Time and Cognitive Load Within the Time-Based Resource- Sharing Model

Camos, Valérie; Portrat, Sophie; Bernardin, Sophie; Vergauwe, Evie; Barrouillet, Pierre

doi:10.1037/e512682013-228

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“…As Barrouillet et al (2004) emphasizes, it is unfortunately not trivial to determine the exact time during which attention is captured by processing demands. Moreover, as the model was developed and evaluated mainly for working-memory span tasks ( Daneman and Carpenter, 1980 ; Case et al, 1982 ), it’s further evaluation and extension to other executive functions required the design of specific experimental paradigms, allowing for defining certain retention and storage intervals at a predefined pace ( Lépine et al, 2005 ; Barrouillet et al, 2007 ; Camos et al, 2007 ; Liefooghe et al, 2008 ; Portrat, 2008 ).…”

Section: Introductionmentioning

confidence: 99%

Measuring Cognitive Load Using In-Game Metrics of a Serious Simulation Game

et al. 2021

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Serious games have become an important tool to train individuals in a range of different skills. Importantly, serious games or gamified scenarios allow for simulating realistic time-critical situations to train and also assess individual performance. In this context, determining the user’s cognitive load during (game-based) training seems crucial for predicting performance and potential adaptation of the training environment to improve training effectiveness. Therefore, it is important to identify in-game metrics sensitive to users’ cognitive load. According to Barrouillets’ time-based resource-sharing model, particularly relevant for measuring cognitive load in time-critical situations, cognitive load does not depend solely on the complexity of actions but also on temporal aspects of a given task. In this study, we applied this idea to the context of a serious game by proposing in-game metrics for workload prediction that reflect a relation between the time during which participants’ attention is captured and the total time available for the task at hand. We used an emergency simulation serious game requiring management of time-critical situations. Forty-seven participants completed the emergency simulation and rated their workload using the NASA-TLX questionnaire. Results indicated that the proposed in-game metrics yielded significant associations both with subjective workload measures as well as with gaming performance. Moreover, we observed that a prediction model based solely on data from the first minutes of the gameplay predicted overall gaming performance with a classification accuracy significantly above chance level and not significantly different from a model based on subjective workload ratings. These results imply that in-game metrics may qualify for a real-time adaptation of a game-based learning environment.

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