; yet, the temporal structure of this integration remains unexamined. Here, we use a computational approach to quantitatively answer this question and show that early events exert a stronger influence on reported mood (a primacy weighting) compared to recent events. We show that a Primacy model accounts better for mood reports compared to a range of alternative temporal representations across random, consistent, or dynamic reward environments, different age groups, and in both healthy and depressed participants. Moreover, we find evidence for neural encoding of the Primacy, but not the Recency, model in frontal brain regions related to mood regulation. These findings hold implications for the timing of events in experimental or clinical settings and suggest new directions for individualized mood interventions.<\/jats:p>","DOI":"10.7554\/elife.62051","type":"journal-article","created":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T20:00:24Z","timestamp":1623700824000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.62051","source":"Crossref","is-referenced-by-count":26,"title":["The temporal representation of experience in subjective mood"],"prefix":"10.7554","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4122-656X","authenticated-orcid":true,"given":"Hanna","family":"Keren","sequence":"first","affiliation":[{"name":"Section of Clinical and Computational Psychiatry, National Institute of Mental Health, National Institutes of Health, Bethesda, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles","family":"Zheng","sequence":"additional","affiliation":[{"name":"Machine Learning 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