scholarly journals Research on the Quantitative Method of Cognitive Loading in a Virtual Reality System

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 170
Author(s):  
Jian Lv ◽  
Xiaoping Xu ◽  
Ning Ding
Keyword(s):  
Virtual Reality ◽  
Cognitive Load ◽  
Eye Movement ◽  
Probabilistic Neural Network ◽  
Evaluation Model ◽  
Absolute Error ◽  
Interactive System ◽  
Movement Data ◽  
Saccade Length ◽  
Fixation Points

Aimed at the problem of how to objectively obtain the threshold of a user’s cognitive load in a virtual reality interactive system, a method for user cognitive load quantification based on an eye movement experiment is proposed. Eye movement data were collected in the virtual reality interaction process by using an eye movement instrument. Taking the number of fixation points, the average fixation duration, the average saccade length, and the number of the first mouse clicking fixation points as the independent variables, and the number of backward-looking times and the value of user cognitive load as the dependent variables, a cognitive load evaluation model was established based on the probabilistic neural network. The model was validated by using eye movement data and subjective cognitive load data. The results show that the absolute error and relative mean square error were 6.52%–16.01% and 6.64%–23.21%, respectively. Therefore, the model is feasible.

scholarly journals Eye-Tracking-Based Classification of Information Search Behavior Using Machine Learning: Evidence from Experiments in Physical Shops and Virtual Reality Shopping Environments

2020 ◽  
Vol 31 (3) ◽  
pp. 675-691 ◽  
Author(s):  
Jella Pfeiffer ◽  
Thies Pfeiffer ◽  
Martin Meißner ◽  
Elisa Weiß
Keyword(s):  
Virtual Reality ◽  
Recommender Systems ◽  
Eye Movement ◽  
Information Search ◽  
Search Behavior ◽  
Movement Data ◽  
Conversion Rates ◽  
Shopping Environments ◽  
Information Search Behavior

How can we tailor assistance systems, such as recommender systems or decision support systems, to consumers’ individual shopping motives? How can companies unobtrusively identify shopping motives without explicit user input? We demonstrate that eye movement data allow building reliable prediction models for identifying goal-directed and exploratory shopping motives. Our approach is validated in a real supermarket and in an immersive virtual reality supermarket. Several managerial implications of using gaze-based classification of information search behavior are discussed: First, the advent of virtual shopping environments makes using our approach straightforward as eye movement data are readily available in next-generation virtual reality devices. Virtual environments can be adapted to individual needs once shopping motives are identified and can be used to generate more emotionally engaging customer experiences. Second, identifying exploratory behavior offers opportunities for marketers to adapt marketing communication and interaction processes. Personalizing the shopping experience and profiling customers’ needs based on eye movement data promises to further increase conversion rates and customer satisfaction. Third, eye movement-based recommender systems do not need to interrupt consumers and thus do not take away attention from the purchase process. Finally, our paper outlines the technological basis of our approach and discusses the practical relevance of individual predictors.

scholarly journals Contact-Free Cognitive Load Recognition Based on Eye Movement

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Xin Liu ◽  
Tong Chen ◽  
Guoqiang Xie ◽  
Guangyuan Liu
Keyword(s):  
Cognitive Load ◽  
Eye Movement ◽  
Cognitive Load Theory ◽  
Recognition Rate ◽  
Support Vector ◽  
Load Theory ◽  
Whole Process ◽  
Movement Data ◽  
Mental Diseases ◽  
Contact Free

The cognitive overload not only affects the physical and mental diseases, but also affects the work efficiency and safety. Hence, the research of measuring cognitive load has been an important part of cognitive load theory. In this paper, we proposed a method to identify the state of cognitive load by using eye movement data in a noncontact manner. We designed a visual experiment to elicit human’s cognitive load as high and low state in two light intense environments and recorded the eye movement data in this whole process. Twelve salient features of the eye movement were selected by using statistic test. Algorithms for processing some features are proposed for increasing the recognition rate. Finally we used the support vector machine (SVM) to classify high and low cognitive load. The experimental results show that the method can achieve 90.25% accuracy in light controlled condition.

