An R based interface to understand cognitive ability of different participants using fixation and saccade detection

2019 ◽  
Vol 10 (3) ◽  
pp. 2127-2131
Author(s):  
Akshay S ◽  
Ashika P ◽  
Aswathy Ramesh
Keyword(s):  
Computer Vision ◽  
Eye Tracking ◽  
Cognitive Ability ◽  
Cognitive Abilities ◽  
Fixation Duration ◽  
Experimental Results ◽  
Tracking Data ◽  
Detailed Survey ◽  
Wide Range ◽  
The Difference

Eye-tracking is an emerging area of science in a wide range of computer vision-based applications. Eye-tracking mainly deals with where the person is looking at and for what duration. In this work, we propose an R based interface to visualize the eye-tracking data as fixations and saccades that depicts where the person looking at –fixations and saccades and what duration – fixation duration. Through the eye-tracking metrics that are visualized in our work, one can visualize the difference between the viewing behaviour of various participants. The differences thus depicted can later be studied in order to understand the cognitive abilities of the participants. The paper contains a detailed survey of the existing literature and the experimental results generated using the R interface.

scholarly journals Customized Dictionary Learning for Subdatasets with Fine Granularity

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Lei Ye ◽  
Can Wang ◽  
Xin Xu ◽  
Hui Qian
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Theoretical Analysis ◽  
Improve Performance ◽  
Real World Data ◽  
Sparse Models ◽  
Learned Dictionary ◽  
Wide Range ◽  
Sparsity Constraints ◽  
The Difference

Sparse models have a wide range of applications in machine learning and computer vision. Using a learned dictionary instead of an “off-the-shelf” one can dramatically improve performance on a particular dataset. However, learning a new one for each subdataset (subject) with fine granularity may be unwarranted or impractical, due to restricted availability subdataset samples and tremendous numbers of subjects. To remedy this, we consider the dictionary customization problem, that is, specializing an existing global dictionary corresponding to the total dataset, with the aid of auxiliary samples obtained from the target subdataset. Inspired by observation and then deduced from theoretical analysis, a regularizer is employed penalizing the difference between the global and the customized dictionary. By minimizing the sum of reconstruction errors of the above regularizer under sparsity constraints, we exploit the characteristics of the target subdataset contained in the auxiliary samples while maintaining the basic sketches stored in the global dictionary. An efficient algorithm is presented and validated with experiments on real-world data.

scholarly journals Multivariate mapping for experienced users: comparing extrinsic and intrinsic maps with univariate maps

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jolanta Korycka-Skorupa ◽  
Izabela Gołębiowska
Keyword(s):  
Eye Tracking ◽  
Task Difficulty ◽  
Performance Metrics ◽  
Cognitive Effort ◽  
Subjective Rating ◽  
Tracking Data ◽  
Three Solutions ◽  
Visual Variables ◽  
The Difference ◽  
Task Types

Abstract Multivariate mapping is a technique in which multivariate data are encoded into a single map. A variety of design solutions for multivariate mapping refers to the number of phenomena mapped, the map type, and the visual variables applied. Unlike other authors who have mainly evaluated bivariate maps, in our empirical study we compared three solutions when mapping four variables: two types of multivariate maps (intrinsic and extrinsic) and a simple univariate alternative (serving as a baseline). We analysed usability performance metrics (answer time, answer accuracy, subjective rating of task difficulty) and eye-tracking data. The results suggested that experts used all the tested maps with similar results for answer time and accuracy, even when using four-variable intrinsic maps, which is considered to be a challenging solution. However, eye-tracking data provided more nuances in relation to the difference in cognitive effort evoked by the tested maps across task types.

scholarly journals Intuitiveness of geospatial uncertainty visualizations: a user study on point symbols

Geografie ◽  
2019 ◽  
Vol 124 (2) ◽  
pp. 163-185 ◽  
Author(s):  
Jan Brus ◽  
Michal Kučera ◽  
Stanislav Popelka
Keyword(s):  
Eye Tracking ◽  
Spatial Data ◽  
User Study ◽  
User Preferences ◽  
Tracking Data ◽  
User Testing ◽  
The Difference ◽  
Point Symbol ◽  
User Centered

