Department of Psychology, Yale University, New Haven, United States; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom; Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
Francisco Pereira
Machine Learning Team, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
Dylan M Nielson
Section of Clinical and Computational Psychiatry, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
Argyris Stringaris
Section of Clinical and Computational Psychiatry, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
Humans refer to their mood state regularly in day-to-day as well as clinical interactions. 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.
