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A mathematical model of local and global attention in natural scene viewing
Understanding the decision process underlying gaze control is an important question in cognitive neuroscience with applications in diverse fields ranging from psychology to computer vision. The decision for choosing an upcoming saccade target can be framed as a selection process between two states:...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7769622/ https://www.ncbi.nlm.nih.gov/pubmed/33315888 http://dx.doi.org/10.1371/journal.pcbi.1007880 |
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author | Malem-Shinitski, Noa Opper, Manfred Reich, Sebastian Schwetlick, Lisa Seelig, Stefan A. Engbert, Ralf |
author_facet | Malem-Shinitski, Noa Opper, Manfred Reich, Sebastian Schwetlick, Lisa Seelig, Stefan A. Engbert, Ralf |
author_sort | Malem-Shinitski, Noa |
collection | PubMed |
description | Understanding the decision process underlying gaze control is an important question in cognitive neuroscience with applications in diverse fields ranging from psychology to computer vision. The decision for choosing an upcoming saccade target can be framed as a selection process between two states: Should the observer further inspect the information near the current gaze position (local attention) or continue with exploration of other patches of the given scene (global attention)? Here we propose and investigate a mathematical model motivated by switching between these two attentional states during scene viewing. The model is derived from a minimal set of assumptions that generates realistic eye movement behavior. We implemented a Bayesian approach for model parameter inference based on the model’s likelihood function. In order to simplify the inference, we applied data augmentation methods that allowed the use of conjugate priors and the construction of an efficient Gibbs sampler. This approach turned out to be numerically efficient and permitted fitting interindividual differences in saccade statistics. Thus, the main contribution of our modeling approach is two–fold; first, we propose a new model for saccade generation in scene viewing. Second, we demonstrate the use of novel methods from Bayesian inference in the field of scan path modeling. |
format | Online Article Text |
id | pubmed-7769622 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-77696222021-01-08 A mathematical model of local and global attention in natural scene viewing Malem-Shinitski, Noa Opper, Manfred Reich, Sebastian Schwetlick, Lisa Seelig, Stefan A. Engbert, Ralf PLoS Comput Biol Research Article Understanding the decision process underlying gaze control is an important question in cognitive neuroscience with applications in diverse fields ranging from psychology to computer vision. The decision for choosing an upcoming saccade target can be framed as a selection process between two states: Should the observer further inspect the information near the current gaze position (local attention) or continue with exploration of other patches of the given scene (global attention)? Here we propose and investigate a mathematical model motivated by switching between these two attentional states during scene viewing. The model is derived from a minimal set of assumptions that generates realistic eye movement behavior. We implemented a Bayesian approach for model parameter inference based on the model’s likelihood function. In order to simplify the inference, we applied data augmentation methods that allowed the use of conjugate priors and the construction of an efficient Gibbs sampler. This approach turned out to be numerically efficient and permitted fitting interindividual differences in saccade statistics. Thus, the main contribution of our modeling approach is two–fold; first, we propose a new model for saccade generation in scene viewing. Second, we demonstrate the use of novel methods from Bayesian inference in the field of scan path modeling. Public Library of Science 2020-12-14 /pmc/articles/PMC7769622/ /pubmed/33315888 http://dx.doi.org/10.1371/journal.pcbi.1007880 Text en © 2020 Malem-Shinitski et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Malem-Shinitski, Noa Opper, Manfred Reich, Sebastian Schwetlick, Lisa Seelig, Stefan A. Engbert, Ralf A mathematical model of local and global attention in natural scene viewing |
title | A mathematical model of local and global attention in natural scene viewing |
title_full | A mathematical model of local and global attention in natural scene viewing |
title_fullStr | A mathematical model of local and global attention in natural scene viewing |
title_full_unstemmed | A mathematical model of local and global attention in natural scene viewing |
title_short | A mathematical model of local and global attention in natural scene viewing |
title_sort | mathematical model of local and global attention in natural scene viewing |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7769622/ https://www.ncbi.nlm.nih.gov/pubmed/33315888 http://dx.doi.org/10.1371/journal.pcbi.1007880 |
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