Section of Clinical and Computational Psychiatry, National Institute of Mental Health, National Institutes of Health, Bethesda, United States.
Machine Learning Team, National Institute of Mental Health, National Institutes of Health, Bethesda, United States.
Elife. 2021 Jun 15;10:e62051. doi: 10.7554/eLife.62051.
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.
人们在日常生活和临床交流中经常会提到自己的情绪状态。理论解释表明,当我们报告自己的情绪时,会整合我们的过往经历;然而,这种整合的时间结构仍未得到检验。在这里,我们使用一种计算方法来定量回答这个问题,并表明早期事件对报告的情绪(优先权重)的影响比近期事件更大。我们表明,与一系列替代的时间表示形式相比,优先模型能更好地解释情绪报告,这些替代的时间表示形式包括随机、一致或动态奖励环境、不同年龄组以及健康和抑郁参与者。此外,我们还发现了与情绪调节相关的额前脑区对优先模型而非近因模型进行神经编码的证据。这些发现对实验或临床环境中事件的时间安排具有重要意义,并为个性化情绪干预提供了新的方向。