ArticleJournal of vision2024
The visual experience dataset: Over 200 recorded hours of integrated eye movement, odometry, and egocentric video.
Article in Journal of vision, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Eccentricity-Constrained CNN Training Reveals Adaptive Information Coding Around the Visual Field.ArXiv · 2026Article
- Mobile eye tracking in the real world: Best practices.Journal of vision · 2026Article
- Natural visually guided behavior.Brain research · 2025Article
- Representation of locomotive action affordances in human behavior, brains, and deep neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Approaches to understanding natural behavior.Journal of vision · 2025Review
- Warm versus cool colors and their relation to color perception.Journal of vision · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
26 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We introduce the Visual Experience Dataset (VEDB), a compilation of more than 240 hours of egocentric video combined with gaze- and head-tracking data that offer an unprecedented view of the visual world as experienced by human observers. The dataset consists of 717 sessions, recorded by 56 observers ranging from 7 to 46 years of age. This article outlines the data collection, processing, and labeling protocols undertaken to ensure a representative sample and discusses the potential sources of error or bias within the dataset. The VEDB's potential applications are vast, including improving gaze-tracking methodologies, assessing spatiotemporal image statistics, and refining deep neural networks for scene and activity recognition. The VEDB is accessible through established open science platforms and is intended to be a living dataset with plans for expansion and community contributions. It is released with an emphasis on ethical considerations, such as participant privacy and the mitigation of potential biases. By providing a dataset grounded in real-world experiences and accompanied by extensive metadata and supporting code, the authors invite the research community to use and contribute to the VEDB, facilitating a richer understanding of visual perception and behavior in naturalistic settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.