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Eye Movements

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Influence of training and expertise on deep neural network attention and human attention during a medical image classification task.

Journal of vision
In many different domains, experts can make complex decisions after glancing very briefly at an image. However, the perceptual mechanisms underlying expert performance are still largely unknown. Recently, several machine learning algorithms have been...

Artificial Intelligence in Eye Movements Analysis for Alzheimer's Disease Early Diagnosis.

Current Alzheimer research
As the world's population ages, Alzheimer's disease is currently the seventh most common cause of death globally; the burden is anticipated to increase, especially among middle-class and elderly persons. Artificial intelligence-based algorithms that ...

Influence of prior knowledge on eye movements to scenes as revealed by hidden Markov models.

Journal of vision
Human visual experience usually provides ample opportunity to accumulate knowledge about events unfolding in the environment. In typical scene perception experiments, however, participants view images that are unrelated to each other and, therefore, ...

[Research on eye movement data classification using support vector machine with improved whale optimization algorithm].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
When performing eye movement pattern classification for different tasks, support vector machines are greatly affected by parameters. To address this problem, we propose an algorithm based on the improved whale algorithm to optimize support vector mac...

A Cross-modality Deep Learning Method for Measuring Decision Confidence from Eye Movement Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Electroencephalography (EEG) signals can effectively measure the level of human decision confidence. However, it is difficult to acquire EEG signals in practice due to the ex-pensive cost and complex operation, while eye movement signals are much eas...

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

Journal of vision
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 have been processed into aggregate (e.g., fixations, saccades) or statistical ...

Automatic Recording of the Target Location During Smooth Pursuit Eye Movement Testing Using Video-Oculography and Deep Learning-Based Object Detection.

Translational vision science & technology
PURPOSE: To accurately record the movements of a hand-held target together with the smooth pursuit eye movements (SPEMs) elicited with video-oculography (VOG) combined with deep learning-based object detection using a single-shot multibox detector (S...

Machine learning-based classification of viewing behavior using a wide range of statistical oculomotor features.

Journal of vision
Since the seminal work of Yarbus, multiple studies have demonstrated the influence of task-set on oculomotor behavior and the current cognitive state. In more recent years, this field of research has expanded by evaluating the costs of abruptly switc...

Brief communication: Three errors and two problems in a recent paper: gazeNet: End-to-end eye-movement event detection with deep neural networks (Zemblys, Niehorster, and Holmqvist, 2019).

Behavior research methods
Zemblys et al. (Behavior Research Methods, 51(2), 840-864, 2019) reported on a method for the classification of eye-movements ("gazeNet"). I have found three errors and two problems with that paper that are explained herein. Error 1: The gazeNet clas...

Computational framework for fusing eye movements and spoken narratives for image annotation.

Journal of vision
Despite many recent advances in the field of computer vision, there remains a disconnect between how computers process images and how humans understand them. To begin to bridge this gap, we propose a framework that integrates human-elicited gaze and ...