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Virginia Tech

Computational and Human Learning Models of Generalized Unsafety

Abstract

dc:description.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.

Degree

thesis:*
Name thesis:degree_name
Doctor of Philosophy
Level thesis:degree_level
doctoral
Discipline thesis:degree_discipline
Psychology
Department dc:contributor.department
Psychology
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2020

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Huskey, Alisa Mae
Chair dc:contributor.committeechair
  • Friedman, Bruce H.
Committee members dc:contributor.committeemember
  • Jones, Russell T.
  • Diana, Rachel A.
  • Casas, Brooks

Subjects

dc:subject × 5

Rights

dc:rights
Statement dc:rights
  • In Copyright
Language dc:language.iso
en

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:25329
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/99797

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Huskey, Alisa Mae. Computational and Human Learning Models of Generalized Unsafety. doctoral thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99797