A Research of Employing Cognitive Load Theory in Science Education via Web-Pages

2015 ◽  
pp. 902-917
Author(s):  
Yuan-Cheng Lin ◽  
Ming-Hsun Shen ◽  
Chia-Ju Liu
Keyword(s):  
Science Education ◽  
Cognitive Load ◽  
Eye Movement ◽  
Cognitive Load Theory ◽  
Ministry Of Education ◽  
Multimedia Presentations ◽  
Teaching Resources ◽  
Load Theory ◽  
Movement Data ◽  
Movement Models

This study adopted Cognitive Load Theory (CLT) to investigate the influences of multimedia presentations on achievements of science learning and the correlations between eye-movement models under distinct multimedia combinations and learner-controlled modes. Three units from the Science Education Website set by the Ministry of Education (Tainan) to assist student learning were employed: Air and Combustion”, “Heat Effects toward Substances”, and “Healthy Diet.” This multifunctional website offers teaching resources, interesting experiments, inquiry experiments, virtual animations, multi-assessments, and supplementary materials, which are highly interactive and simulative. Six classes of fifth graders (n=192) participated in this study. Our findings showed that the combination of multimedia elements apparently influenced students' performance; the “animation + narration” group performed evidently better than the “animation + subtitles” group. When the animated subject matters were in small segments under the Segmentation Principle, multimedia presentations still brought affections to learning achievement, suggesting that the modality effect on students' learning exists constantly. Regarding the eye-movement models, this study focused mainly on discussing the “active-control mode” and “multimedia combination forms”. These eye movement data supplemented the evidences gained to identify the relevant results. In conclusion, inappropriate multimedia combinations may interfere with learning. More functions and information inputs do not guarantee better learning effects.

Comparison of Expert and Novice Eye Movement Behaviors during Landing Flight

2011 ◽  
Vol 383-390 ◽  
pp. 2556-2560 ◽  
Author(s):  
Wei Liu ◽  
Cheng Kun Liu ◽  
Da Min Zhuang ◽  
Zhong Qi Liu ◽  
Xiu Gan Yuan
Keyword(s):  
Eye Movement ◽  
Mental Workload ◽  
Movement Pattern ◽  
Flight Simulation ◽  
Flight Training ◽  
Fixation Time ◽  
Flight Performance ◽  
Movement Data ◽  
Expert And Novice ◽  
Fixation Points

Objective To study expert and novice eye movement pattern during simulated landing flight for providing references to evaluate flight performance and training of pilots. Methods The subjects were divided in to two group s of expert and novice according to their flight simulation experience. Eye movement data were recorded when they were performing landing task. Comparison of expert and novice flight performance data and eye movement data was made. Results It was found that the differences between expert and novice lay not only in flight performance but also in eye movement pattern. Performance of expert was better than novice. Expert had shorter fixation time, more fixation points, faster scan velocity, greater scan frequency and wider scan area than novice. It was also found that eye movement pattern of expert bring lower mental workload than novice. Conclusion Flight performance is related to eye movement pattern. Effective eye movement pattern is related to good flight performance. The analysis of eye movement indices can evaluate pilots’ flight performance and provide reference for flight training.

scholarly journals Research on visual physiological characteristics via virtual driving platform

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401771766
Author(s):  
Jieyu Fan ◽  
Shengdi Chen ◽  
Mingzhang Liang ◽  
Fengyuan Wang
Keyword(s):  
Eye Movement ◽  
Measured Data ◽  
Information Environment ◽  
Eye Tracker ◽  
Complex Environment ◽  
Source Information ◽  
Safe Driving ◽  
Movement Data ◽  
Visual Characteristics ◽  
Fixation Points

The transportation system is synthesized by people, vehicles, roads, and environment, and people factor is initiative and plays a key role in the intermediate link between complex environment and vehicles. Virtual driving test is designed to research driver’s dynamic visual characteristics under different road conditions. This article applies faceLAB 5.0 eye tracker, Blue Tiger virtual driving device, and others to record eye movement changes of a driver in different road conditions, collect driver’s eye movement data, and analyze eye movement variation. Also, by comparing, checking, and disciplinary analyzing the measured data in different driving phases, this article conducts an analysis of eye movement changes of a driver while driving. In virtual driving test, drivers have low blinking frequency and long blinking duration in started section. When the section is complicated, drivers have increasing blinking frequency but shorter blinking duration and disperse visual fixation points. In decelerating section, drivers slow down and stop the vehicle. The research results provide the basis for safe driving, when date is more than a variable value, which can determine the driver unsafe driving. The research also has an important and practical role in the research of the driver’s behavior process in multi-source information environment.