Be understanding of uncertainty, or the difference between a real geographic phenomenon and the user’s understanding of that phenomenon, is essential for those who work with spatial data. From this perspective, map symbols can be used as a tool for providing information about the level of uncertainty. Nevertheless, communicating uncertainty to the user in this way can be a challenging task. Be main aim of the paper is to propose intuitive symbols to represent uncertainty. Bis goal is achieved by user testing of specially compiled point symbol sets. Emphasis is given to the intuitiveness and easy interpretation of proposed symbols. Symbols are part of a user-centered eye-tracking experiment designed to evaluate the suitability of the proposed solutions. Eye-tracking data is analyzed to determine the subject’s performance in reading the map symbols. Be analyses include the evaluation of observed parameters, user preferences, and cognitive metrics. Based on these, the most appropriate methods for designing point symbols are recommended and discussed.

scholarly journals Predicting Spatial Visualization Problems’ Difficulty Level from Eye-Tracking Data

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1949
Author(s):  
Xiang Li ◽  
Rabih Younes ◽  
Diana Bairaktarova ◽  
Qi Guo
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Eye Movement ◽  
Spatial Visualization ◽  
Difficulty Level ◽  
Tracking Data ◽  
Multiple Choice Questions ◽  
Essential Information ◽  
E Learning ◽  
The Difference

The difficulty level of learning tasks is a concern that often needs to be considered in the teaching process. Teachers usually dynamically adjust the difficulty of exercises according to the prior knowledge and abilities of students to achieve better teaching results. In e-learning, because there is no teacher involvement, it often happens that the difficulty of the tasks is beyond the ability of the students. In attempts to solve this problem, several researchers investigated the problem-solving process by using eye-tracking data. However, although most e-learning exercises use the form of filling in blanks and choosing questions, in previous works, research focused on building cognitive models from eye-tracking data collected from flexible problem forms, which may lead to impractical results. In this paper, we build models to predict the difficulty level of spatial visualization problems from eye-tracking data collected from multiple-choice questions. We use eye-tracking and machine learning to investigate (1) the difference of eye movement among questions from different difficulty levels and (2) the possibility of predicting the difficulty level of problems from eye-tracking data. Our models resulted in an average accuracy of 87.60% on eye-tracking data of questions that the classifier has seen before and an average of 72.87% on questions that the classifier has not yet seen. The results confirmed that eye movement, especially fixation duration, contains essential information on the difficulty of the questions and it is sufficient to build machine-learning-based models to predict difficulty level.

scholarly journals How Face-to-Face Interviews and Cognitive Skill Affect Item Non-Response: A Randomized Experiment Assigning Mode of Interview

2016 ◽  
Vol 7 (1) ◽  
pp. 143-162 ◽  
Author(s):  
Andrew Gooch ◽  
Lynn Vavreck
Keyword(s):  
Cognitive Ability ◽  
Survey Research ◽  
Cognitive Abilities ◽  
Adult Population ◽  
Cognitive Skill ◽  
The Self ◽  
Randomized Experiment ◽  
Face To Face ◽  
Low Levels ◽  
The Difference

Technology and the decreased cost of survey research have made it possible for researchers to collect data using new and varied modes of interview. These data are often analyzed as if they were generated using similar processes, but the modes of interview may produce differences in response simply due to the presence or absence of an interviewer. In this paper, we explore the differences in item non-response that result from different modes of interview and find that mode makes a difference. The data are from an experiment in which we randomly assigned an adult population to an in-person or self-completed survey after subjects agreed to participate in a short poll. For nearly every topic and format of question, we find less item non-response in the self-complete mode. Furthermore, we find the difference across modes in non-response is exacerbated for respondents with low levels of cognitive abilities. Moving from high to low levels of cognitive ability, an otherwise average respondent can be up to six times more likely to say “don’t know” in a face-to-face interview than in a self-completed survey, depending on the type of question.

scholarly journals Product Recommendation Based on Eye Tracking Data Using Fixation Duration

2021 ◽  
Vol 5 (4) ◽  
pp. 109
Author(s):  
Juni Nurma Sari ◽  
Lukito Edi Nugroho ◽  
Paulus Insap Santosa ◽  
Ridi Ferdiana
Keyword(s):  
Eye Tracking ◽  
Collaborative Filtering ◽  
Fixation Duration ◽  
Product Information ◽  
Sensor Technology ◽  
Tracking Data ◽  
Eye Tracker ◽  
Product Recommendation ◽  
User Attention ◽  
Product Recommendations

