Computational modeling of visual salience alteration and its application to eye-movement data
Computational saliency map models have facilitated quantitative investigations into how bottom-up visual salience influences attention. Two primary approaches to modeling salience computation exist: one focuses on functional approximation, while the other explores neurobiological implementation. The...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1614468/full |
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| author | Yoshihisa Fujita Toshiya Murai Jun Miyata Jun Miyata |
| author_facet | Yoshihisa Fujita Toshiya Murai Jun Miyata Jun Miyata |
| author_sort | Yoshihisa Fujita |
| collection | DOAJ |
| description | Computational saliency map models have facilitated quantitative investigations into how bottom-up visual salience influences attention. Two primary approaches to modeling salience computation exist: one focuses on functional approximation, while the other explores neurobiological implementation. The former provides sufficient performance for applying saliency map models to eye-movement data analysis, whereas the latter offers hypotheses on how neuronal abnormalities affect visual salience. In this study, we propose a novel saliency map model that integrates both approaches. It handles diverse image-derived features, as seen in functional approximation models, while implementing center-surround competition—the core process of salience computation—via an artificial neural network, akin to neurobiological models. We evaluated our model using an open eye-movement dataset and confirmed that its predictive performance is comparable to the conventional saliency map model used in eye-movement analysis. Beyond eye-movement prediction, our model enables neural-level simulations of how neurobiological disturbances influence salience computation. Simulations showed that parameter changes for excitatory-inhibitory balance, baseline neural activity, and synaptic connection density affected the contrast between salient and non-salient objects—in other words—the weighting of salience. Finally, we demonstrated the model’s potential for quantifying changes in salience weighting as reflected in eye movements, highlighting its ability to bridge both predictive and neurobiological perspectives. These results present a novel strategy for investigating mechanisms underlying abnormal visual salience. |
| format | Article |
| id | doaj-art-a12767a2ee2f496c99e4719ab53b5863 |
| institution | Kabale University |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-a12767a2ee2f496c99e4719ab53b58632025-08-20T03:41:44ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-08-011910.3389/fnins.2025.16144681614468Computational modeling of visual salience alteration and its application to eye-movement dataYoshihisa Fujita0Toshiya Murai1Jun Miyata2Jun Miyata3Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, JapanDepartment of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, JapanDepartment of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, JapanDepartment of Psychiatry, Aichi Medical University, Aichi, JapanComputational saliency map models have facilitated quantitative investigations into how bottom-up visual salience influences attention. Two primary approaches to modeling salience computation exist: one focuses on functional approximation, while the other explores neurobiological implementation. The former provides sufficient performance for applying saliency map models to eye-movement data analysis, whereas the latter offers hypotheses on how neuronal abnormalities affect visual salience. In this study, we propose a novel saliency map model that integrates both approaches. It handles diverse image-derived features, as seen in functional approximation models, while implementing center-surround competition—the core process of salience computation—via an artificial neural network, akin to neurobiological models. We evaluated our model using an open eye-movement dataset and confirmed that its predictive performance is comparable to the conventional saliency map model used in eye-movement analysis. Beyond eye-movement prediction, our model enables neural-level simulations of how neurobiological disturbances influence salience computation. Simulations showed that parameter changes for excitatory-inhibitory balance, baseline neural activity, and synaptic connection density affected the contrast between salient and non-salient objects—in other words—the weighting of salience. Finally, we demonstrated the model’s potential for quantifying changes in salience weighting as reflected in eye movements, highlighting its ability to bridge both predictive and neurobiological perspectives. These results present a novel strategy for investigating mechanisms underlying abnormal visual salience.https://www.frontiersin.org/articles/10.3389/fnins.2025.1614468/fullvisual saliencesaliency mapcomputational modelneural networkeye movement |
| spellingShingle | Yoshihisa Fujita Toshiya Murai Jun Miyata Jun Miyata Computational modeling of visual salience alteration and its application to eye-movement data Frontiers in Neuroscience visual salience saliency map computational model neural network eye movement |
| title | Computational modeling of visual salience alteration and its application to eye-movement data |
| title_full | Computational modeling of visual salience alteration and its application to eye-movement data |
| title_fullStr | Computational modeling of visual salience alteration and its application to eye-movement data |
| title_full_unstemmed | Computational modeling of visual salience alteration and its application to eye-movement data |
| title_short | Computational modeling of visual salience alteration and its application to eye-movement data |
| title_sort | computational modeling of visual salience alteration and its application to eye movement data |
| topic | visual salience saliency map computational model neural network eye movement |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1614468/full |
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