Using Machine Learning Classification to Predict Social Inferencing Performance from Eye-tracking Data in Participants with and without Brain Injury

2021 ◽  
Vol 102 (10) ◽  
pp. e27
Author(s):  
Syeda Iffat Naz ◽  
Dawn Neumann ◽  
Rifat Mueid ◽  
Lauren Christopher
Keyword(s):  
Machine Learning ◽  
Brain Injury ◽  
Eye Tracking ◽  
Tracking Data ◽  
Machine Learning Classification

scholarly journals Correction to: “Using machine learning to detect events in eye-tracking data”

2018 ◽  
Vol 51 (1) ◽  
pp. 451-452
Author(s):  
Raimondas Zemblys ◽  
Diederick C. Niehorster ◽  
Kenneth Holmqvist
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Tracking Data

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 Distinctive Oculomotor Behaviors in Alzheimer's Disease and Frontotemporal Dementia

2021 ◽  
Vol 12 ◽  
Author(s):  
Carmen Lage ◽  
Sara López-García ◽  
Alexandre Bejanin ◽  
Martha Kazimierczak ◽  
Ignacio Aracil-Bolaños ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Eye Tracking ◽  
Frontotemporal Dementia ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Classification Algorithms ◽  
Learning Approaches ◽  
Machine Learning Classification

Oculomotor behavior can provide insight into the integrity of widespread cortical networks, which may contribute to the differential diagnosis between Alzheimer's disease and frontotemporal dementia. Three groups of patients with Alzheimer's disease, behavioral variant of frontotemporal dementia (bvFTD) and semantic variant of primary progressive aphasia (svPPA) and a sample of cognitively unimpaired elders underwent an eye-tracking evaluation. All participants in the discovery sample, including controls, had a biomarker-supported diagnosis. Oculomotor correlates of neuropsychology and brain metabolism evaluated with 18F-FDG PET were explored. Machine-learning classification algorithms were trained for the differentiation between Alzheimer's disease, bvFTD and controls. A total of 93 subjects (33 Alzheimer's disease, 24 bvFTD, seven svPPA, and 29 controls) were included in the study. Alzheimer's disease was the most impaired group in all tests and displayed specific abnormalities in some visually-guided saccade parameters, as pursuit error and horizontal prosaccade latency, which are theoretically closely linked to posterior brain regions. BvFTD patients showed deficits especially in the most cognitively demanding tasks, the antisaccade and memory saccade tests, which require a fine control from frontal lobe regions. SvPPA patients performed similarly to controls in most parameters except for a lower number of correct memory saccades. Pursuit error was significantly correlated with cognitive measures of constructional praxis and executive function and metabolism in right posterior middle temporal gyrus. The classification algorithms yielded an area under the curve of 97.5% for the differentiation of Alzheimer's disease vs. controls, 96.7% for bvFTD vs. controls, and 92.5% for Alzheimer's disease vs. bvFTD. In conclusion, patients with Alzheimer's disease, bvFTD and svPPA exhibit differentiating oculomotor patterns which reflect the characteristic neuroanatomical distribution of pathology of each disease, and therefore its assessment can be useful in their diagnostic work-up. Machine learning approaches can facilitate the applicability of eye-tracking in clinical practice.

scholarly journals Machine learning analysis of speech and eye tracking data to distinguish Alzheimer's clinic patients from healthy controls

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Thalia S. Field ◽  
Sally May Newton‐Mason ◽  
Sheetal Shajan ◽  
Oswald Barral ◽  
Hyeju Jang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Healthy Controls ◽  
Tracking Data ◽  
Learning Analysis
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