The Index of Cognitive Activity: measuring cognitive workload

Marshall, Sandra P.

doi:10.1109/hfpp.2002.1042860

Cited by 188 publications

(145 citation statements)

References 3 publications

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“…ICA appears to be a reliable measure of increases in cognitive load during linguistic processing [47]. It also performs well in simulated driving scenarios [48] and visual search [46]. There is also a report about different types of loads being more significantly represented in one of the eyes when load is measured by ICA.…”

Section: Metrics Derived From Pupil Diametermentioning

confidence: 89%

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A Review of Pupillometry for Human-computer Interaction Studies

Koles

2017

Period. Polytech. Elec. Eng. Comp. Sci.

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Section: Metrics Derived From Pupil Diametermentioning

confidence: 89%

“…The Index of Cognitive Activity (ICA) [46] is one of such methods, that attempts to differentiate between task and lighting change induced pupillary responses. Although the exact method of calculation is patented, so it is not freely accessible, the logic behind it is available.…”

Section: Metrics Derived From Pupil Diametermentioning

confidence: 99%

A Review of Pupillometry for Human-computer Interaction Studies

Koles

2017

Period. Polytech. Elec. Eng. Comp. Sci.

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“…Based on this patented work, she introduced the socalled index of cognitive activity (ICA) to measure cognitive effort (Marshall, 2002). The ICA "measures abrupt discontinuities in the signal created from continuous recording of pupil diameter" (Marshall, 2002, p. 7-5).…”

Section: Pupillary Responses As a Marker Of Mental Effort In Psycholomentioning

confidence: 99%

Real-time Prediction of User Performance based on Pupillary Assessment via Eye-Tracking

Buettner¹,

Sauer²,

Maier³

et al. 2018

THCI

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Abstract:We propose a method to predict user performance based on eye-tracking. The method uses eye-tracking-based pupillometry to capture pupil diameter data and calculates-based on a Random Forest algorithm-user performance expectations. We conducted a large-scale experimental evaluation (125 participants aged from 21 to 61 years) and found promising results that pave the way for a dynamic real-time adaption of IT to a user's mental effort and expected user performance. We have already achieved a good classification accuracy of user performance after only 40 seconds (5% of the mean total trial time that our participants took to complete our experiment). The non-invasive contact-free method can be applied cost-efficiently both in research and practical environments.

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“…The findings suggest that cognitive load is lower for visual than for aural presentation. Marshall (2002) proposed a physiological measure of cognitive workload, index of cognitive activity (ICA), from changes in pupil dilation. ICA would measure abrupt discontinuities in pupil diameter and try to separate pupil's reflex reaction to changes in light from the reflex reaction to changes in workload.…”

Section: Correlation To Workload In Visual Taskmentioning

confidence: 99%

“…The rate of pupil dilation over baseline pupil size Index of cognitive activity Based on changes in pupil dilation (Marshall, 2002) Power spectrum…”

Section: Pupil Dilationmentioning

confidence: 99%

Robust Multimodal Cognitive Load Measurement (RMCLM)

Chen¹

2013

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This report summarizes the important research activities, study results and research accomplishments out of the RMCLM project in the past year period. The objective of this project includes research of the fundamental issues related to the use of multiple input modalities and their fusion to enable robust and automatic cognitive load measurement (CLM) in the real world. Firstly, we carried out a further literature review on physiological measures of cognitive workload to include the recent advances of physiological measures of cognitive workload. In the meantime, we examined the use of various features (e.g. spectral and approximate entropies, wavelet-based complexity measures, correlation dimension, Hurst exponent) of electroencephalogram (EEG) signals to evaluate changes in working memory load during the performance of a cognitive task with varying difficulty/load levels. Eye based CLM was also studied. Three types of eye activity were investigated: pupillary response, blink, and eye movement (fixation and saccade). We further investigated the linguistic feature based CLM in this study and analyzed novel linguistic features as potential indices of cognitive load. All together, we had carried out CLM study of three unobtrusive modalities, namely EEG, eye activity, and linguistic feature based CLM, in the past year period. List of Publications Report Documentation PageForm Approved OMB No. Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. REPORT DATE APR 20132. REPORT TYPE This report summarizes the important research activities, study results and research accomplishments out of the RMCLM project in the past year period. The objective of this project includes research of the fundamental issues related to the use of multiple input modalities and their fusion to enable robust and automatic cognitive load measurement (CLM) in the real world. Firstly, we carried out a further literature review on physiological measures of cognitive workload to include the recent advances of physiological measures of cognitive workload. In the meantime, we examined the use of various features (e.g. spectral and approximate entropies, wavelet-based complexity measures, correlation dimension, Hurst exponent) of electroencephalogram (EEG) signals to evaluate changes ...

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The Index of Cognitive Activity: measuring cognitive workload

Cited by 188 publications

References 3 publications

A Review of Pupillometry for Human-computer Interaction Studies

A Review of Pupillometry for Human-computer Interaction Studies

Real-time Prediction of User Performance based on Pupillary Assessment via Eye-Tracking

Robust Multimodal Cognitive Load Measurement (RMCLM)

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