scholarly journals Eye Movement and Pupil Size Constriction Under Discomfort Glare

2015 ◽  
Vol 56 (3) ◽  
pp. 1649-1656 ◽  
Author(s):  
Y. Lin ◽  
S. Fotios ◽  
M. Wei ◽  
Y. Liu ◽  
W. Guo ◽  
...  
Keyword(s):  
Eye Movement ◽  
Pupil Size ◽  
Discomfort Glare

Subjective and pupil responses to discomfort glare from small, high-luminance light sources

2018 ◽  
Vol 51 (4) ◽  
pp. 592-611 ◽  
Author(s):  
Y Tyukhova ◽  
CE Waters
Keyword(s):  
Pupil Size ◽  
Rating Scale ◽  
Light Sources ◽  
Background Luminance ◽  
High Luminance ◽  
Low Luminance ◽  
Point Rating Scale ◽  
Main Effect ◽  
Discomfort Glare ◽  
Relative Change

This study examined human subjective and pupil responses to small, high-luminance light sources seen against low-luminance backgrounds. Subjective judgements of glare using a seven-point rating scale and the change in pupil diameters following exposure to glare of 47 subjects were measured during evaluation of 36 conditions comprising three glare source luminances (20,000; 205,000; 750,000 cd/m2), two source positions (0°, 10°), two source sizes (10−5, 10−4 sr) and three background luminances (0.03; 0.3; 1 cd/m2). Data analysis suggests that the relative pupil size is correlated with subjective responses to discomfort glare to some extent (r = 0.659). Analysis of variance of relative pupil size measurements demonstrates a significant main effect of the background luminance suggesting that when the background luminance decreases, the relative pupil size increases. Relative pupil size shows the same trend as the relative change in illuminance at the eyes and the discomfort glare perception.

scholarly journals The study of pupil size and eye movement during deception using eye tracker

2016 ◽  
Vol 30 (2) ◽  
pp. 63-76
Author(s):  
Yeonsil Lee ◽  
LeeJangHan ◽  
Hoon Choi ◽  
Seok Chan Kim ◽  
Sang Hyun Lee ◽  
...  
Keyword(s):  
Eye Movement ◽  
Pupil Size ◽  
Eye Tracker

scholarly journals The World (of Warcraft) through the eyes of an expert

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3783 ◽  
Author(s):  
Yousri Marzouki ◽  
Valériane Dusaucy ◽  
Myriam Chanceaux ◽  
Sebastiaan Mathôt
Keyword(s):  
Eye Movement ◽  
Negative Correlation ◽  
Pupil Size ◽  
Visual Complexity ◽  
Mental Effort ◽  
Role Playing ◽  
World Of Warcraft ◽  
Movement Behavior ◽  
Standard Tool ◽  
Role Playing Game

Negative correlations between pupil size and the tendency to look at salient locations were found in recent studies (e.g., Mathôt et al., 2015). It is hypothesized that this negative correlation might be explained by the mental effort put by participants in the task that leads in return to pupil dilation. Here we present an exploratory study on the effect of expertise on eye-movement behavior. Because there is no available standard tool to evaluate WoW players’ expertise, we built an off-game questionnaire testing players’ knowledge about WoW and acquired skills through completed raids, highest rated battlegrounds, Skill Points, etc. Experts (N = 4) and novices (N = 4) in the massively multiplayer online role-playing game World of Warcraft (WoW) viewed 24 designed video segments from the game that differ in regards with their content (i.e, informative locations) and visual complexity (i.e, salient locations). Consistent with previous studies, we found a negative correlation between pupil size and the tendency to look at salient locations (experts, r =  − .17, p < .0001, and novices, r =  − .09, p < .0001). This correlation has been interpreted in terms of mental effort: People are inherently biased to look at salient locations (sharp corners, bright lights, etc.), but are able (i.e., experts) to overcome this bias if they invest sufficient mental effort. Crucially, we observed that this correlation was stronger for expert WoW players than novice players (Z =  − 3.3, p = .0011). This suggests that experts learned to improve control over eye-movement behavior by guiding their eyes towards informative, but potentially low-salient areas of the screen. These findings may contribute to our understanding of what makes an expert an expert.

