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Explainable deep learning reveals a behavioral strategy underlying human decisions in a spatial navigation task

Year: 2025

Type: conference-paper

Abstract: This paper uses a set of explainable AI (xAI) methods to study human behavior in a spatial navigation task.First, locomotion and gaze dynamics of human subjects recorded during the task were reproduced in a virtual environment and visual snapshots extracted from this simulation were used as a dataset.Second, the dataset was used to train a deep convolutional network to reproduce human navigation decisions.Third, network strategies used for image classification were analyzed using a combination of three xAI methods.Using this analysis, we discovered a specific oculomotor marker that indicated the behavioral strategy used by human participants in this task.We conclude that xAI is a promising approach to study human behavior in complex real-world tasks.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-181

Author: Youri MARQUISE

Institutions: Centre National de la Recherche Scientifique, Institut de la Vision, Inserm, Sorbonne Université

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4409480538

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