Measuring Cognition Load Using Eye-Tracking Parameters Based on Algorithm Description Tools

Katona, József

doi:10.3390/s22030912

Cited by 37 publications

(14 citation statements)

References 31 publications

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“…By divided the source code into different AOIs, the source code analysis with eye tracking technology can provide more accurate and precise results ( Katona, 2021 ). Future research can measure cognitive load using eye-tracking parameters ( Katona, 2022 ) to ensure that the content of messages does not cause excessive cognitive load on consumers.…”

Section: Discussionmentioning

confidence: 99%

Visual Attention, Behavioral Intention, and Choice Behavior Among Older Consumers Toward Sports Marketing Images: An Eye-Tracking Study

2022

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Mental health benefits (i.e., relaxing and relieving pressure) can influence consumers’ consumption decisions. However, there is still no clear understanding of the impact of mental health benefits on visual attention, behavioral intention, and choice behavior. Study 1 was thus aimed at exploring the visual attention and behavioral intention of older consumers with respect to exercise consumption. A sample of 186 older consumers was investigated. An eye-tracking analysis was used to evaluate the visual attention of participants observing health promotion messages, and questionnaires were used to assess the behavioral intention of the older consumers under consideration in this work. The findings confirmed that marketing pictures combining natural sportscapes with prevention focus messages (i.e., conveying information to consumers that it is safe and not easy to be injured when engaging in yoga activities in natural settings) can best capture older consumers’ visual attention (e.g., fixation numbers and fixation times) and behavioral intentions. In Study 2, 75 participants were recruited. It was found that marketing pictures combining natural sportscapes with prevention focus messages were selected more by the participants, with health communication images successfully attracting them to choose the sports program products being presented. The findings of the two studies suggested that marketing pictures can effectively stimulate consumers’ visual attention and has effects on their behavioral intention and choices toward exercising in a safe, natural environment.

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Section: Discussionmentioning

confidence: 99%

Visual Attention, Behavioral Intention, and Choice Behavior Among Older Consumers Toward Sports Marketing Images: An Eye-Tracking Study

2022

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“…For each UI description, 1 XAML and 1 classic C# description were randomly selected for the test subjects. Since the density of images or texts can affect information processing [35], comprehensibility and easy readability are also important points in UI descriptions, so it was tried to set the same distance between the texts and the source codes. A total of 36 XAML and 36 classic C#-based eye movement parameter packages were saved in a database for further evaluation.…”

Section: Test Conditions and Steps Of The Researchmentioning

confidence: 99%

An Eye Movement Study in Unconventional Usage of Different Software Tools

Katona

2023

Sensors

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One of the main challenges of Human-Computer Interaction is the creation of UIs that enable the use of different systems in an easy and understandable method. The study analyses the student audience who uses software tools differently from the basis. In the research, two languages supporting UI implementation related to .NET technology, XAML and classic C#, were compared in terms of the cognitive load of test subjects. The results of the traditional knowledge level assessment tests and the answers to the questionnaires show that the UI implementation described in XAML is easier to read and understand than the same description in classic C#. When viewing the source codes, the eye movement parameters of the test subjects were also recorded and then evaluated, where a significant difference in the number and duration of fixations was observed, i.e., the interpretation of the classic C# source code showed a larger cognitive load. Overall, the results of the eye movement parameters supported the results of the other two measurement methods when comparing the different types of UI descriptions. The results established in the study and its conclusion may have an impact on programming education as well as industrial software development in the future, and also clearly shows the importance of choosing the development technology that best suits the person or development team.

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“…However, interaction and spatial visualization in VEs are not the same as in reality. Human–computer interfaces (such as brain–computer interface (BCI) [ 8 , 9 , 10 ], eye-tracking [ 11 , 12 , 13 ], and other similar alternative communication interfaces between human and computer or mobile devices) and interaction can differ between applications [ 14 ]. Consequently, among others, I/O devices, the VR engine itself, and humans are integral parts of VR systems, and are equally important [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Examining the Results of Virtual Reality-Based Egocentric Distance Estimation Tests Based on Immersion Level

Guzsvinecz

Perge

Szűcs

2023

Sensors

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Depth perception as well as egocentric distance estimation can be trained in virtual spaces, although incorrect estimates can occur in these environments. To understand this phenomenon, a virtual environment with 11 changeable factors was created. Egocentric distance estimation skills of 239 participants were assessed with it in the range [25 cm, 160 cm]. One hundred fifty-seven people used a desktop display and seventy-two the Gear VR. According to the results, these investigated factors can have various effects combined with the two display devices on distance estimation and its time. Overall, desktop display users are more likely to accurately estimate or overestimate distances, and significant overestimations occur at 130 and 160 cm. With the Gear VR, distances in the range [40 cm, 130 cm] are significantly underestimated, while at 25 cm, they are significantly overestimated. Estimation times are significantly decreased with the Gear VR. When developing future virtual environments that require depth perception skills, developers should take these results into account.

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Measuring Cognition Load Using Eye-Tracking Parameters Based on Algorithm Description Tools

Cited by 37 publications

References 31 publications

Visual Attention, Behavioral Intention, and Choice Behavior Among Older Consumers Toward Sports Marketing Images: An Eye-Tracking Study

Visual Attention, Behavioral Intention, and Choice Behavior Among Older Consumers Toward Sports Marketing Images: An Eye-Tracking Study

An Eye Movement Study in Unconventional Usage of Different Software Tools

Examining the Results of Virtual Reality-Based Egocentric Distance Estimation Tests Based on Immersion Level

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