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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2502.10290 |
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| _version_ | 1866914322555338752 |
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| author | Marticorena, Dom CP Lu, Zeyu Wissmann, Chris Agarwal, Yash Garrison, David Zempel, John M Barbour, Dennis L |
| author_facet | Marticorena, Dom CP Lu, Zeyu Wissmann, Chris Agarwal, Yash Garrison, David Zempel, John M Barbour, Dennis L |
| contents | Studies of human cognition often rely on brief, highly controlled tasks that emphasize group-level effects but poorly capture the rich variability within and between individuals. A suite of minigames built on the novel pixelDOPA platform was designed to overcome these limitations by embedding classic cognitive task paradigms in a 3D virtual interactive environment with continuous behavior logging. Four of the minigames explore constructs that overlap established NIH Toolbox tasks, including processing speed, rule shifting, inhibitory control and working memory. Across a clinical sample of 66 participants collected outside a controlled laboratory setting, large correlations (r = 0.47-0.92) between the pixelDOPA tasks and NIH Toolbox counterparts were found. Process-informed metrics improved both task convergence and data quality. Test-retest analyses revealed high reliability (ICC = 0.71-0.85) for all minigames. Beyond endpoint metrics, movement and gaze trajectories revealed stable, idiosyncratic profiles of gameplay strategy, with unsupervised clustering differentiating participants by their navigational and viewing behaviors. These trajectory-based features showed lower within-person variability than between-person variability, facilitating participant identification across repeated sessions. Game-based tasks can therefore retain the psychometric rigor of standard cognitive assessments while providing new insights into dynamic individual-specific behaviors. By leveraging a highly engaging, fully customizable game engine, comprehensive behavioral tracking can boost the power to detect individual differences without sacrificing group-level inference. This possibility reveals a path toward cognitive measures that are both psychometrically robust and deployable in less-than-ideal settings, while capturing richer behavioral data than traditional paradigms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10290 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interactive Virtual Games: Winners for Deep Cognitive Assessment Marticorena, Dom CP Lu, Zeyu Wissmann, Chris Agarwal, Yash Garrison, David Zempel, John M Barbour, Dennis L Human-Computer Interaction Studies of human cognition often rely on brief, highly controlled tasks that emphasize group-level effects but poorly capture the rich variability within and between individuals. A suite of minigames built on the novel pixelDOPA platform was designed to overcome these limitations by embedding classic cognitive task paradigms in a 3D virtual interactive environment with continuous behavior logging. Four of the minigames explore constructs that overlap established NIH Toolbox tasks, including processing speed, rule shifting, inhibitory control and working memory. Across a clinical sample of 66 participants collected outside a controlled laboratory setting, large correlations (r = 0.47-0.92) between the pixelDOPA tasks and NIH Toolbox counterparts were found. Process-informed metrics improved both task convergence and data quality. Test-retest analyses revealed high reliability (ICC = 0.71-0.85) for all minigames. Beyond endpoint metrics, movement and gaze trajectories revealed stable, idiosyncratic profiles of gameplay strategy, with unsupervised clustering differentiating participants by their navigational and viewing behaviors. These trajectory-based features showed lower within-person variability than between-person variability, facilitating participant identification across repeated sessions. Game-based tasks can therefore retain the psychometric rigor of standard cognitive assessments while providing new insights into dynamic individual-specific behaviors. By leveraging a highly engaging, fully customizable game engine, comprehensive behavioral tracking can boost the power to detect individual differences without sacrificing group-level inference. This possibility reveals a path toward cognitive measures that are both psychometrically robust and deployable in less-than-ideal settings, while capturing richer behavioral data than traditional paradigms. |
| title | Interactive Virtual Games: Winners for Deep Cognitive Assessment |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2502.10290 |