Testing the role of reward and punishment sensitivity in avoidance behavior: a computational modeling approach

Abstract

Exaggerated avoidance behavior is a predominant symptom in all anxiety disorders and its degree often parallels the development and persistence of these conditions. Both human and non-human animal studies suggest that individual differences as well as various contextual cues may impact avoidance behavior. Specifically, we have recently shown that female sex and inhibited temperament, two anxiety vulnerability factors, are associated with greater duration and rate of the avoidance behavior, as demonstrated on a computer-based task closely related to common rodent avoidance paradigms. We have also demonstrated that avoidance is attenuated by the administration of explicit visual signals during "non-threat" periods (i.e., safety signals). Here, we use a reinforcement-learning network model to investigate the underlying mechanisms of these empirical findings, with a special focus on distinct reward and punishment sensitivities. Model simulations suggest that sex and inhibited temperament are associated with specific aspects of these sensitivities. Specifically, differences in relative sensitivity to reward and punishment might underlie the longer avoidance duration demonstrated by females, whereas higher sensitivity to punishment might underlie the higher avoidance rate demonstrated by inhibited individuals. Simulations also suggest that safety signals attenuate avoidance behavior by strengthening the competing approach response. Lastly, several predictions generated by the model suggest that extinction-based cognitive-behavioral therapies might benefit from the use of safety signals, especially if given to individuals with high reward sensitivity and during longer safe periods. Overall, this study is the first to suggest cognitive mechanisms underlying the greater avoidance behavior observed in healthy individuals with different anxiety vulnerabilities.

Keywords: Anxiety vulnerability; Avoidance; Computational model; Individual difference; Reinforcement learning; Safety-signal.