Personalized Adaptive Training Improves Performance at a Professional First-Person Shooter Action Videogame
Open accessAbstract
First-Person Shooter (FPS) game experience can be transferred to untrained cognitive functions such as attention, visual short-term memory, spatial cognition, and decision-making. However, previous studies have been using off-the-shelf FPS games based on predefined gaming settings, therefore it is not known whether such improvement of in game performance and transfer of abilities can be further improved by creating a in-game, adaptive in-game training protocol. To address this question, we compared the impact of a popular FPS-game (Counter-Strike:Global-Offensive–CS:GO) with an ad hoc version of the game based on a personalized, adaptive algorithm modifying the artificial intelligence of opponents as well as the overall game difficulty on the basis of individual gaming performance. Two groups of FPS-naïve healthy young participants were randomly assigned to playing one of the two game versions (11 and 10 participants, respectively) 2 h/day for 3 weeks in a controlled laboratory setting, including daily in-game performance monitoring and extensive cognitive evaluations administered before, immediately after, and 3 months after training. Participants exposed to the adaptive version of the game were found to progress significantly faster in terms of in-game performance, reaching gaming scenarios up to 2.5 times more difficult than the group exposed to standard CS:GO (p< 0.05). A significant increase in cognitive performance was also observed. Personalized FPS gaming can significantly speed-up the learning curve of action videogame-players, with possible future applications for expert-video-gamers and potential relevance for clinical-rehabilitative applications.
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Individualized adaptive training appears to result in promising findings in professional sports and gaming.
“Individualized adaptive training appears to result in promising findings within areas such as professional sports and gaming [179].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [179]
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“Individualized adaptive training appears to result in promising findings within areas such as professional sports and gaming [179].”
- Authors
- Neri, F., Smeralda, C. L., Momi, D., Sprugnoli, G., Menardi, A., Ferrone, S., Rossi, S., Rossi, A., Di Lorenzo, G., & Santarnecchi, E.
- Affiliations
- University of Siena (IT)
- Beth Israel Deaconess Medical Center (US)
- Corresponding author
- Santarnecchi, Emiliano
- ORCID
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2021-06-10
- Volume
- 12
- Pages
- 598410-598410
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC8224404
- OpenAlex
- W3173095692
- Cited by
- 27
- References
- 93
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 6 · 2023: 5 · 2022: 2
- FWCI
- 3.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Florence Research (University of Florence) (submittedVersion, cc-by-nc-nd)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- Cineca Institutional Research Information System (Tor Vergata University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Use Siena air (University of Siena) (submittedVersion, other-oa)
- Funders
- Università degli Studi di Siena
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
- Social Sciences › Sociology and Political Science › Digital Games and Media
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Functions and Memory
- Keywords
- first-person shooter games, adaptive training, individualized training, transfer of learning, game difficulty adaptation, Counter-Strike: Global Offensive, cognitive performance, learning curve, video game training
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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