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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.09262 |
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| _version_ | 1866916840511373312 |
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| author | Yim, Soobin Yoo, Sangbong Yoon, Chanyoung Jung, Chanyoung Kim, Chansoo Jang, Yun Quadri, Ghulam Jilani |
| author_facet | Yim, Soobin Yoo, Sangbong Yoon, Chanyoung Jung, Chanyoung Kim, Chansoo Jang, Yun Quadri, Ghulam Jilani |
| contents | Accurate assessment of mental workload (MW) is crucial for understanding cognitive processes during visualization tasks. While EEG-based measures are emerging as promising alternatives to conventional assessment techniques, such as selfreport measures, studies examining consistency across these different methodologies are limited. In a preliminary study, we observed indications of potential discrepancies between EEGbased and self-reported MW measures. Motivated by these preliminary observations, our study further explores the discrepancies between EEG-based and self-reported MW assessment methods through an experiment involving visualization tasks. In the experiment, we employ two benchmark tasks: the Visualization Literacy Assessment Test (VLAT) and a Spatial Visualization (SV) task. EEG signals are recorded from participants using a 32-channel system at a sampling rate of 128 Hz during the visualization tasks. For each participant, MW is estimated using an EEG-based model built on a Graph Attention Network (GAT) architecture, and these estimates are compared with conventional MW measures to examine potential discrepancies. Our findings reveal notable discrepancies between task difficulty and EEG-based MW estimates, as well as between EEG-based and self-reported MW measures across varying task difficulty levels. Additionally, the observed patterns suggest the presence of unconscious cognitive effort that may not be captured by selfreport alone. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09262 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Discrepancies in Mental Workload Estimation: Self-Reported versus EEG-Based Measures in Data Visualization Evaluation Yim, Soobin Yoo, Sangbong Yoon, Chanyoung Jung, Chanyoung Kim, Chansoo Jang, Yun Quadri, Ghulam Jilani Human-Computer Interaction Accurate assessment of mental workload (MW) is crucial for understanding cognitive processes during visualization tasks. While EEG-based measures are emerging as promising alternatives to conventional assessment techniques, such as selfreport measures, studies examining consistency across these different methodologies are limited. In a preliminary study, we observed indications of potential discrepancies between EEGbased and self-reported MW measures. Motivated by these preliminary observations, our study further explores the discrepancies between EEG-based and self-reported MW assessment methods through an experiment involving visualization tasks. In the experiment, we employ two benchmark tasks: the Visualization Literacy Assessment Test (VLAT) and a Spatial Visualization (SV) task. EEG signals are recorded from participants using a 32-channel system at a sampling rate of 128 Hz during the visualization tasks. For each participant, MW is estimated using an EEG-based model built on a Graph Attention Network (GAT) architecture, and these estimates are compared with conventional MW measures to examine potential discrepancies. Our findings reveal notable discrepancies between task difficulty and EEG-based MW estimates, as well as between EEG-based and self-reported MW measures across varying task difficulty levels. Additionally, the observed patterns suggest the presence of unconscious cognitive effort that may not be captured by selfreport alone. |
| title | Discrepancies in Mental Workload Estimation: Self-Reported versus EEG-Based Measures in Data Visualization Evaluation |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2507.09262 |