Use of Mouse-tracking Method to Measure Cognitive Load

Rheem, Hansol; Verma, Vipin; Becker, David V.

doi:10.1177/1541931218621449

Cited by 26 publications

(18 citation statements)

References 29 publications

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“…We think that mouse cursor movement tracking provides a viable remedy. Mouse cursor movement properties, such as the area under the curve, speed and distance have been found to be associated with elevated levels of emotion [33], stress [34], cognitive impairment [35], cognitive load [36, 37], as well as ADHD [7, 38, 39]. Mouse and hand movement properties were also found to be receptive to attention and cognitive control [40, 41] and, most importantly, inhibitory abilities [42].…”

Section: Introductionmentioning

confidence: 99%

Mouse movement measures enhance the stop-signal task in adult ADHD assessment

Leontyev

Yamauchi

2019

PLoS ONE

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The accurate detection of attention-deficit/hyperactivity disorder (ADHD) symptoms, such as inattentiveness and behavioral disinhibition, is crucial for delivering timely assistance and treatment. ADHD is commonly diagnosed and studied with specialized questionnaires and behavioral tests such as the stop-signal task. However, in cases of late-onset or mild forms of ADHD, behavioral measures often fail to gauge the deficiencies well-highlighted by questionnaires. To improve the sensitivity of behavioral tests, we propose a novel version of the stop-signal task (SST), which integrates mouse cursor tracking. In two studies, we investigated whether introducing mouse movement measures to the stop-signal task improves associations with questionnaire-based measures, as compared to the traditional (keypress-based) version of SST. We also scrutinized the influence of different parameters of stop-signal tasks, such as the method of stop-signal delay setting or definition of response inhibition failure, on these associations. Our results show that a) SSRT has weak association with impulsivity, while mouse movement measures have strong and significant association with impulsivity; b) machine learning models trained on the mouse movement data from “known” participants using nested cross-validation procedure can accurately predict impulsivity ratings of “unknown” participants; c) mouse movement features such as maximum acceleration and maximum velocity are among the most important predictors for impulsivity; d) using preset stop-signal delays prompts behavior that is more indicative of impulsivity.

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Section: Introductionmentioning

confidence: 99%

Mouse movement measures enhance the stop-signal task in adult ADHD assessment

Leontyev

Yamauchi

2019

PLoS ONE

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“…During high-intensity activities, a user may generate an average of 70 or 70 events per second [47], meaning that 70 rows per second will be generated on a table. The traditional way of reducing the size of these tracking data is by reducing the sampling rate [11].…”

Section: Reducing Eye and Mouse Tracking Datamentioning

confidence: 99%

Using real-time online preprocessed mouse tracking for lower storage and transmission costs

Purnama

Usagawa

2020

J Big Data

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IntroductionWe are living in the age of information where information and communication technologies are becoming mainstream [1]. Computers and the Internet have become inseparable from our daily lives. It is now possible to do many things without the need to travel and wait; examples include, long-distance communication through online text, voice and video calls, social interaction via social media, online learning, ecommerce or online shopping, and many forms of entertainments such as online music, online videos, and online games. Most importantly, the Internet has become the primary source of information around the world.Online analytics has become a major area of interest because it enables the study of users' online behaviors. These include, users' attentions and, students' performance in online learning. Businesses also use online analytics to capture consumers' interests in AbstractPageview is the most popular webpage analytic metric in all sectors including blogs, business, e-commerce, education, entertainment, research, social media, and technology. To perform deeper analysis, additional methods are required such as mouse tracking, which can help researchers understand online user behavior on a single webpage. However, the geometrical data generated by mouse tracking are extremely large, and qualify as big data. A single swipe on a webpage from left to right can generate a megabyte (MB) of data. Fortunately, the geometrical data of each x and y point of the mouse trail are not always needed. Sometimes, analysts only need the heat map of a certain area or perhaps just a summary of the number of activities that occurred on a webpage. Therefore, recording all geometrical data is sometimes unnecessary. This work introduces preprocessing during real-time and online mouse tracking sessions. The preprocessing that is introduced converts the geometrical data from each x and y point to a region-of-interest concentration, in other words only heat map areas that the analyzer is interested in. Ultimately, the approach used here is able to greatly reduce the storage and transmission cost of real-time online mouse tracking.

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“…Generally, research on the computer mouse focuses on how mouse movements could reveal users' intentions. Its availability and its potential for scalability enable innovative applications, such as authentication (Zheng et al, 2011), the prediction of the users' cognitive load (Rheem et al, 2018), the prediction of users' intentions (Guo and Agichtein, 2010;Fu et al, 2017), or pattern behavior analysis (Tzafilkou and Protogeros, 2018). One of the most studied topics is the computer mouse movement patterns commonly used by participants when browsing.…”

Section: Introductionmentioning

confidence: 99%

Similarities and Differences Between Eye and Mouse Dynamics During Web Pages Exploration

et al. 2021

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The study of eye movements is a common way to non-invasively understand and analyze human behavior. However, eye-tracking techniques are very hard to scale, and require expensive equipment and extensive expertise. In the context of web browsing, these issues could be overcome by studying the link between the eye and the computer mouse. Here, we propose new analysis methods, and a more advanced characterization of this link. To this end, we recorded the eye, mouse, and scroll movements of 151 participants exploring 18 dynamic web pages while performing free viewing and visual search tasks for 20 s. The data revealed significant differences of eye, mouse, and scroll parameters over time which stabilize at the end of exploration. This suggests the existence of a task-independent relationship between eye, mouse, and scroll parameters, which are characterized by two distinct patterns: one common pattern for movement parameters and a second for dwelling/fixation parameters. Within these patterns, mouse and eye movements remained consistent with each other, while the scrolling behaved the opposite way.

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Use of Mouse-tracking Method to Measure Cognitive Load

Cited by 26 publications

References 29 publications

Mouse movement measures enhance the stop-signal task in adult ADHD assessment

Mouse movement measures enhance the stop-signal task in adult ADHD assessment

Using real-time online preprocessed mouse tracking for lower storage and transmission costs

Similarities and Differences Between Eye and Mouse Dynamics During Web Pages Exploration

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