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Towards measuring cognitive load through multimodal physiological data

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Abstract
Cognitive load plays an important role during learning and working, as it has been linked to well-functioning cognitive processes, performance, burnout and depression. Nonetheless, attempts to assess cognitive load in real-time by means of physiological data have been proven difficult, and interpreting these data remains challenging. The aim of this study is to examine whether and how well experienced cognitive load can be measured through psycho-physiological data. The approach of this study is rather unique, for a combination of reasons. First, this study takes a multimodal approach, monitoring EDA (electrodermal activity), EEG (electroencephalography) and EOG (electrooculography). Second, this study is based on a relatively intensive data collection (N = 46) in a controlled lab setting in which varying cognitive load levels are deliberately induced. Finally, not only focussing on statistical significance but also on the size of the association gives insights into how suitable physiological markers are to measure cognitive load. Results from a multilevel analysis suggest that the following physiological markers might be related to cognitive load, for example, in an industrial context: the rate and the duration of skin conductance responses, the alpha power, the alpha peak frequency and the eye blink rate. About 22.8% of the variance in self-reported cognitive load can be explained using these five measures.
Keywords
Philosophy, Human-Computer Interaction, Computer Science Applications, Cognitive load, Mental effort, Physiology, EEG, EOG, EDA, MENTAL WORKLOAD, WORKING-MEMORY, TRACKING, BURNOUT, TASK

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MLA
Vanneste, Pieter, et al. “Towards Measuring Cognitive Load through Multimodal Physiological Data.” COGNITION TECHNOLOGY & WORK, 2020, doi:10.1007/s10111-020-00641-0.
APA
Vanneste, P., Raes, A., Morton, J., Bombeke, K., Van Acker, B., Larmuseau, C., … Van den Noortgate, W. (2020). Towards measuring cognitive load through multimodal physiological data. COGNITION TECHNOLOGY & WORK. https://doi.org/10.1007/s10111-020-00641-0
Chicago author-date
Vanneste, Pieter, Annelies Raes, Jessica Morton, Klaas Bombeke, Bram Van Acker, Charlotte Larmuseau, Fien Depaepe, and Wim Van den Noortgate. 2020. “Towards Measuring Cognitive Load through Multimodal Physiological Data.” COGNITION TECHNOLOGY & WORK. https://doi.org/10.1007/s10111-020-00641-0.
Chicago author-date (all authors)
Vanneste, Pieter, Annelies Raes, Jessica Morton, Klaas Bombeke, Bram Van Acker, Charlotte Larmuseau, Fien Depaepe, and Wim Van den Noortgate. 2020. “Towards Measuring Cognitive Load through Multimodal Physiological Data.” COGNITION TECHNOLOGY & WORK. doi:10.1007/s10111-020-00641-0.
Vancouver
1.
Vanneste P, Raes A, Morton J, Bombeke K, Van Acker B, Larmuseau C, et al. Towards measuring cognitive load through multimodal physiological data. COGNITION TECHNOLOGY & WORK. 2020;
IEEE
[1]
P. Vanneste et al., “Towards measuring cognitive load through multimodal physiological data,” COGNITION TECHNOLOGY & WORK, 2020.
@article{8672195,
  abstract     = {Cognitive load plays an important role during learning and working, as it has been linked to well-functioning cognitive processes, performance, burnout and depression. Nonetheless, attempts to assess cognitive load in real-time by means of physiological data have been proven difficult, and interpreting these data remains challenging. The aim of this study is to examine whether and how well experienced cognitive load can be measured through psycho-physiological data. The approach of this study is rather unique, for a combination of reasons. First, this study takes a multimodal approach, monitoring EDA (electrodermal activity), EEG (electroencephalography) and EOG (electrooculography). Second, this study is based on a relatively intensive data collection (N = 46) in a controlled lab setting in which varying cognitive load levels are deliberately induced. Finally, not only focussing on statistical significance but also on the size of the association gives insights into how suitable physiological markers are to measure cognitive load. Results from a multilevel analysis suggest that the following physiological markers might be related to cognitive load, for example, in an industrial context: the rate and the duration of skin conductance responses, the alpha power, the alpha peak frequency and the eye blink rate. About 22.8% of the variance in self-reported cognitive load can be explained using these five measures.},
  author       = {Vanneste, Pieter and Raes, Annelies and Morton, Jessica and Bombeke, Klaas and Van Acker, Bram and Larmuseau, Charlotte and Depaepe, Fien and Van den Noortgate, Wim},
  issn         = {1435-5558},
  journal      = {COGNITION TECHNOLOGY & WORK},
  keywords     = {Philosophy,Human-Computer Interaction,Computer Science Applications,Cognitive load,Mental effort,Physiology,EEG,EOG,EDA,MENTAL WORKLOAD,WORKING-MEMORY,TRACKING,BURNOUT,TASK},
  language     = {eng},
  title        = {Towards measuring cognitive load through multimodal physiological data},
  url          = {http://dx.doi.org/10.1007/s10111-020-00641-0},
  year         = {2020},
}

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