A novel machine learning analysis of eye-tracking data reveals suboptimal visual information extraction from facial stimuli in individuals with autism

AbstractVisual scanning plays an important role in sampling visual information from the surrounding environments for a lot of everyday sensorimotor tasks, such as walking and car driving. In this paper, we consider the problem of visual scanning mechanism underpinning sensorimotor tasks in 3D dynamic environments. We exploit the use of eye tracking data as a behaviometric, for indicating the visuo-motor behavioral measures in the context of virtual driving. A new metric of visual scanning efficiency (VSE), which is defined as a mathematical divergence between a fixation distribution and a distribution of optical flows induced by fixations, is proposed by making use of a widely-known information theoretic tool, namely the square root of Jensen-Shannon divergence. Based on the proposed efficiency metric, a cognitive effort measure (CEM) is developed by using the concept of quantity of information. Psychophysical eye tracking studies, in virtual reality based driving, are conducted to reveal that the new metric of visual scanning efficiency can be employed very well as a proxy evaluation for driving performance. In addition, the effectiveness of the proposed cognitive effort measure is demonstrated by a strong correlation between this measure and pupil size change. These results suggest that the exploitation of eye tracking data provides an effective behaviometric for sensorimotor activity.

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

Sensors ◽

10.3390/s20071949 ◽

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.

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Machine Learning Algorithm to Identify Eye Movement Metrics using Raw Eye Tracking Data

2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT) ◽

10.1109/icssit48917.2020.9214290 ◽

2020 ◽

Author(s):

S Akshay ◽

Y J Megha ◽

Chethan Babu Shetty

Keyword(s):

Machine Learning ◽

Eye Tracking ◽

Eye Movement ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Tracking Data

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Application of mathematical and machine learning techniques to analyse eye-tracking data enabling better understanding of children’s visual-cognitive behaviours

10.14786/flr.v6i3.365 ◽

2018 ◽

pp. 72-84

Author(s):

Enrique Garcia Moreno-Esteva ◽

Sonia L. J. White ◽

Joanne M. Wood ◽

Alex A. Black

Keyword(s):

Machine Learning ◽

Eye Tracking ◽

Machine Learning Techniques ◽

Tracking Data ◽

Learning Techniques ◽

Cognitive Behaviours ◽

Visual Cognitive

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Random Spray Retinex Extensions Considering Region of Interest and Eye Movements

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2019.63.6.060403 ◽

2019 ◽

Vol 63 (6) ◽

pp. 60403-1-60403-6

Author(s):

Midori Tanaka ◽

Matteo Paolo Lanaro ◽

Takahiko Horiuchi ◽

Alessandro Rizzi

Keyword(s):

Information Processing ◽

Eye Movements ◽

Visual Field ◽

Eye Tracking ◽

Mathematical Description ◽

Visual Information ◽

Tracking System ◽

Region Of Interest ◽

Tracking Data ◽

Proposed Model

Abstract The Random spray Retinex (RSR) algorithm was developed by taking into consideration the mathematical description of Milano-Retinex. The RSR substituted random paths with random sprays. Mimicking some characteristics of the human visual system (HVS), this article proposes two variants of RSR adding a mechanism of region of interest (ROI). In the first proposed model, a cone distribution based on anatomical data is considered as ROI. In the second model, the visual resolution depending on the visual field based on the knowledge of visual information processing is considered as ROI. We have measured actual eye movements using an eye-tracking system. By using the eye-tracking data, we have simulated the HVS using test images. Results show an interesting qualitative computation of the appearance of the processed area around real gaze points.

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