Effort Reinforces Learning

Speaker: Huw Jarvis

30th July 2020


Humans routinely learn the value of actions by assessing their outcomes. Critically, actions also require effort. While effort is known to affect the valuation of rewards, how effort modulates reward-based learning remains unclear. Here, we applied a reinforcement learning paradigm in which individuals (N = 213) had to exert predefined levels of force to register their responses. Our key finding was that greater exertion of effort boosts teaching signals that are positive, but blunts those that are negative. Moreover, the extent to which effort reinforced learning for a given individual was proportional to their aversion to effort. This suggests that the same computation that discounts value before choice serves to reinforce learning after that choice is made. Overall, our findings are consistent with growing evidence that motivation and learning operate within a common computational framework, and refine current reinforcement learning models.

Speaker Bio

Huw Jarvis is a PhD student in the Cognitive Neurology Lab at the Turner Institute for Brain and Mental Health at Monash University. Prior to his PhD, he completed studies in medical science (University of Tasmania) and public health (University of Melbourne), and worked in research translation at the National Health and Medical Research Council. Huw is undertaking a PhD to investigate the computational underpinnings of motivation and reward-based learning, with a view to better understanding related impairments in neurological and psychiatric disease. He was recently awarded a Fulbright Future Scholarship to pursue related work at Yale University.

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