Revisiting visual attention identification based on eye tracking data analytics

2016 ◽  
Author(s):  
Yingxue Zhang ◽  
Zhenzhong Chen
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Data Analytics ◽  
Tracking Data

scholarly journals Are Photographs Worth More Than a Thousand Words? Examining the Effects of Photographic–Textual and Textual-Only Frames on Public Attitude Toward Nuclear Energy and Nanotechnology

2018 ◽  
Vol 95 (4) ◽  
pp. 948-970 ◽  
Author(s):  
Edmund W. J. Lee ◽  
Shirley S. Ho
Keyword(s):  
Risk Perception ◽  
Visual Attention ◽  
Eye Tracking ◽  
Nuclear Energy ◽  
Public Support ◽  
Public Attitude ◽  
Theory And Practice ◽  
Tracking Data ◽  
Subject Design ◽  
The Impact

This study examines the impact of photographic–textual and risk–benefit frames on the level of visual attention, risk perception, and public support for nuclear energy and nanotechnology in Singapore. Using a 2 (photographic–textual vs. textual-only frames) × 2 (risk vs. benefit frames) × 2 (nuclear energy vs. nanotechnology) between-subject design with eye-tracking data, the results showed that photographic–textual frames elicited more attention and did have partial amplification effect. However, this was observable only in the context of nuclear energy, where public support was lowest when participants were exposed to risk frames accompanied by photographs. Implications for theory and practice were discussed.

Triggering and Detecting Attentional Narrowing in Controlled Environments

2016 ◽  
Vol 60 (1) ◽  
pp. 298-302 ◽  
Author(s):  
Julie C. Prinet ◽  
Alexander C. Mize ◽  
Nadine Sarter
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Difficult Problem ◽  
Focused Attention ◽  
Tracking Data ◽  
Controlled Environments ◽  
Attention Performance ◽  
Anxiety Levels ◽  
And Performance ◽  
Source Of Information

Attentional narrowing refers to a state in which operators, involuntarily and unconsciously, fail to process a subset of potentially critical information. Little is known about factors that trigger the phenomenon and how to detect and distinguish it from a related state, focused attention, where one deliberately concentrates on one source of information. The objectives of this study were to (1) evaluate the effectiveness of three factors - workload, a novel difficult problem and incentive - for inducing attentional narrowing, and (2) identify markers of attentional narrowing and focused attention. Performance, eye-tracking data and anxiety levels were recorded while participants timeshared numerous tasks. When confronted with a novel problem, participants’ visual attention narrowed towards the affected task, and performance dropped on several tasks when all three factors were present. The findings from this study highlight promising means of inducing narrowing and show that eye-tracking, together with performance data, enable real-time detection of attentional narrowing.

scholarly journals Nutri-Score and Nutrition Facts Panel through the Eyes of the Consumer: Correct Healthfulness Estimations Depend on Transparent Labels, Fixation Duration, and Product Equivocality

Nutrients ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2915
Author(s):  
Saar Bossuyt ◽  
Kathleen Custers ◽  
José Tummers ◽  
Laura Verbeyst ◽  
Bert Oben
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Fixation Duration ◽  
Positive Impact ◽  
Estimation Accuracy ◽  
Tracking Data ◽  
Negative Effect ◽  
Set Up ◽  
Nutrition Facts Panel ◽  
The Impact

Research on front-of-pack labels (FOPLs) demonstrated that Nutri-Score is one of the most promising FOPLs regarding healthfulness estimation accuracy. Nevertheless, as consumers are exposed to both the Nutri-Score and the mandatory Nutrition Facts Panel (NFP) in the supermarket, it is key to understand if and how both labels interact. This study investigates the contribution of Nutri-Score and NFP regarding healthfulness estimation accuracy, whether this impact differs depending on the product, and what role visual attention plays. We set up an eye-tracking experiment in a controlled setting in which 398 participants rated the healthfulness of 20 products. The results confirmed the positive impact of the Nutri-Score on healthfulness estimation accuracy, though the impact was larger for equivocal (i.e., difficult to judge) products. Interestingly, NFP either had no effect (compared to a package without Nutri-Score or NFP) or a negative effect (compared to a package with Nutri-Score alone) on healthfulness estimation accuracy. Eye-tracking data corroborated that ‘cognitive overload’ issues could explain why consumers exposed to Nutri-Score alone outperformed those exposed to both Nutri-Score and NFP. This study offers food for thought for policymakers and the industry seeking to maximize the potential of the Nutri-Score.

Markov chain based computational visual attention model that learns from eye tracking data

2014 ◽  
Vol 49 ◽  
pp. 1-10 ◽  
Author(s):  
Ma Zhong ◽  
Xinbo Zhao ◽  
Xiao-chun Zou ◽  
James Z. Wang ◽  
Wenhu Wang
Keyword(s):  
Markov Chain ◽  
Visual Attention ◽  
Eye Tracking ◽  
Tracking Data ◽  
Visual Attention Model ◽  
Attention Model

scholarly journals Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning

2019 ◽  
Vol 12 (2) ◽  
Author(s):  
Sangwon Lee ◽  
Yongha Hwang ◽  
Yan Jin ◽  
Sihyeong Ahn ◽  
Jaewan Park
Keyword(s):  
Machine Learning ◽  
Visual Attention ◽  
Eye Tracking ◽  
Mathematical Models ◽  
Classification Algorithms ◽  
Tracking Data ◽  
The Impact

Machine learning, particularly classification algorithms, constructs mathematical models from labeled data that can predict labels for new data. Using its capability to identify distinguishing patterns among multi-dimensional data, we investigated the impact of three factors on the observation of architectural scenes: Individuality, education, and image stimuli. An analysis of the eye-tracking data revealed that (1) a velocity histogram was unique to individuals, (2) students of architecture and other disciplines could be distinguished via endogenous parameters, but (3) they were more distinct in terms of seeking structural versus symbolic elements. Because of the reverse nature of the classification algorithms that automatically learn from data, we could identify relevant parameters and distinguishing eye-tracking patterns that have not been reported in previous studies.

scholarly journals A Visual Attention Model Based on Eye Tracking in 3D Scene Maps

2021 ◽  
Vol 10 (10) ◽  
pp. 664
Author(s):  
Bincheng Yang ◽  
Hongwei Li
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Goodness Of Fit ◽  
Quantitative Relationship ◽  
Visual Saliency ◽  
Tracking Data ◽  
Visual Attention Model ◽  
Attention Model ◽  
Map Reading ◽  
3D Scene

Visual attention plays a crucial role in the map-reading process and is closely related to the map cognitive process. Eye-tracking data contains a wealth of visual information that can be used to identify cognitive behavior during map reading. Nevertheless, few researchers have applied these data to quantifying visual attention. This study proposes a method for quantitatively calculating visual attention based on eye-tracking data for 3D scene maps. First, eye-tracking technology was used to obtain the differences in the participants’ gaze behavior when browsing a street view map in the desktop environment, and to establish a quantitative relationship between eye movement indexes and visual saliency. Then, experiments were carried out to determine the quantitative relationship between visual saliency and visual factors, using vector 3D scene maps as stimulus material. Finally, a visual attention model was obtained by fitting the data. It was shown that a combination of three visual factors can represent the visual attention value of a 3D scene map: color, shape, and size, with a goodness of fit (R2) greater than 0.699. The current research helps to determine and quantify the visual attention allocation during map reading, laying the foundation for automated machine mapping.

Sign in / Sign up

Export Citation Format

Share Document