Workload alerts - using physiological measures of mental workload to provide feedback during tasks

Maior, Horia, Wilson, Max L. and Sharples, Sarah (2018) Workload alerts - using physiological measures of mental workload to provide feedback during tasks. ACM Transactions on Computer-Human Interaction, 25 (2). 9:1-9:30. ISSN 1073-0516

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Feedback is valuable for allowing us to improve on tasks. While retrospective feedback can help us improve for next time, feedback 'in action' can allow us to improve the outcome of on-going tasks. In this paper, we use data from functional Near InfraRed Spectroscopy to provide participants with feedback about their Mental Workload levels during high-workload tasks. We evaluate the impact of this feedback on task performance and perceived task performance, in comparison to industry standard mid-task self assessments, and explore participants' perceptions of this feedback. In line with previous work, we confirm that deploying self-reporting methods affect both perceived and actual performance. Conversely, we conclude that our objective concurrent feedback correlated more closely with task demand, supported reflection in action, and did not negatively affect performance. Future work, however, should focus on the design of this feedback and the potential behavior changes that will result.

Additional Information:© ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Computer-Human Interaction, VOL 25, ISS 2, April 2018
Keywords:Mental Workload, FNIRS, Physiological computing, Feedback, Task Demand, Performance, Bio-feedback of Workload
Subjects:G Mathematical and Computer Sciences > G440 Human-computer Interaction
G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
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ID Code:39591
Deposited On:17 Jan 2020 15:59

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