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Effects of Valence and Arousal on Working Memory Performance in Virtual Reality Gaming

Accepted version
Peer-reviewed

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Authors

Gabana, D 
Tokarchuk, L 
Hannon, E 

Abstract

The role of affective states in cognitive performance has long been an area of interest in cognitive science. Recent research in game-based cognitive training suggest that cognitive games should incorporate real-time adaptive mechanisms. These adaptive mechanisms would change the game’s difficulty according to the player’s performance in order to provide appropriate challenges and thus, achieve a real cognitive improvement. However, these mechanisms currently ignore the effects of valence and arousal on the player’s cognitive skills. In this paper we investigate how working memory (WM) performance is affected when playing a VR game, and the effects of valence and arousal in this context. To this aim, a custom video game was created for Desktop and VR. Three difficulty levels were designed to evoke different levels of arousal while maintaining the same memory load for each difficulty level. We found an improvement in WM performance when playing in VR compared to Desktop. This effect was particularly pronounced in those with a low WM capacity. Significantly higher levels of valence and arousal were self-reported when playing in VR.We explore the impact that reported affective states could have in the player’s WM performance. We suggest that high levels of arousal and positive valence can lead players to a flow state [1] that may have a positive impact on the player’s WM performance.

Description

Keywords

46 Information and Computing Sciences, 4608 Human-Centred Computing, Basic Behavioral and Social Science, Behavioral and Social Science

Journal Title

Proceedings of the Seventh International Conference on Affective Computing and Intelligent Interaction

Conference Name

Seventh International Conference on Affective Computing and Intelligent Interaction

Journal ISSN

2156-8103

Volume Title

Publisher

IEEE
Sponsorship
This work is supported by Queen Mary University of London and EPSRC Media and Arts Technology Doctoral Training Centre (EP/G03723X/1).