E-commerce can be used to increase companies or sellers’ profits. For consumers, e-commerce can help them shop faster. The weakness of e-commerce is that there is too much product information presented in the catalog which in turn makes consumers confused. The solution is by providing product recommendations. As the development of sensor technology, eye tracker can capture user attention when shopping. The user attention was used as data of consumer interest in the product in the form of fixation duration following the Bojko taxonomy. The fixation duration data was processed into product purchase prediction data to know consumers’ desire to buy the products by using Chandon method. Both data could be used as variables to make product recommendations based on eye tracking data. The implementation of the product recommendations based on eye tracking data was an eye tracking experiment at selvahouse.com which sells hijab and women modest wear. The result was a list of products that have similarities to other products. The product recommendation method used was item-to-item collaborative filtering. The novelty of this research is the use of eye tracking data, namely the fixation duration and product purchase prediction data as variables for product recommendations. Product recommendation that produced by eye tracking data can be solution of product recommendation’s problems, namely sparsity and cold start.

scholarly journals Eye-tracking glasses in face-to-face interactions: Manual versus automated assessment of areas-of-interest

2021 ◽  
Author(s):  
Chiara Jongerius ◽  
T. Callemein ◽  
T. Goedemé ◽  
K. Van Beeck ◽  
J. A. Romijn ◽  
...  
Keyword(s):  
Computer Vision ◽  
Eye Tracking ◽  
Interrater Reliability ◽  
Tracking Data ◽  
Dynamic Interactions ◽  
Area Of Interest ◽  
Gaze Behaviour ◽  
Vision Algorithm ◽  
Areas Of Interest ◽  
Mobile Eye Tracking

AbstractThe assessment of gaze behaviour is essential for understanding the psychology of communication. Mobile eye-tracking glasses are useful to measure gaze behaviour during dynamic interactions. Eye-tracking data can be analysed by using manually annotated areas-of-interest. Computer vision algorithms may alternatively be used to reduce the amount of manual effort, but also the subjectivity and complexity of these analyses. Using additional re-identification (Re-ID) algorithms, different participants in the interaction can be distinguished. The aim of this study was to compare the results of manual annotation of mobile eye-tracking data with the results of a computer vision algorithm. We selected the first minute of seven randomly selected eye-tracking videos of consultations between physicians and patients in a Dutch Internal Medicine out-patient clinic. Three human annotators and a computer vision algorithm annotated mobile eye-tracking data, after which interrater reliability was assessed between the areas-of-interest annotated by the annotators and the computer vision algorithm. Additionally, we explored interrater reliability when using lengthy videos and different area-of-interest shapes. In total, we analysed more than 65 min of eye-tracking videos manually and with the algorithm. Overall, the absolute normalized difference between the manual and the algorithm annotations of face-gaze was less than 2%. Our results show high interrater agreements between human annotators and the algorithm with Cohen’s kappa ranging from 0.85 to 0.98. We conclude that computer vision algorithms produce comparable results to those of human annotators. Analyses by the algorithm are not subject to annotator fatigue or subjectivity and can therefore advance eye-tracking analyses.

scholarly journals The Effect of Make a Match Learning Models and Cognitive Ability to Recognize the Concept of Number

2020 ◽  
Vol 3 (4) ◽  
pp. 1873-1883
Author(s):  
Siti Rahmatunnisa ◽  
Anita Yus ◽  
Evi Eviyanti
Keyword(s):  
Cognitive Ability ◽  
Cognitive Abilities ◽  
Significant Interaction ◽  
Learning Model ◽  
Learning Models ◽  
Number Concept ◽  
Average Value ◽  
Model Based ◽  
The Difference ◽  
Banda Aceh

This study aims to investigate: (1) the difference of ability to recognize the concept of numbers between children taught by Make a Match learning model based on creativity and children taught by Make a Match learning model; (2) the difference of ability to recognize the concept of numbers between children who have high cognitive abilities and low cognitive abilities, and (3) the interaction between Make a Match learning model with children's cognitive abilities on the ability to recognize concepts. The sample in this study is 28 children in class B1 for the experimental class who are taught by Make a Match based on creativity and for the control class, class B2 consisted of 28 children who were taught by Make a Match.  They are 5-6 years old children  at Raudhatul Athfal Mutiara Bunda Banda Aceh. The results show that: (1) The ability of children who taught by Make a Match learning model based on creativity  is 44.96, it is higher than children who taught by Make A Match learning, it is  34.64, (2) The ability to recognize the number concept of children who have high cognitive abilities obtained an average value of = 46.89, while children who have low cognitive abilities obtained an average value of = 33.21,  and (3) The results show that there was no significant interaction between the use of learning and children's cognitive abilities (high and low) in influencing the ability to recognize the concept of numbers.

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.

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