Emotional Virtual Reality Stroop Task

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Mevlevioğlu, Deniz
Tabirca, Sabin
Murphy, David
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Anxiety classification in Virtual Reality using biosensors is an ongoing challenge for the area. This paper presents the development and validation of a Virtual Reality version of the well-established cognitive task; the Emotional Stroop Colour-Word Task. This environment aims to be able to differentiate between varying states of anxiety of the user. The task is split into three “stages” to correspond with three different states; no anxiety, low anxiety and anxiety. To validate whether the environment can accurately separate between these states, we have used a repeated measures design comparing each stage based on the mean (1) average electrodermal activity, (2) average self-reported measure of anxiety, and (3) average performance of the users in the stage. Repeated measures ANOVA results show that there is a significant difference between stages in electrodermal activity and self-reported anxiety, but not in response time. This signifies that it might be possible to record different anxiety states using the application. Further research must be conducted to test this hypothesis.
Stroop , VR , Viosensors , Anxiety , GSR , EEG , PPG , Virtual reality , Emotional Stroop
Mevlevioğlu, D., Tabirca, S. and Murphy, D. (2023) ‘Emotional Virtual Reality Stroop Task’, 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Atlanta, Georgia (USA), March 13-17, pp. 172-177. Forthcoming publication
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