ArticleCommunications biology2020
Modeling the effects of perisaccadic attention on gaze statistics during scene viewing.
Article in Communications biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A robotics-inspired scanpath model reveals the importance of uncertainty and semantic object cues for gaze guidance in dynamic scenes.Journal of vision · 2025Article
- Motor "laziness" constrains fixation selection in real-world tasks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Potsdam data set of eye movement on natural scenes (DAEMONS).Frontiers in psychology · 2024Article
- Objects guide human gaze behavior in dynamic real-world scenes.PLoS computational biology · 2023Article
- Faces in scenes attract rapid saccades.Journal of vision · 2023Article
- Look twice: A generalist computational model predicts return fixations across tasks and species.PLoS computational biology · 2022Article
- DeepGaze III: Modeling free-viewing human scanpaths with deep learning.Journal of vision · 2022Article
- Potsdam Eye-Movement Corpus for Scene Memorization and Search With Color and Spatial-Frequency Filtering.Frontiers in psychology · 2022Article
- A mathematical model of local and global attention in natural scene viewing.PLoS computational biology · 2020Article
- Task-dependence in scene perception: Head unrestrained viewing using mobile eye-tracking.Journal of vision · 2020Article
- Toward Dynamical Modeling of Infants' Looking Times.Wiley interdisciplinary reviews. Cognitive scienceArticle
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
How we perceive a visual scene depends critically on the selection of gaze positions. For this selection process, visual attention is known to play a key role in two ways. First, image-features attract visual attention, a fact that is captured well by time-independent fixation models. Second, millisecond-level attentional dynamics around the time of saccade drives our gaze from one position to the next. These two related research areas on attention are typically perceived as separate, both theoretically and experimentally. Here we link the two research areas by demonstrating that perisaccadic attentional dynamics improve predictions on scan path statistics. In a mathematical model, we integrated perisaccadic covert attention with dynamic scan path generation. Our model reproduces saccade amplitude distributions, angular statistics, intersaccadic turning angles, and their impact on fixation durations as well as inter-individual differences using Bayesian inference. Therefore, our result lend support to the relevance of perisaccadic attention to gaze statistics.
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