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bioRxiv · 10.1101/815944

Timing matters: primacy effects of experience on mood revealed using a novel Mood-Machine-Interface

Abstract

Humans refer to their own mood state regularly in day-to-day as well as in 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 the reported mood compared to recent events. We show that a Primacy model accounts better for mood reports compared to a range of alternative temporal representations, and replicate this result across random, consistent or dynamic structures of reward environments, age groups and 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. SignificanceHow we rate our own mood at any given moment is shaped by our experiences; but are the most recent experiences the most influential, as assumed by current theories? Using several sources of experimental data and mathematical modeling, we show that earlier experiences within a context are more influential than recent events, and replicate this finding across task environments, age groups, and in healthy and depressed participants. Additionally, we present neural evidence supporting this primacy model. Our findings show that delineating a temporal structure is crucial in modeling mood and this has key implications for its measurement and definition in both clinical and everyday settings.

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BibTeXRIS

Keren, H., Zheng, C., Jangraw, D., Chang, K., Vitale, A., Nielson, D. M., Rutledge, R., Pereira, F., Stringaris, A.. 2019-10-28. Timing matters: primacy effects of experience on mood revealed using a novel Mood-Machine-Interface. https://doi.org/10.1101/815944

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