scholarly journals Hubungan Penggunaan Smartphone terhadap Ketajaman Penglihatan

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Yonathan S. A. Angmalisang ◽  
Maya E. W. Moningka ◽  
Jimmy F. Rumampuk
Keyword(s):  
Visual Acuity ◽  
Light Intensity ◽  
Literature Review ◽  
Everyday Life ◽  
Eye Movement ◽  
Health Problem ◽  
Pupil Size ◽  
Blurred Vision ◽  
Retinal Stimulation ◽  
Smartphone Use

Abstract: Visual acuity is the ability of a person's eyes to distinguish the shapes and details of objects at a certain distance. Decreased visual acuity is still a health problem in society. A person's visual acuity is influenced by refraction, pupil size, light intensity, exposure time, retinal stimulation area, eye adaptation, and eye movement. The use of smartphones has become a necessity of everyday life in society. Several studies have shown that smartphone use can lead to decreased visual acuity. This study aimed to determine whether there is a relationship between smartphone use and visual acuity and the factors that can affect visual acuity due to smartphone use. The research design used was a literature review with journals that can be accessed free full text through PubMed and ClinicalKey. As a result, the smartphone use can lead to DED, myopia, dan blurred vision. In conclusion, there is a relationship between smartphone use and visual acuityKeywords: smartphone, visual acuity  Abstrak: Ketajaman penglihatan adalah kemampuan mata seseorang untuk membedakan bentuk dan detail objek pada jarak tertentu. Penurunan ketajaman penglihatan masih menjadi masalah kesehatan dalam masyarakat. Ketajaman penglihatan seseorang dipengaruhi oleh refraksi, ukuran pupil, intensitas cahaya, waktu pemaparan, area stimulasi retina, adaptasi mata, dan gerakan mata. Penggunaan smartphone sudah menjadi kebutuhan kehidupan sehari-hari dalam masyarakat. Beberapa penelitian menunjukkan bahwa penggunaan smartphone dapat menyebabkan penurunan ketajaman penglihatan. Tujuan penelitian untuk mengetahui apakah terdapat hubungan penggunaan smartphone terhadap ketajaman penglihatan dan faktor-faktor yang dapat mempengaruhi ketajaman penglihatan karena penggunaan smartphone. Desain penelitian yang dipakai adalah literature review dengan jurnal-jurnal yang dapat diakses secara gratis melalui PubMed dan ClinicalKey. Hasilnya menunjukkan bahwa penggunaan smartphone dapat mengakibatkan DED, miopia dan penglihatan kabur. Sebagai simpulan, terdapat hubungan penggunaan smartphone terhadap ketajaman penglihatanKata Kunci: smartphone, ketajaman penglihatan

scholarly journals Convolutional neural networks can decode eye movement data: A black box approach to predicting task from eye movements

2020 ◽  
Author(s):  
Zachary Jay Cole ◽  
Karl Kuntzelman ◽  
Michael D. Dodd ◽  
Matthew Johnson
Keyword(s):  
Neural Networks ◽  
Eye Movement ◽  
Convolutional Neural Networks ◽  
Pupil Size ◽  
General Pattern ◽  
Image Data ◽  
Black Box ◽  
Minimally Processed ◽  
Movement Data

Previous attempts to classify task from eye movement data have relied on model architectures designed to emulate theoretically defined cognitive processes, and/or data that has been processed into aggregate (e.g., fixations, saccades) or statistical (e.g., fixation density) features. _Black box_ convolutional neural networks (CNNs) are capable of identifying relevant features in raw and minimally processed data and images, but difficulty interpreting these model architectures has contributed to challenges in generalizing lab-trained CNNs to applied contexts. In the current study, a CNN classifier was used to classify task from two eye movement datasets (Exploratory and Confirmatory) in which participants searched, memorized, or rated indoor and outdoor scene images. The Exploratory dataset was used to tune the hyperparameters of the model, and the resulting model architecture was re-trained, validated, and tested on the Confirmatory dataset. The data were formatted into timelines (i.e., x-coordinate, y-coordinate, pupil size) and minimally processed images. To further understand the informational value of each component of the eye movement data, the timeline and image datasets were broken down into subsets with one or more components systematically removed. Classification of the timeline data consistently outperformed the image data. The Memorize condition was most often confused with Search and Rate. Pupil size was the least uniquely informative component when compared with the x- and y-coordinates. The general pattern of results for the Exploratory dataset was replicated in the Confirmatory dataset. Overall, the present study provides a practical and reliable black box solution to classifying task from eye movement data.

