The concept of task engagement is associated with effortful striving to reach a desired goal. This dimension is fundamental for software designed to elicit high quality performance. This paper will review the concept of task engagement, both in the psychological literature and with respect to affective computing approaches, such as biofeedback and the definition of 'flow' states. This paper will briefly describe a series of laboratory experiments designed to explore measures of task engagement based on EEG and cardiovascular measures. These experiments employed a number of manipulations to influence task engagement, e.g. performance feedback, task difficulty and financial incentives. Results demonstrated the sensitivity of EEG measures to cognitive sources of engagement (e.g. mental workload) whilst cardiovascular variables tended to respond to the motivation to achieve. We use these findings to explore how real-time monitoring of engagement may generate adaptive dynamics for software design using a computer game as an exemplar system.

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The operationalisation of user psychological state is essential for physiological computing systems. The present study investigated the sensitivity of different psychophysiological measures to task engagement in a cognitive challenge scenario. A repeated measures design manipulated working memory demand and financial incentive. Low, high and impossible levels of cognitive challenge were created by combining memory demands with required standards of performance. 20 participants completed two blocks of the task, once with a financial incentive and once without. Performance, EEG, blood pressure, ECG, respiration and pupillometry were measured during task completion along with subjective reports of motivation and effort. Results showed sensitivity to demand for all EEG measures, blood pressure and heart rate variability (0.1Hz component). Sensitivity to incentive was found for EEG lower alpha suppression, heart rate and blood pressure. No effects were found on pupillometry and respiration data. Implications for the development of a real time cognitive monitor are discussed.

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