Cardiac Cycle Affects the Asymmetric Value Updating in Instrumental Reward Learning

Abstract

This study aimed to investigate whether instrumental reward learning is affected by the cardiac cycle. To this end, we examined the effects of the cardiac cycle (systole or diastole) on the computational processes underlying the participants’ choices in the instrumental learning task. In the instrumental learning task, participants were required to select one of two discriminative stimuli (neutral visual stimuli) and immediately receive reward/punishment feedback depending on the probability assigned to the chosen stimuli. To manipulate the cardiac cycle, the presentation of discriminative stimuli was timed to coincide with either cardiac systole or diastole. We fitted the participants’ choices in the task with reinforcement learning (RL) models and estimated parameters involving instrumental learning (i.e., learning rate and inverse temperature) separately in the systole and diastole trials. Model-based analysis revealed that the learning rate for positive prediction errors was higher than that for negative prediction errors in the systole trials; however, learning rates did not differ between positive and negative prediction errors in the diastole trials. These results demonstrate that the natural fluctuation of cardiac afferent signals can affect asymmetric value updating in instrumental reward learning.

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“…Their model shows that animals stabilize their internal state around the ideal value by simply learning to perform behaviors that lead to rewarding outcomes. Indeed, HEP amplitude in the vmPFC is strongly correlated with preference value decision-making (Azzalini et al, 2021 ), and cardiac afferent signals, only in the systole phase but not in the diastole phase, enhance asymmetric value updating based on reinforcement learning (Kimura et al, 2022 ).…”