ArticlePLoS computational biology2022
Look twice: A generalist computational model predicts return fixations across tasks and species.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed, 18 citations in OpenAlex.
- Detection, Inspection, Return: An Object-Based Classification and Metric of Fixations in Complex Scenes.Open mind : discoveries in cognitive science · 2026Article
- Human-like monocular depth biases in deep neural networks.PLoS computational biology · 2025Article
- Distinct attentional characteristics of neurons with visual feature coding in the primate brain.Science advances · 2025Article
- Motor "laziness" constrains fixation selection in real-world tasks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Inactivation of face-selective neurons alters eye movements when free viewing faces.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Planning to revisit: Neural activity in refixation precursors.Journal of vision · 2023Article
- Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases.Advances in neural information processing systems · 2021Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
Abstract
Primates constantly explore their surroundings via saccadic eye movements that bring different parts of an image into high resolution. In addition to exploring new regions in the visual field, primates also make frequent return fixations, revisiting previously foveated locations. We systematically studied a total of 44,328 return fixations out of 217,440 fixations. Return fixations were ubiquitous across different behavioral tasks, in monkeys and humans, both when subjects viewed static images and when subjects performed natural behaviors. Return fixations locations were consistent across subjects, tended to occur within short temporal offsets, and typically followed a 180-degree turn in saccadic direction. To understand the origin of return fixations, we propose a proof-of-principle, biologically-inspired and image-computable neural network model. The model combines five key modules: an image feature extractor, bottom-up saliency cues, task-relevant visual features, finite inhibition-of-return, and saccade size constraints. Even though there are no free parameters that are fine-tuned for each specific task, species, or condition, the model produces fixation sequences resembling the universal properties of return fixations. These results provide initial steps towards a mechanistic understanding of the trade-off between rapid foveal recognition and the need to scrutinize previous fixation locations.
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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.