A Large-Scale Analysis of Attentional Deployment across One Hundred Sessions of Adaptive Multitask Training

AbstractHuman cognition is routinely challenged by today’s multitasking demands which require continuous attentional deployment to multiple task components in parallel. While practice-based multitasking training has been shown to improve multitasking performance, little is known about how attention should be best deployed for optimal training. To this end, we leveraged a large-scale dataset from an online cognitive-training platform to investigate individual differences in task learning across long-term training. We developed an index of attentional deployment that specifies the temporal dynamics of learning for each component of the multitask and calculate distance maps between clusters of users to specify distinct learning styles. While long-term practice improved the multitasking performance of all participant groups, participants who focused on learning one task component earlier and more emphatically, benefited from exhibited superior learning gains throughout the entirety of training.

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