Age Differences in Eye Movements During Reading: Degenerative Problems or Compensatory Strategy?

2019 ◽  
Vol 24 (4) ◽  
pp. 297-311
Author(s):  
José David Moreno ◽  
José A. León ◽  
Lorena A. M. Arnal ◽  
Juan Botella
Keyword(s):  
Eye Movements ◽  
Eye Movement ◽  
Meta Analysis ◽  
Age Groups ◽  
Behavioral Changes ◽  
Aging Effects ◽  
Movement Data ◽  
Sentence Reading ◽  
The Difference ◽  
Eye Movements During Reading

Abstract. We report the results of a meta-analysis of 22 experiments comparing the eye movement data obtained from young ( Mage = 21 years) and old ( Mage = 73 years) readers. The data included six eye movement measures (mean gaze duration, mean fixation duration, total sentence reading time, mean number of fixations, mean number of regressions, and mean length of progressive saccade eye movements). Estimates were obtained of the typified mean difference, d, between the age groups in all six measures. The results showed positive combined effect size estimates in favor of the young adult group (between 0.54 and 3.66 in all measures), although the difference for the mean number of fixations was not significant. Young adults make in a systematic way, shorter gazes, fewer regressions, and shorter saccadic movements during reading than older adults, and they also read faster. The meta-analysis results confirm statistically the most common patterns observed in previous research; therefore, eye movements seem to be a useful tool to measure behavioral changes due to the aging process. Moreover, these results do not allow us to discard either of the two main hypotheses assessed for explaining the observed aging effects, namely neural degenerative problems and the adoption of compensatory strategies.

Do Successor Effects in Reading Reflect Parafoveal Processing? Evidence From Corpus-Based and Experimental Eye Movement Data

2014 ◽  
Author(s):  
Bernhard Angele ◽  
Elizabeth R. Schotter ◽  
Timothy Slattery ◽  
Tara L. Chaloukian ◽  
Klinton Bicknell ◽  
...  
Keyword(s):  
Eye Movement ◽  
Parafoveal Processing ◽  
Movement Data

scholarly journals Eye Movements in Response to Pain-Related Feelings in the Presence of Low and High Cognitive Loads

2020 ◽  
Vol 10 (5) ◽  
pp. 92
Author(s):  
Ramtin Zargari Marandi ◽  
Camilla Ann Fjelsted ◽  
Iris Hrustanovic ◽  
Rikke Dan Olesen ◽  
Parisa Gazerani
Keyword(s):  
Eye Movements ◽  
Cognitive Load ◽  
Eye Movement ◽  
Cognitive Processing ◽  
Pain Perception ◽  
Cognitive Task ◽  
Load Level ◽  
Task Load ◽  
Pupillary Responses ◽  
Movement Characteristics

The affective dimension of pain contributes to pain perception. Cognitive load may influence pain-related feelings. Eye tracking has proven useful for detecting cognitive load effects objectively by using relevant eye movement characteristics. In this study, we investigated whether eye movement characteristics differ in response to pain-related feelings in the presence of low and high cognitive loads. A set of validated, control, and pain-related sounds were applied to provoke pain-related feelings. Twelve healthy young participants (six females) performed a cognitive task at two load levels, once with the control and once with pain-related sounds in a randomized order. During the tasks, eye movements and task performance were recorded. Afterwards, the participants were asked to fill out questionnaires on their pain perception in response to the applied cognitive loads. Our findings indicate that an increased cognitive load was associated with a decreased saccade peak velocity, saccade frequency, and fixation frequency, as well as an increased fixation duration and pupil dilation range. Among the oculometrics, pain-related feelings were reflected only in the pupillary responses to a low cognitive load. The performance and perceived cognitive load decreased and increased, respectively, with the task load level and were not influenced by the pain-related sounds. Pain-related feelings were lower when performing the task compared with when no task was being performed in an independent group of participants. This might be due to the cognitive engagement during the task. This study demonstrated that cognitive processing could moderate the feelings associated with pain perception.

Challenges in Interpretability of Neural Networks for Eye Movement Data

2020 ◽  
Author(s):  
Ayush Kumar ◽  
Prantik Howlader ◽  
Rafael Garcia ◽  
Daniel Weiskopf ◽  
Klaus Mueller
Keyword(s):  
Neural Networks ◽  
Eye Movement ◽  
Movement Data
Sign in / Sign up

Export Citation Format

Share Document