Measuring the effect of Think Aloud Protocols on workload using fNIRS

Pike, Matthew F., Maior, Horia A., Porcheron, Martin, Sharples, Sarah C. and Wilson, Max L. (2014) Measuring the effect of Think Aloud Protocols on workload using fNIRS. In: 32nd Annual ACM Conference on Human Factors in Computing Systems.

Full content URL: https://doi.org/10.1145/2556288.2556974

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Measuring the effect of Think Aloud Protocols on workload using fNIRS
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Abstract

The Think Aloud Protocol (TAP) is a verbalisation technique widely employed in HCI user studies to give insight into user experience, yet little work has explored the impact that TAPs have on participants during user studies. This paper utilises a brain sensing technique, fNIRS, to observe the effect that TAPs have on participants. Functional Near-Infrared Spectroscopy (fNIRS) is a brain sensing technology that offers the potential to provide continuous, detailed insight into brain activity, enabling an objective view of cognitive processes during complex tasks. Participants were asked to perform a mathematical task under 4 conditions: nonsense verbalisations, passive concurrent think aloud protocol, invasive concurrent think aloud protocol, and a baseline of silence. Subjective ratings and performance measures were collected during the study. Our results provide a novel view into the effect that different forms of verbalisation have on workload during tasks. Further, the results provide a means for estimating the effect of spoken artefacts when measuring workload, which is another step towards our goal of proactively involving fNIRS analysis in ecologically valid user studies.

Additional Information:Published in: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '14). New York : ACM, 2014. ISBN 9781450324731 pp. 3807-3816, DOI: 10.1145/2556288.2556974
Keywords:bci, fnirs, functional near-infrared spectroscopy, hci, human cognition, think aloud protocol
Subjects:G Mathematical and Computer Sciences > G440 Human-computer Interaction
Divisions:College of Science > School of Computer Science
ID Code:39597
Deposited On:31 Mar 2020 12:15

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