Perceptual learning during action video game playing
Open accessAbstract
Action video games have been shown to enhance behavioral performance on a wide variety of perceptual tasks, from those that require effective allocation of attentional resources across the visual scene, to those that demand the successful identification of fleetingly presented stimuli. Importantly, these effects have not only been shown in expert action video game players, but a causative link has been established between action video game play and enhanced processing through training studies. Although an account based solely on attention fails to capture the variety of enhancements observed after action game playing, a number of models of perceptual learning are consistent with the observed results, with behavioral modeling favoring the hypothesis that avid video game players are better able to form templates for, or extract the relevant statistics of, the task at hand. This may suggest that the neural site of learning is in areas where information is integrated and actions are selected; yet changes in low-level sensory areas cannot be ruled out.
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Screen media activities like video gaming may generate physiological arousal that alters the processing and shaping of child and adolescent neural networks.
“screen media activities like video gaming may generate physiological arousal that produces alterations in the processing and shaping of child and adolescent neural networks (Green, Li, & Bavelier, 2010).”
From Screen Media Overuse and Associated Physical, Cognitive, and Emotional/Behavioral Outcomes in Children and Adolescents: An Integrative Review (2022) · cites this paper as Green, Li, & Bavelier, 2010
The Mechanisms section (p. 9) says video games are physiologically arousing and that arousal can modulate learning and task performance. It also proposes neural-network changes during game-related perceptual learning (pp. 11–12), but does not establish that arousal causes those changes or specifically describe the shaping of child and adolescent neural networks. (LLM, conf=0.86)
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- Authors
- Green, C. S., Li, R., & Bavelier, D.
- Affiliations
- University of Minnesota (US)
- University of Rochester (US)
- Corresponding author
- Green, C. Shawn
- ORCID
- Green, C. Shawn
- Venue
- Topics in Cognitive Science
- Publisher
- Wiley
- Published
- 2010-04
- Volume
- 2
- Issue
- 2
- Pages
- 202-216
- ISSN
- 1756-8757 (ISSN-L)
- 1756-8765
- OpenAlex
- W2115713159
- Semantic Scholar
- fd2a30c9e89c75d91da776d0fd830478580dcbb5
- Cited by
- 182
- References
- 81
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 7 · 2023: 4 · 2022: 18 · 2021: 15 · 2020: 6 · 2019: 14 · 2018: 10 · 2017: 10 · 2016: 9 · 2015: 19 · 2014: 14 · 2013: 17 · 2012: 15
- FWCI
- 0.55
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Topics in Cognitive Science (publishedVersion)
- PubMed (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion, other-oa)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Social Sciences › Psychology › Developmental and Educational Psychology › Sport Psychology and Performance
- Social Sciences › Sociology and Political Science › Digital Games and Media
- Keywords
- action video games, perceptual learning, video game training, attention, statistical learning, behavioral modeling
- MeSH terms
- Attention, Humans, Learning, Models, Psychological, Perception, Psychomotor Performance, Visual Perception, Video Games
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