scholarly journals Fixation Duration and Pupil Size as Diagnostic Tools in Parkinson’s Disease

2021 ◽  
pp. 1-11
Author(s):  
Panagiota Tsitsi ◽  
Mattias Nilsson Benfatto ◽  
Gustaf Öqvist Seimyr ◽  
Olof Larsson ◽  
Prof Per Svenningsson ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Eye Movements ◽  
Eye Tracking ◽  
Eye Movement ◽  
Pupil Size ◽  
Clinical Investigation ◽  
Sampling Rate ◽  
Diagnostic Tools ◽  
Eye Tracker

Background: Visual and oculomotor problems are very common in Parkinson’s disease (PD) and by using eye-tracking such problems could be characterized in more detail. However, eye-tracking is not part of the routine clinical investigation of parkinsonism. Objective: To evaluate gaze stability and pupil size in stable light conditions, as well as eye movements during sustained fixation in a population of PD patients and healthy controls (HC). Methods: In total, 50 PD patients (66% males) with unilateral to mild-to-moderate disease (Hoehn & Yahr 1– 3, Schwab and England 70– 90% ) and 43 HC (37% males) were included in the study. Eye movements were recorded with Tobii Pro Spectrum, a screen-based eye tracker with a sampling rate of 1200 Hz. Logistic regression analysis was applied to investigate the strength of association of eye-movement measures with diagnosis. Results: Median pupil size (OR 0.811; 95% CI 0.666– 0.987; p = 0.037) and longest fixation period (OR 0.798; 95% CI 0.691-0.921; p = 0.002), were the eye-movement parameters that were independently associated with diagnosis, after adjustment for sex (OR 4.35; 95% CI 1.516– 12.483; p = 0.006) and visuospatial/executive score in Montreal Cognitive Assessment (OR 0.422; 95% CI 0.233– 0.764; p = 0.004). The area under the ROC curve was determined to 0.817; 95% (CI) 0.732– 0.901. Conclusion: Eye-tracking based measurements of gaze fixation and pupil reaction may be useful biomarkers of PD diagnosis. However, larger studies of eye-tracking parameters integrated into the screening of patients with suspected PD are necessary, to further investigate and confirm their diagnostic value.

scholarly journals Assessing Deception in Questionnaire Surveys With Eye-Tracking

2021 ◽  
Vol 12 ◽  
Author(s):  
Xinyue Fang ◽  
Yiteng Sun ◽  
Xinyi Zheng ◽  
Xinrong Wang ◽  
Xuemei Deng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Eye Tracking ◽  
Eye Movement ◽  
Pupil Size ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Detection Of Deception ◽  
Questionnaire Surveys ◽  
Sensitive Issues

Deceit often occurs in questionnaire surveys, which leads to the misreporting of data and poor reliability. The purpose of this study is to explore whether eye-tracking could contribute to the detection of deception in questionnaire surveys, and whether the eye behaviors that appeared in instructed lying still exist in spontaneous lying. Two studies were conducted to explore eye movement behaviors in instructed and spontaneous lying conditions. The results showed that pupil size and fixation behaviors are both reliable indicators to detect lies in questionnaire surveys. Blink and saccade behaviors do not seem to predict deception. Deception resulted in increased pupil size, fixation count and duration. Meanwhile, respondents focused on different areas of the questionnaire when lying versus telling the truth. Furthermore, in the actual deception situation, the linear support vector machine (SVM) deception classifier achieved an accuracy of 74.09%. In sum, this study indicates the eye-tracking signatures of lying are not restricted to instructed deception, demonstrates the potential of using eye-tracking to detect deception in questionnaire surveys, and contributes to the questionnaire surveys of sensitive issues.

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