Modeling Human Eye Movements with Neural Networks in a Maze-Solving Task

doi:10.32470/ccn.2022.1291-0

Paper

Modeling Human Eye Movements with Neural Networks in a Maze-Solving Task

Published Dec 20, 2022 · Jason Li, Nicholas Watters, Yingting Wang

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Abstract

From smoothly pursuing moving objects to rapidly shifting gazes during visual search, humans employ a wide variety of eye movement strategies in different contexts. While eye movements provide a rich window into mental processes, building generative models of eye movements is notoriously difficult, and to date the computational objectives guiding eye movements remain largely a mystery. In this work, we tackled these problems in the context of a canonical spatial planning task, maze-solving. We collected eye movement data from human subjects and built deep generative models of eye movements using a novel differentiable architecture for gaze fixations and gaze shifts. We found that human eye movements are best predicted by a model that is optimized not to perform the task as efficiently as possible but instead to run an internal simulation of an object traversing the maze. This not only provides a generative model of eye movements in this task but also suggests a computational theory for how humans solve the task, namely that humans use mental simulation.

Study Snapshot

Key takeawayHuman eye movements in a maze-solving task are best predicted by a model that optimizes for internal simulation of an object traversing the maze, suggesting mental simulation as a computational theory.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

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Paper

Modeling Human Eye Movements with Neural Networks in a Maze-Solving Task

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References

Citations

Computational basis of hierarchical and counterfactual information processing

Human decision-making strategies, such as hierarchical and counterfactual reasoning, are a continuum of computationally rational solutions under various constraints, including optimal, counterfactual, postdictive, and hierarchical.

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Exploring Foveation and Saccade for Improved Weakly-Supervised Localization

Incorporating foveation and saccades in a weakly-supervised object localization framework improves object detection quality and resilience, aligning with human perception.

Planning By Active Sensing

This study developed an algorithm that rapidly plans under uncertainty by actively sensing the environment, using visual samples to compute optimal paths in complex mazes.