Altered Reinforcement Learning from Reward and Punishment in Anorexia Nervosa: Evidence from Computational Modeling

Anorexia nervosa (AN) is associated with altered sensitivity to reward and punishment. Few studies have investigated whether this results in aberrant learning. The ability to learn from rewarding and aversive experiences is essential for flexibly adapting to changing environments, yet individuals with AN tend to demonstrate cognitive inflexibility, difficulty set-shifting and altered decision-making. Deficient reinforcement learning may contribute to repeated engagement in maladaptive behavior.

This study investigated learning in AN using a probabilistic associative learning task that separated learning of stimuli via reward from learning via punishment. Forty-two individuals with Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 restricting-type AN were compared to 38 healthy controls (HCs). We applied computational models of reinforcement learning to assess group differences in learning, thought to be driven by violations in expectations, or prediction errors (PEs). Linear regression analyses examined whether learning parameters predicted BMI at discharge.

AN had lower learning rates than HC following both positive and negative PE (p < .02), and were less likely to exploit what they had learned. Negative PE on punishment trials predicted lower discharge BMI (p < .001), suggesting individuals with more negative expectancies about avoiding punishment had the poorest outcome.

This is the first study to show lower rates of learning in AN following both positive and negative outcomes, with worse punishment learning predicting less weight gain. An inability to modify expectations about avoiding punishment might explain persistence of restricted eating despite negative consequences, and suggests that treatments that modify negative expectancy might be effective in reducing food avoidance in AN.