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

ABSTRACT: 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 (e.g., fixation density) features. Black box convolutional neural networks (CNNs) are capable of identifying relevant features in raw and minimally processed data and images, but difficulty interpreting these model architectures has contributed to challenges in generalizing lab-trained CNNs to applied contexts. In the current study, a CNN classifier was used to classify task from two eye movement datasets (Exploratory and Confirmatory) in which participants searched, memorized, or rated indoor and outdoor scene images. The Exploratory dataset w

SUBMITTER: Cole ZJ 

PROVIDER: S-EPMC8288051 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature