Measuring the Effect of Think Aloud Protocols on Workload using fNIRS
CHI 2014, April 2014
Matthew Pike, Horia A. Maior, Martin Porcheron, Sarah Sharples, Max L. Wilson. 2014. Measuring the Effect of Think Aloud Protocols on Workload using fNIRS. In CHI 2014. DOI:https://doi.org/10.1145/2556288.2556974
Matthew Pike and Horia A. Maior and Martin Porcheron and Sarah Sharples and Max L. Wilson. (2014). Measuring the Effect of Think Aloud Protocols on Workload using fNIRS. CHI 2014. https://doi.org/10.1145/2556288.2556974
Matthew Pike and Horia A. Maior and Martin Porcheron and Sarah Sharples and Max L. Wilson. "Measuring the Effect of Think Aloud Protocols on Workload using fNIRS." CHI 2014, 2014. https://doi.org/10.1145/2556288.2556974
Matthew Pike, Horia A. Maior, Martin Porcheron, Sarah Sharples, Max L. Wilson. 2014. Measuring the Effect of Think Aloud Protocols on Workload using fNIRS. CHI 2014. doi:10.1145/2556288.2556974
Matthew Pike and Horia A. Maior and Martin Porcheron and Sarah Sharples and Max L. Wilson, "Measuring the Effect of Think Aloud Protocols on Workload using fNIRS," CHI 2014, 2014. doi: 10.1145/2556288.2556974
@inproceedings{chi-2014,
title={Measuring the Effect of Think Aloud Protocols on Workload using fNIRS},
author={Matthew Pike and Horia A. Maior and Martin Porcheron and Sarah Sharples and Max L. Wilson},
booktitle={CHI 2014},
year={2014},
doi={10.1145/2556288.2556974}
}
fNIRS, Mental Workload, Think Aloud Protocols, HCI, Cognitive Load
Abstract
The Think Aloud Protocol (TAP) is a widely used verbalization technique in HCI research to gain insights into user experiences, but little work has explored the cognitive impact of TAPs on study participants. This paper employs functional near-infrared spectroscopy (fNIRS) to observe how different types of TAPs affect cognitive workload. Participants performed mathematical tasks under four conditions: nonsense verbalizations, passive think-aloud, invasive think-aloud, and silence. The study measured task performance, subjective workload (NASA-TLX), and brain activity via fNIRS. Results show that invasive TAPs impose greater cognitive demand than passive ones, while nonsense verbalizations lead to significantly higher workload. These findings contribute to refining TAP methodologies and highlight the potential of fNIRS for real-world HCI studies.