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"AVOIDANCE BEHAVIOR IN HUMANS:
EMPIRICAL AND COMPUTATIONAL APPROACHES"

by
Jony Sheynin
Biomedical Engineering Program
B.Sc. 2006, Ben-Gurion University
M.Med.Sc. 2008, Ben-Gurion University



Thesis Advisor: Catherine E. Myers, Ph.D.
Professor
Department of Neurology and Neurosciences

Tuesday, September 9, 2014
10:00 A.M., VA Medical Center, East Orange, NJ, Main Building, Main Conference Room , (11-137), 11th Floor


Abstract

While avoidance is normally an adaptive behavior that protects one from harm, increased avoidance can become detrimental and contribute to the development of psychopathologies. Indeed, exaggerated avoidance behavior is a predominant symptom in all anxiety disorders and post-traumatic stress disorder. In spite of the clinical importance and the rich animal literature, research of avoidance behavior in humans has been very limited, mainly due to the lack of adequate assessment tools. In this dissertation, I present three studies in which young adults completed computer-based tasks that were developed to assess different aspects of avoidance behavior. While results show that simple computer-based tools can be successfully used for assessing avoidance behavior in humans, they also reveal that similarly to non-human animals, humans with anxiety vulnerabilities (female sex and inhibited temperament) demonstrate increased avoidance responding. Interestingly, the administration of signals associated with non-threat periods (i.e., safety signals) was shown to attenuate this responding. Lastly, I introduce computational modeling as an another approach to study avoidance. Model simulations suggest that distinct sensitivities to reward and punishment might underlie the behavioral differences reported in this work. They also predict that 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 dissertation bridges the gap between human and animal avoidance research, encourages the use of specific contextual cues and personalized approaches in therapy, and suggests cognitive mechanisms that can potentially underlie some of the unique avoidance patterns observed in humans.


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