Reliance on model-based and model-free control in obesity

Abstract

Consuming more energy than is expended may reflect a failure of control over eating behaviour in obesity. Behavioural control arises from a balance between two dissociable strategies of reinforcement learning: model-free and model-based. We hypothesized that weight status relates to an imbalance in reliance on model-based and model-free control, and that it may do so in a linear or quadratic manner. To test this, 90 healthy participants in a wide BMI range [normal-weight (n = 31), overweight (n = 29), obese (n = 30)] performed a sequential decision-making task. The primary analysis indicated that obese participants relied less on model-based control than overweight and normal-weight participants, with no difference between overweight and normal-weight participants. In line, secondary continuous analyses revealed a negative linear, but not quadratic, relationship between BMI and model-based control. Computational modelling of choice behaviour suggested that a mixture of both strategies was shifted towards less model-based control in obese participants. Our findings suggest that obesity may indeed be related to an imbalance in behavioural control as expressed in a phenotype of less model-based control potentially resulting from enhanced reliance on model-free computations.

Greater reliance on model-free learning in adolescent anorexia nervosa: An examination of dual-system reinforcement learning

“…While behavioral patterns present for AN-R are relatively consistent with prior work in psychiatric samples 25,37–39,41,44 , the lack of difference between AN-BP and HC was unexpected. A prior study showed that AN displayed attenuated model-based learning compared to HC, with this effect being largely driven by AN-BP 41 .…”

“…Reinforcement Learning in Anorexia Nervosa Across psychiatric conditions, there is emerging evidence suggesting that disorders of compulsivity (e.g., obsessive-compulsive disorder, substance use) demonstrate deficits in model-based learning during sequential decision-making 25,[37][38][39][40] . Critically, prior work examining model-based and model-free learning in a mixture of community and clinical samples endorsing symptoms of eating pathology (e.g., compulsive eating), also demonstrate attenuation in modelbased learning 25,[37][38][39]41 . Only one such study was conducted in a sample of individuals with fullthreshold AN and found evidence of deficits in model-based learning for food and money that were not remediated at weight restoration, reflecting a potential trait behavioral marker of the disorder.…”

Food reinforcement architecture: A framework for impulsive and compulsive overeating and food abuse

“…Propensity for the habit‐based control system increases an individual's risk for excessive substance use/abuse [72, 73, 110–112], as well as obesity [73–78, 110]. As already discussed, habit‐based control involves slower or no updating of choices when changes in reinforcement occur.…”

“…Importantly, obesity is characterized by altered dorsal and ventral striatal functioning and an insensitivity to prediction errors [85]. This is not surprising considering the relation between propensity for habit‐based control and obesity [73, 75–77], as habit‐based control is largely insensitive to prediction error.…”

“…State prediction error is a highly flexible, prospective learning approach relative to habit-based control and reward prediction error. Propensity for the habit-based over the goal-directed control system has been proposed to underlie addiction and obesity [73][74][75][76][77][78]. This is primarily due to the reflexive nature of this system and the inability of the habit-based control system to be updated by changing reinforcement structures.…”