Learning Behavior Differs Between OCD and Problem Gambling

Summary: When the reward is less than expected, people with OCD exhibit lower-than-normal learning rates. Conversely, those with gambling addictions exhibit boosted and blunted patterns of learning when the rewards are higher and lower than expected.

Source: PLOS

Shinsuke Suzuki at The University of Melbourne, Australia reports distinct patterns of reward-seeking behavior between obsessive compulsive disorder (OCD) and problem gambling, in a study published in PLOS Biology.

OCD is associated with lower-than-normal learning rates when rewards are less than expected. On the other hand, people with problem gambling exhibit boosted and blunted learning from rewards higher and lower than expected, respectively.

Understanding the differences between obsessive and addictive behaviors is essential for developing treatments for conditions like problem gambling and OCD. Although these conditions share characteristics such as behavioral inflexibility, their distinctness might be related to differences in how reward-based learning is processed in the brain.

The group of researchers approached this problem by modeling learning behavior and its associated brain activity. Healthy controls and people with either OCD or problem gambling performed a reinforcement-learning task while their brain activity was recorded with fMRI. The analysis focused on how each group learned when the outcomes differed from what they expected, a measure known as prediction error.

Behaviorally, the researchers found that when seeking rewards, people with OCD did not learn as well as controls when the rewards were less than expected. This was reflected by lower-than-normal negative prediction errors encoded in the dorsomedial prefrontal cortex and dorsal striatum.

In addition to this kind of “under-learning,” people with problem gambling also displayed “over-learning” when the rewards were higher than expected.

For these individuals, activity in the anterior insula reflected the higher-than-normal positive prediction errors. In contrast to these differences in reward-seeking behavior, neither group differed from controls when asked to avoid undesirable outcomes.

The study highlights the benefits of using a neurocomputational approach to studying psychiatric disorders. By teasing apart differences in positive/negative reinforcement learning, this approach can help discern subtle differences between conditions, which could point toward different treatment approaches.

Suzuki adds, “Individuals with problem gambling and obsessive-compulsive disorder show distinct patterns of learning from better- and worse-than-expected outcomes.”

Funding: This work was supported by the National Health and Medical Research Council of Australia (APP236175 to M.Y.). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Abstract

Individuals with problem gambling and obsessive-compulsive disorder learn through distinct reinforcement mechanisms

Obsessive-compulsive disorder (OCD) and pathological gambling (PG) are accompanied by deficits in behavioural flexibility. In reinforcement learning, this inflexibility can reflect asymmetric learning from outcomes above and below expectations. In alternative frameworks, it reflects perseveration independent of learning.

Here, we examine evidence for asymmetric reward-learning in OCD and PG by leveraging model-based functional magnetic resonance imaging (fMRI).

Compared with healthy controls (HC), OCD patients exhibited a lower learning rate for worse-than-expected outcomes, which was associated with the attenuated encoding of negative reward prediction errors in the dorsomedial prefrontal cortex and the dorsal striatum.

PG patients showed higher and lower learning rates for better- and worse-than-expected outcomes, respectively, accompanied by higher encoding of positive reward prediction errors in the anterior insula than HC. Perseveration did not differ considerably between the patient groups and HC.

These findings elucidate the neural computations of reward-learning that are altered in OCD and PG, providing a potential account of behavioural inflexibility in those mental disorders.