Individual differences in emotional learning underpinning vulnerabilities to compulsive reward-seeking behaviors

Dr. Eva Pool - FNS Ambizione

Research question: A variety of psychological disorders (e.g., binge eating, substance use disorders) are characterized by situations where the individual seeks a reward (e.g., food, drug), even though once they obtain the reward they do not experience it as very pleasant. Strikingly, very large individual differences exist when it comes to the development of these compulsive reward-seeking behaviors. Here, we aim at a better understanding of why some individuals are more vulnerable than others to situations where the choice-behavior is hijacked in the service of outcomes that are no longer valued. More specifically, this project tests whether some specific individual differences in emotional learning (i.e., a particular way in which an individual learns to assign and update affective value) represent a vulnerability to compulsive reward-seeking behaviors. An animal model has proposed that a key factor associated with vulnerability to compulsive substance-seeking behaviors relies on the individual’s tendency to preferentially interact with the cues that predict the reward (i.e., sign-tracking) rather than the location of the reward delivery (i.e., goal-tracking). Computational explanations have been developed to account for the behavioral components of sign- versus goal-tracking, and suggests that frontostriatal network and dopaminergic signals underlie the individual differences associated with compulsive reward-seeking behaviors. This project aim at testing whether this animal model can be translated to a human population so that it could be used as a framework to identify risk profiles.

Methods: This project is divided in three work-packages, each one with a specific aim: (1) testing how individual differences in emotional learning predict compulsive reward-seeking behaviors; (2) investigating the neuro-computational mechanisms underlying these individual differences; (3) assessing how reliable the measures of individual differences in emotional learning tasks are over time. To fulfill these objectives, the project proposes to use an innovative longitudinal approach leveraging a combination of fMRI techniques involving computational modeling analyses, along with emotional learning tasks involving primary rewards. A large sample of participants will be tested at time one, using behavioral and eyetracking techniques to measure individual differences in emotional learning. Additionally, we will assess a large variety of problematic reward-seeking behaviors (e.g., drug, binge eating, problematic use of the phone, gambling) through questionnaires. Participants being very high or very low on sign versus goal tracker dimension will take part in an fMRI experiment at time two, using computational model analyses and methods involving primary rewards. After the fMRI experiment they will perform the emotional learning task a second time to measure the reliability over time of the individual differences.

Implications: The outcome of this project could have a high impact: the question of why some individuals are more vulnerable to pursue outcomes that are not longer valued is of great interest and relevance for several disciplines such as psychology, economics, neuroscience, and psychiatry. Understanding the mechanisms underpinning these behaviors could foster insights into human decision-making at a fundamental level. This may further contribute to unravelling why very often we observe behaviors that appear to be irrational. This line of research could have important clinical implications as it is becoming increasingly recognized that individual differences should be exploited -rather than being ignored - for the identification of individual risk profiles for disorders characterized by compulsive reward-seeking behaviors (e.g., substance use disorder, binge eating). This aid in informing the development of personalized evidence-based treatments and prevention strategies targeting specific neuro-computational mechanisms rather than diagnostic categories.

Team