Neurocomputational mechanisms underlying differential reinforcement learning from wins and losses in obesity with and without binge eating.

ABSTRACT

BACKGROUND: Binge Eating Disorder (BED) is thought of as a disorder of cognitive control but evidence regarding its neurocognitive mechanisms is inconclusive. Key limitations in prior research are a lack of consistent separation between effects of BED and obesity, and a disregard for self-report evidence suggesting that neurocognitive alterations may emerge primarily in loss- or harm-avoidance contexts. METHODS: Addressing these gaps, this longitudinal study investigated behavioral flexibility and its underlying neuro-computational processes in reward-seeking and loss-avoidance contexts. Obese participants with BED (BED), without BED (OB), and healthy normal-weight participants (NW) (Ntotal=96) performed a probabilistic reversal learning task during functional imaging, with different blocks focused on obtaining wins or avoiding losses. They were reinvited for a 6-months follow-up. RESULTS: Analyses informed by computational models of reinforcement learning showed that unlike BED, OB performed worse in the win than the loss condition. Computationally, this was explained by differential learning sensitivities in the win vs loss conditions between groups. In the brain, this was echoed in differential neural learning signals in the ventromedial prefrontal cortex (vmPFC) per condition. The differences were subtle, but scaled with BED symptoms, such that more severe BED symptoms were associated with increasing bias towards improved learning from wins vs losses. Across conditions, OB switched more between choice options than NW. This was reflected in diminished representation of choice certainty in the vmPFC. CONCLUSIONS: Our study highlights the importance of distinguishing between obesity with and without BED to identify unique neuro-computational alterations underlying different styles of maladaptive eating behavior.