Robust Multimodal Cognitive Load Measurement

Chen, Fang; Zhou, Jianlong; Wang, Yang; Yu, Kun; Arshad, Syed Z.; Khawaji, Ahmad; Conway, Dan

doi:10.1007/978-3-319-31700-7

Cited by 111 publications

(100 citation statements)

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“…These values can then be used to establish an individual's average pupil size and how it reacts to changes in light conditions (e.g. compare the machine learning based classification of cognitive load as described by [5]). Based on this, conclusion about the user's cognitive load could be drawn, not based on a static value but based on the pupil size's dynamic changes over time.…”

Section: Pupil Dilation and Environmentmentioning

confidence: 99%

“…One such metric that is currently of interest is the cognitive load that the system and task place on the user. Cognitive load describes the amount of mental effort that is used from the working memory while performing a cognitive task [5] and interacting with a system [22]. Designing successful systems means implementing visualizations and interaction techniques in a way that they require little capacity to use and allow the user to concentrate on the task at hand.…”

Section: Introductionmentioning

confidence: 99%

“…To this end, several studies have investigated the relation of voluntary eye-movements like fixations and saccades on cognitive processes [21] as well as involuntary eye-movements like blink rate and pupil dilation [17]. These eye movements are also called behavioural (voluntary) and physiological (involuntary) [5]. Through the tracking of the eyes, one can study a user's physical reaction to a task and a system and adapt the interface accordingly.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Pupil Dilation and Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

“…Pupillometry, which refers to measuring pupil diameter, is one of the widely used physiological measures to assess cognitive load (Chen et al, ). Pupil dilation has been known for decades as a highly valid indicator of cognitive load (Bækgaard et al, ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Recent advances in physiological measurements opened the possibility of doing this. Pupillometry, which refers to measuring pupil diameter, is one of the candidates for physiological measurement (Chen et al, ); pupil dilation has been known for decades as a highly valid indicator of cognitive load (Bækgaard, Jalaliniya, & Hansen, ). Hess and Polt () found that participants' pupil diameter differed as a function of difficulty during arithmetic tasks, leading the conclusion that pupil dilation was an index of total mental activity.…”

Section: Introductionmentioning

confidence: 99%

A novel method to monitoring changes in cognitive load in video‐based learning

Huh

Kim

2019

Computer Assisted Learning

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One of the golden rules in instructional design methods is to optimize the use of working memory capacity and avoid cognitive overload. The study of cognitive load has historically relied on one's introspection. However, it is difficult to capture changes in cognitive load levels during learning sensitively. This paper suggests an approach to investigating dynamic changes in cognitive load by using a pupillometry. With the method, this study explores the effects of learners' prior knowledge and task complexity on cognitive load. An experiment was conducted on two groups of students (N = 19) with distinct levels of prior knowledge. In the experimental session, participants watched a video lecture on a mathematics proposition, while being eye‐tracked. The lecture consists of sections, which can be either a high task complexity or a low task complexity based on elements they have. Pupil dilations acquired in each section were used to explore the time course of cognitive load. To formulate cognitive load patterns, a time‐series clustering was used. The research conducted a chi‐square analysis to test differences in cognitive load patterns by prior knowledge and task complexity. Results show that pupil dilation patterns can be applied to monitor changes in cognitive load during learning.

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Experimental study on the relationship between the harmony and cognitive load of business intelligence dashboard color combinations

Tan

Liu

2021

Color Research & Application

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The color combination is an important factor affecting dashboard visual design and is key to triggering the operator's visual harmony and emotion. However, in the actual design process, the relationship among the different color schemes of a dashboard and the operator's harmony, pleasure, and cognitive load has not been effectively analyzed. To solve this problem, two methods, questionnaire measurement and eye movement tracking, were used to evaluate the effects of 24‐color combinations under four‐color schemes of a dashboard by analyzing the changes in color harmony, pleasure, eye movement indicators. The four schemes were red–yellow–blue, green–purple–orange, blue green–blue purple–yellow green, and red orange–yellow orange–red purple. The research results show that the degrees of color harmony and pleasure show a positive correlation. Cognitive load is not affected by color harmony. The larger the amount of information, the higher the cognitive load. This research can be used as a reference when designing and optimizing the color scheme of a business intelligence dashboard.

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Robust Multimodal Cognitive Load Measurement

Cited by 111 publications

References 0 publications

Measuring Cognitive Load using Eye Tracking Technology in Visual Computing

Measuring Cognitive Load using Eye Tracking Technology in Visual Computing

A novel method to monitoring changes in cognitive load in video‐based learning

Experimental study on the relationship between the harmony and cognitive load of business intelligence dashboard color combinations

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