AUTHOR=Aberg Kristoffer C. , Paz Rony 

TITLE=Average reward rates enable motivational transfer across independent reinforcement learning tasks

JOURNAL=Frontiers in Behavioral Neuroscience

VOLUME=16

YEAR=2022

URL=https://www.frontiersin.org/journals/behavioral-neuroscience/articles/10.3389/fnbeh.2022.1041566

DOI=10.3389/fnbeh.2022.1041566

ISSN=1662-5153

ABSTRACT=<p>Outcomes and feedbacks on performance may influence behavior beyond the context in which it was received, yet it remains unclear what neurobehavioral mechanisms may account for such lingering influences on behavior. The average reward rate (ARR) has been suggested to regulate motivated behavior, and was found to interact with dopamine-sensitive cognitive processes, such as vigilance and associative memory encoding. The ARR could therefore provide a bridge between independent tasks when these are performed in temporal proximity, such that the reward rate obtained in one task could influence performance in a second subsequent task. Reinforcement learning depends on the coding of prediction error signals by dopamine neurons and their downstream targets, in particular the nucleus accumbens. Because these brain regions also respond to changes in ARR, reinforcement learning may be vulnerable to changes in ARR. To test this hypothesis, we designed a novel paradigm in which participants (<italic>n</italic> = 245) performed two probabilistic reinforcement learning tasks presented in interleaved trials. The ARR was controlled by an “induction” task which provided feedback with a low (<italic>p</italic> = 0.58), a medium (<italic>p</italic> = 0.75), or a high probability of reward (<italic>p</italic> = 0.92), while the impact of ARR on reinforcement learning was tested by a second “reference” task with a constant reward probability (<italic>p</italic> = 0.75). We find that performance was significantly lower in the reference task when the induction task provided low reward probabilities (i.e., during low levels of ARR), as compared to the medium and high ARR conditions. Behavioral modeling further revealed that the influence of ARR is best described by models which accumulates average rewards (rather than average prediction errors), and where the ARR directly modulates the prediction error signal (rather than affecting learning rates or exploration). Our results demonstrate how affective information in one domain may transfer and affect motivated behavior in other domains. These findings are particularly relevant for understanding mood disorders, but may also inform abnormal behaviors attributed to dopamine dysfunction.</p>