Computational and Human Learning Models of Generalized Unsafety

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Date
2020-08-20
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Journal ISSN
Volume Title
Publisher
Virginia Tech
Abstract

The Generalized Unsafety Theory of Stress proposes that physiological markers of generalized stress impair learning of safe cues in stressful environments. Based on this model, chronic problems inhibiting physiological arousal lead to a heightened perception of threat, which involves experiencing anxiety symptoms without any obvious precipitating stressful or traumatic event. This investigation aims to determine the impact of stressor- versus context-related emotional learning on generalized unsafety, using a Pavlovian threat-conditioning paradigm. The difference in learning threatening cues ([CS+] paired with an aversive stimulus) compared to safety cues ([CS-] not paired with an aversive stimulus) was used as a proxy measure of generalized unsafety, as conceptualized by the GUTS model. This difference is expected to be moderated by individual differences in tonic cardiac regulation (i.e. heart rate variability). Lastly, a temporal-differences learning model was used to predict skin-conductance learning during stressor, stressor context and general contexts to determine which best predicts Pavlovian learning. TD learning is expected to better predict skin-conductance in individuals with higher fear inhibition in comparison to those with low fear inhibition.

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Keywords
Pavlovian threat learning, anxiety, HRV, skin conductance, prediction-error learning
Citation