Eye activity as a measure of human mental effort in HCI

Chen, Siyuan; Epps, Julien; Ruiz, Natalie; Chen, Fang

doi:10.1145/1943403.1943454

Cited by 122 publications

(88 citation statements)

References 7 publications

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“…As in the case of fixation duration, these measures provide online measures of instantaneous load. In particular, there seems to be an inverse relationship between cognitive load and blink rate with blink rate decreasing as the cognitive workload increases (Bagley & Manelis, 1979, Brookings, Wilson, & Swain, 1996Chen, Epps, Ruiz, & Chen, 2011). Blink latency has also been found to increase with higher cognitive load (Chen et al, 2011).…”

Section: Eye Movementsmentioning

confidence: 93%

Measuring cognitive load in the presence of educational video: Towards a multimodal methodology

Kruger

Doherty

2016

AJET

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The use of video has become well established in education, from traditional courses to blended and online courses. It has grown both in its diversity of applications as well as its content. Such educational video however is not fully accessible to all students, particularly those who require additional visual support or students studying in a foreign language. Subtitles (also known as captions) represent a unique solution to these language and accessibility barriers, however, the impact of subtitles on cognitive load in such a rich and complex multimodal environment has yet to be determined. Cognitive load is a complex construct and its measurement by means of single indirect and unidimensional methods is a severe methodological limitation. Building upon previous work from several disciplines, this paper moves to establish a multimodal methodology for the measurement of cognitive load in the presence of educational video. We show how this methodology, with refinement, can allow us to determine the effectiveness of subtitles as a learning support in educational contexts. This methodology will also make it possible to analyse the impact of other multimedia learning technology on cognitive load.

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Section: Eye Movementsmentioning

confidence: 93%

Measuring cognitive load in the presence of educational video: Towards a multimodal methodology

Kruger

Doherty

2016

AJET

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“…This was accompanied by a decrease in fixation rate. Chen et al conclude that fixation duration and fixation rate are indicators of an increment in the attention that is needed as the complexity of the task increases [6].…”

Section: Fixationsmentioning

confidence: 99%

Measuring Cognitive Load using Eye Tracking Technology in Visual Computing

Zagermann

Pfeil

Reiterer

2016

Proceedings of the Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization

138

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In this position paper we encourage the use of eye tracking measurements to investigate users' cognitive load while interacting with a system. We start with an overview of how eye movements can be interpreted to provide insight about cognitive processes and present a descriptive model representing the relations of eye movements and cognitive load. Then, we discuss how specific characteristics of human-computer interaction (HCI) interfere with the model and impede the application of eye tracking data to measure cognitive load in visual computing. As a result, we present a refined model, embedding the characteristics of HCI into the relation of eye tracking data and cognitive load. Based on this, we argue that eye tracking should be considered as a valuable instrument to analyze cognitive processes in visual computing and suggest future research directions to tackle outstanding issues.

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“…Speech [10], pen input [21] and eye movements [5] are instances of behavioral signals used in cognitive load measurement. Eye activity can reveal valuable information about mental workload.…”

Section: Introductionmentioning

confidence: 99%

“…Another research has measured the blink rate in resting, reading and talking conditions [1]. A few studies have examined blink features in two cognitive load levels and found them related with mental load level [5,8].…”

Section: Introductionmentioning

confidence: 99%

GSR and Blink Features for Cognitive Load Classification

Nourbakhsh

Wang

Chen

2013

Lecture Notes in Computer Science

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Abstract.A system capable of monitoring its user's mental workload can evaluate the suitability of its interface and interactions for user's current cognitive status and properly change them when necessary. Galvanic skin response (GSR) and eye blinks are cognitive load measures which can be captured conveniently and at low cost. The present study has assessed multiple features of these two signals in classification of cognitive workload level. The experiment included arithmetic tasks with four difficulty levels and two types of machine learning algorithms have been applied for classification. Obtained results show that the studied features of blink and GSR can reasonably discriminate workload levels and combining features of the two modalities improves the accuracy of cognitive load classification. We have achieved around 75% for binary classification and more than 50% for four-class classification.

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Eye activity as a measure of human mental effort in HCI

Cited by 122 publications

References 7 publications

Measuring cognitive load in the presence of educational video: Towards a multimodal methodology

Measuring cognitive load in the presence of educational video: Towards a multimodal methodology

Measuring Cognitive Load using Eye Tracking Technology in Visual Computing

GSR and Blink Features for Cognitive Load Classification

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