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 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.