ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2017
A Model of the Superior Colliculus Predicts Fixation Locations during Scene Viewing and Visual Search.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- The role of event understanding in guiding attentional selection in real-world scenes: The Scene Perception & Event Comprehension Theory (SPECT).Attention, perception & psychophysics · 2026Review
- Combined conceptual and perceptual control of visual attention in search for real-world objects.Attention, perception & psychophysics · 2025Article
- Top-down instructions influence the attentional weight on color and shape dimensions during bidimensional search.Scientific reports · 2024Article
- Inhibitory tagging both speeds and strengthens saccade target selection in the superior colliculus during visual search.Journal of neurophysiology · 2024Article
- A brain-inspired object-based attention network for multiobject recognition and visual reasoning.Journal of vision · 2023Article
- A Midbrain Inspired Recurrent Neural Network Model for Robust Change Detection.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Salience by competitive and recurrent interactions: Bridging neural spiking and computation in visual attention.Psychological review · 2022Article
- DeepGaze III: Modeling free-viewing human scanpaths with deep learning.Journal of vision · 2022Article
- Weighting the factors affecting attention guidance during free viewing and visual search: The unexpected role of object recognition uncertainty.Journal of vision · 2022Article
- Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps.Journal of vision · 2022Article
- Training for object recognition with increasing spatial frequency: A comparison of deep learning with human vision.Journal of vision · 2021Article
- Neurally-constrained modeling of human gaze strategies in a change blindness task.PLoS computational biology · 2021Article
- The effect of target salience and size in visual search within naturalistic scenes under degraded vision.Journal of vision · 2021Article
- Salience Models: A Computational Cognitive Neuroscience Review.Vision (Basel, Switzerland) · 2019Review
- Specifying the precision of guiding features for visual search.Journal of experimental psychology. Human perception and performance · 2019Article
- Selective Influence and Sequential Operations: A Research Strategy for Visual Search.Visual cognition · 2019Article
- Article
- Occluded information is restored at preview but not during visual search.Journal of vision · 2018Article
- Saccadic inhibition interrupts ongoing oculomotor activity to enable the rapid deployment of alternate movement plans.Scientific reports · 2018Article
- Visual attention is not deployed at the endpoint of averaging saccades.PLoS biology · 2018Article
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3 authors.
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Abstract
Modern computational models of attention predict fixations using saliency maps and target maps, which prioritize locations for fixation based on feature contrast and target goals, respectively. But whereas many such models are biologically plausible, none have looked to the oculomotor system for design constraints or parameter specification. Conversely, although most models of saccade programming are tightly coupled to underlying neurophysiology, none have been tested using real-world stimuli and tasks. We combined the strengths of these two approaches in MASC, a model of attention in the superior colliculus (SC) that captures known neurophysiological constraints on saccade programming. We show that MASC predicted the fixation locations of humans freely viewing naturalistic scenes and performing exemplar and categorical search tasks, a breadth achieved by no other existing model. Moreover, it did this as well or better than its more specialized state-of-the-art competitors. MASC's predictive success stems from its inclusion of high-level but core principles of SC organization: an over-representation of foveal information, size-invariant population codes, cascaded population averaging over distorted visual and motor maps, and competition between motor point images for saccade programming, all of which cause further modulation of priority (attention) after projection of saliency and target maps to the SC. Only by incorporating these organizing brain principles into our models can we fully understand the transformation of complex visual information into the saccade programs underlying movements of overt attention. With MASC, a theoretical footing now exists to generate and test computationally explicit predictions of behavioral and neural responses in visually complex real-world contexts.
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