Rodriguez Lopez, Marina; Liu, Huaiyu; Mancinelli, Federico; Brookes, Jack; Bach, Dominik R; (2025) The CogLearn Toolkit for Unity: Validating a virtual reality paradigm for human avoidance learning. Behavior Research Methods , 57 (6) , Article 160. 10.3758/s13428-025-02630-5.

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Abstract

Avoidance learning encompasses the acquisition of behaviours that enable individuals to evade or withdraw from potentially harmful stimuli, prior to their occurrence. Maladaptive avoidance is a crucial feature of anxiety and trauma-related disorders. In biological and clinical settings, avoidance behaviours usually involve uninstructed, idiosyncratic and complex motor actions. However, there is a lack of laboratory paradigms that allow investigating how such actions are acquired. To fill this gap, we developed a wireless virtual reality platform to investigate avoidance learning in naturalistic settings, with an uncomfortable sound as unconditioned stimulus (US), a physically plausible avoidance action, and allowing for unconstrained movements. This platform, the CogLearn Toolkit for Unity, is publicly available and allows conducting various types of learning experiments with simple text files as input. We validated this platform in an exploration-confirmation approach with five independent experiments. Overall, participants showed successful acquisition of avoidance behaviour in all experiments. In three exploration experiments, we refined the paradigm and identified mean distance from US location during conditioned stimulus (CS) presentation (before US occurs) as a sensitive measure of avoidance. Two confirmation experiments revealed stronger avoidance for CS+ than CS- during avoidance learning, whether or not this phase was preceded by Pavlovian acquisition. Furthermore, we demonstrated reduced avoidance during extinction with instruction to approach CS, but persistent residual avoidance during this phase. We found evidence of reinstatement in one of two confirmation experiments. Overall, our study provides robust evidence supporting the efficacy of our paradigm in studying avoidance learning in conditions of high ecological relevance.

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