11 September 2019: Using Computational Modeling to Study the Impact of Individual Differences and Contextual Cues on Avoidance Behavior

Jony Sheynin
Department of Psychiatry and Behavioral Science
Texas A&M University Health Science Center

Exaggerated avoidance behavior is a predominant symptom in all anxiety disorders and posttraumatic stress disorder (PTSD), 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, I have reported that female sex and inhibited temperament, two anxiety vulnerability factors, are associated with greater avoidance behavior in humans, as demonstrated on a computer-based task. I have also reported that avoidance is attenuated by the administration of explicit visual signals during “non-threat” periods (i.e., safety signals). Here, I demonstrate how a reinforcement-learning network model was used to investigate the underlying mechanisms of these empirical findings. Model simulations suggest that differences in relative sensitivity to reward and punishment might underlie the longer avoidance duration demonstrated by females, whereas greater sensitivity to punishment might underlie the greater 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 work highlights the utility of computational modeling for studying the impact of individual differences and contextual cues on avoidance behavior.

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