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Antoine Coutrot

Researcher at University of Nantes

Publications -  67
Citations -  1420

Antoine Coutrot is an academic researcher from University of Nantes. The author has contributed to research in topics: Gaze & Computer science. The author has an hindex of 16, co-authored 53 publications receiving 1001 citations. Previous affiliations of Antoine Coutrot include University of Grenoble & University College London.

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Journal ArticleDOI

Global Determinants of Navigation Ability

TL;DR: Cognitive abilities, at least for spatial navigation, are clustered according to economic wealth and gender inequalities globally, which has significant implications for cross-cultural studies and multi-center clinical trials using cognitive testing.
Proceedings ArticleDOI

A dataset of head and eye movements for 360° videos

TL;DR: This paper presents a novel dataset of 360° videos with associated eye and head movement data, which is a follow-up to the previous dataset for still images and its associated code is made publicly available to support research on visual attention for 360° content.
Journal ArticleDOI

How saliency, faces, and sound influence gaze in dynamic social scenes.

TL;DR: Faces, and particularly talking faces, are the features that best explain the gazes recorded, especially in the original soundtrack condition, and the proposed groundwork for an audiovisual saliency model is proposed.
Journal ArticleDOI

Face exploration dynamics differentiate men and women.

TL;DR: The gender of both the participant and the person being observed are the factors that most influence gaze patterns during face exploration, and it is demonstrated that female gazers follow a much more exploratory scanning strategy than males watching videos of another person.
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Toward personalized cognitive diagnostics of at-genetic-risk Alzheimer’s disease

TL;DR: Assessment of navigational behavior using the Sea Hero Quest app provides a means of discriminating healthy aging from genetically at-risk individuals of Alzheimer’s disease (AD), and it is demonstrated that high-risk preclinical cases can be reliably distinguished from low-risk participants using big-data spatial navigation benchmarks.