Project R-15849

Harnessing idiosyncratic semantic networks to predict individual fear differences and to optimize individualized fear extinction protocols. (Research)

Associative fear learning is considered a transdiagnostic mechanism underlying multiple disorders. Research has documented that associative fear learning relies on semantic knowledge. Yet, research has never acknowledged the idiosyncratic nature of this semantic knowledge nor how this may explain individual differences in fear behavior. As a consequence, a significant source of individual differences in fear responding in the laboratory remains overlooked, and more importantly, its clinical potential unexplored. In a series of experimental studies in healthy volunteers, this research project will explore the potential of idiosyncratic semantic networks in predicting individual fear behavior and optimizing fear extinction protocols. By estimating idiosyncratic semantic networks (based on a repeated fluency task) prior to a fear conditioning procedure, we will investigate whether network characteristics explain individual differences in fear generalization (WP1), and fear extinction and reinstatement (WP2). Furthermore, we will use network characteristics to optimize fear extinction protocols by tailoring the selection of stimuli used for fear extinction to the individual's semantic network (WP3). The combination of network science, semantics, and fear learning not only promises to open a new research field but also challenges existing theories and entails a significant and sorely needed first step toward the development of individualized fear exposure protocols.

01 January 2025 - 31 December 2028