Measuring the task-evoked pupillary response with a remote eye tracker

Klingner, Jeff; Kumar, Rahul; Hanrahan, Pat

doi:10.1145/1344471.1344489

Cited by 171 publications

(140 citation statements)

References 8 publications

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“…A valid and reliable assessment method of workload could be helpful in the optimization of systems that involve human-computer interaction, such as vehicles, computers, and simulators. One promising method for measuring workload is pupillometry, which is the measurement of the pupil diameter (e.g., Goldinger & Papesh, 2012;Granholm & Steinhauer, 2004;Klingner, Kumar & Hanrahan, 2008;Laeng, Sirois & Gredebäck, 2012;Marshall, 2007;Palinko et al, 2010;Schwalm, Keinath & Zimmer, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Workload assessment for mental arithmetic tasks using the task-evoked pupillary response

Marquart

Winter

2015

PeerJ Computer Science

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Pupillometry is a promising method for assessing mental workload and could be helpful in the optimization of systems that involve human-computer interaction. The present study focuses on replicating the studies by Ahern (1978) and Klingner (2010), which found that for three levels of difficulty of mental multiplications, the more difficult multiplications yielded larger dilations of the pupil. Using a remote eye tracker, our research expands upon these two previous studies by statistically testing for each 1.5 s interval of the calculation period (1) the mean absolute pupil diameter (MPD), (2) the mean pupil diameter change (MPDC) with respect to the pupil diameter during the pre-stimulus accommodation period, and (3) the mean pupil diameter change rate (MPDCR). An additional novelty of our research is that we compared the pupil diameter measures with a self-report measure of workload, the NASA Task Load Index (NASA-TLX), and with the mean blink rate (MBR). The results showed that the findings of Ahern and Klingner were replicated, and that the MPD and MPDC discriminated just as well between the lowest and highest difficulty levels as did the NASA-TLX. The MBR, on the other hand, did not differentiate between the difficulty levels. Moderate to strong correlations were found between the MPDC and the proportion of incorrect responses, indicating that the MPDC was higher for participants with a poorer performance. For practical applications, validity could be improved by combining pupillometry with other physiological techniques.

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Section: Introductionmentioning

confidence: 99%

“…The influence of gaze direction on the measured pupil size is another issue. Where Pomplun & Sunkara (2003) reported a systematic dependence of pupil size on gaze direction, Klingner, Kumar & Hanrahan (2008) argued that the ellipse-fitting method for the estimation of the pupil size is not affected by perspective distortion.…”

Section: Introductionmentioning

confidence: 99%

Workload assessment for mental arithmetic tasks using the task-evoked pupillary response

Marquart

Winter

2015

PeerJ Computer Science

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“…Pupil diameter is of interest due to the fact that when people are faced with a challenging cognitive task, their pupils dilate. This phenomenon is called the Task Evoked Pupillary Response (TEPR) (Beatty, 1982;Klingner et al, 2008). However, cognitive load is certainly not the only factor influencing pupil diameter.…”

Section: Introductionmentioning

confidence: 99%

“…Using a remote eye tracker Klingner et al (2008) successfully estimated cognitive load in a desktop environment, while our group did so in a driving simulator (Palinko et al, 2010). However, neither study explored the interaction between cognitive load and the pupil's light reflex, opting instead to maintain near-constant lighting.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Influence of Light and Cognitive Load on Pupil Diameter in Driving Simulator Studies

Palinko

Kun

2011

Proceedings of the 6th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design : Dr

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“…The experimental results indicated that the fluctuation rhythm of the pupil area could be used as an effective physiological index to evaluate mental workload. Klingner et al (2008) examined the pupil measuring capability of video based eye tracker for cognitive workload evaluation. In the experiment of several tasks including arithmetic and memory ones, subtle changes of pupil size in the task-evoked pupillary response were detected using remote eye tracker.…”

Section: Correlation To Workload In Arithmetic/memory Taskmentioning

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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Measuring the task-evoked pupillary response with a remote eye tracker

Cited by 171 publications

References 8 publications

Workload assessment for mental arithmetic tasks using the task-evoked pupillary response

Workload assessment for mental arithmetic tasks using the task-evoked pupillary response

Exploring the Influence of Light and Cognitive Load on Pupil Diameter in Driving Simulator Studies

Robust Multimodal Cognitive Load Measurement (RMCLM)

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