Timing matters: The temporal representation of experience in subjective mood reports

Abstract

Mood is thought to integrate across our experiences, yet we do not know how the relative timing of past events shapes how we feel in the moment. Here, we investigate the relationship between the timing of previous experiences and mood by combining a novel closed-loop mood controller alongside computational modelling and neural data. We first present the development of a Mood Machine Interface which allows us to individualize rewards in real-time in order to generate substantial mood transitions, across healthy as well as depressed, adolescents and adults. We then show that early-experiences have a larger effect on mood than recent ones, and that the longer one is exposed to a given context, the harder it is for new events to change mood. We find that ACC neural activity underlies the influence of early experiences on mood. This provides a neuro-computational account of mood regulation by early events and suggests new directions for individualized mood interventions.

Electrophysiological correlates of mood and reward dynamics in human adolescents

“…14 subjects were initially analyzed as an exploratory sample to inform the study hypotheses (as reported in our preregistration available at https://osf.io/djw8h), leaving a separate confirmatory sample of 40 subjects for analyses at the sensor level and a subsample of 37 subjects at the source level. The task consists of three blocks, where in each block a closed-loop mood controller delivers reward prediction errors to try to move the participant's mood to a target value (see (Keren et al, 2020) for details on the task design). The targets of the controller are to reach the highest (first block), lowest (second block), and then highest mood again (third block).…”

“…Participants rate their mood with a slider between a value of 0 and 100 (with 0 being the lowest and 100 being the highest) every 2 to 3 trials of the gambling task. Reward prediction error and expectation are estimated according to the primacy mood model proposed by (Keren et al, 2020) defining the mood at time ! as:…”

“…The model parameters M " , , β # and β + are derived by fitting the self-reported mood to the mood model with python's TensorFlow package as described in (Keren et al, 2020).…”

“…Humans report on their moods in their everyday conversations and subjective mood reports form the basis of clinical assessment and much of research in affective neuroscience. Advances in computational modeling lend support to the idea that mood is intricately linked with reward processing and that it serves to integrates over a person's history of rewards and punishers (Keren et al, 2020;Nettle and Bateson, 2012;Rutledge et al, 2014). Yet, despite its ubiquity and importance, the brain mechanisms underlying mood and its relationship with changes in reward contingencies in the environment are surprisingly understudied.…”

“…In the exploratory analysis of (n= 14, age 16.22 years, described in supplementary materials), we collected MEG data with a monetary gambling task developed by (Keren et al, 2020) to parametrically shift mood by manipulating reward prediction errors (RPEs) and tested with a linear mixed effect model whether brain oscillations and trial-level changes in evoked responses would show a relationship with mood or its model components.…”