Year

2025

Season

Spring

Paper Type

Master's Thesis

College

College of Arts and Sciences

Degree Name

Master of Science in Psychological Science (MSPS)

Department

Psychological and Brain Sciences

NACO controlled Corporate Body

University of North Florida. Department of Psychological and Brain Sciences

Committee Chairperson

Dr. Lori Lange

Second Advisor

Dr. Gregory Kohn

Department Chair

Dr. Lori Lange

Abstract

This pilot study examined physiological markers of the human freeze response during exposure to threat and safety-based video stimuli. The freeze response is an adaptive defense mechanism characterized by reduced heart rate and heightened sympathetic arousal (Roelofs, 2017; Hagenaars et al., 2014; Sagliano et al., 2014; Hermans et al., 2013). 20 university students were randomly assigned to view either a threatening or safety-based video clip while heart rate and electrodermal activity (EDA) were recorded using Biopac Bionomadix wireless systems. Trauma severity and PTSD symptomatology were assessed through validated self-report measures (BTQ; Schnurr et al., 1999), (PCL-5; Weathers et al., 2013). Although traditional ANCOVA analyses did not reveal statistically significant group differences, moderate effect sizes suggested trends toward elevated sympathetic activity. Additionally, correlation analysis did not reveal any significant association between trauma severity and PTSD severity with physiological responses regardless of the video condition. Time-segmented analyses revealed brief autonomic shifts, particularly during the early moments of video onset, indicative of an orienting response. These patterns were obscured in condition-level averaging, emphasizing the value of segmented physiological analysis in freeze response research. Although the study was underpowered, it demonstrated the basis for capturing subtle biobehavioral changes through physiological recording and segmentation analysis. Future research should use larger, trauma-diverse samples, use subjective experience metrics, and employ machine learning approaches to confirm patterns in physiological data. Findings from this pilot contribute to an emerging understanding of how trauma influences defensive states and highlight the potential for refined methodologies in psychophysiological research.

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