PanicRoom: a virtual reality-based Pavlovian fear conditioning paradigm

Front Psychol. 2024 Oct 4:15:1432141. doi: 10.3389/fpsyg.2024.1432141. eCollection 2024.

Abstract

Introduction: Pavlovian fear conditioning is an experimental paradigm used to study the acquisition and extinction of fear responses and the various aspects of fear and anxiety. We developed a virtual reality (VR) version of this paradigm to leverage the benefits of virtual reality, such as ecological validity, standardization, safety, and therapeutic applications. Our objective was to create an open-source and immersive environment for studying fear-related responses using Unity Engine 3D and the Oculus Rift device.

Methods: In this virtual environment, the participants encountered a monster screaming at 100 dB approaching them as the fear-inducing stimulus (unconditioned stimulus or US). Our protocol included three sessions: habituation, acquisition, and extinction, with two stimuli associated with different doors (blue vs. red). The blue door (CS+) was linked to the US, while the red door (CS-) was the control. We tested this VR paradigm on 84 young participants, recording their skin conductance response (SCRs) and fear stimulus ratings (FSRs) on a 10-point Likert scale.

Results: The findings showed significantly higher SCRs and FSRs for CS+ as compared to CS- during the acquisition phase and higher SCRs and FSRs for CS+ during the acquisition phase as compared to the habituation and extinction sessions.

Discussion and conclusions: These results supported the reliability of the protocol for studying fear and anxiety-related conditions.

Keywords: Pavlovian (classical) conditioning; Pavlovian fear conditioning task in virtual reality; experimental paradigm; fear conditioning; virtual reality.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. CV was supported by Ministero Istruzione Università e Ricerca (PRIN 2022, NextGenerationEU, Project code: 2022L3AALJ). This work is supported by the Italian PNRR MUR project PE0000013--FAIR: Future Artificial Intelligence Research.