Gradations in task engagement emerge from metacognitive priority control

AbstractEngagement is a critical motivational factor that has broad effects on learning, productivity, performance, and even satisfaction and happiness. However, it can also be impacted by a myriad of factors which have made it difficult to model and design interventions. Here we address this problem by developing an integrated metacognitive framework for understanding task engagement. We treat engagement as resulting from a unified metacognitive decision process where the gradient of engagement results from a common priority calculation. Priority signals are computed relative to a set of available tasks and updated across time and environmental changes. We propose a metacognitive controller makes decisions about both task switching (when to quit, next task) and cognitive resourcing (working memory, attention, etc) using the graded priority signals. By simultaneously choosing the task and allocating resources using the same graded signals, we capture the complex dependencies of engagement with task errors, performance, and time allocation.

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