ArticleVisual cognition2009
SUN: Top-down saliency using natural statistics.
Article in Visual cognition, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
34 citing papers in PubMed.
- Where to look at the movies: Analyzing visual attention to understand movie editing.Behavior research methods · 2023Article
- BIAS-3D: Brain inspired attentional search model fashioned after what and where/how pathways for target search in 3D environment.Frontiers in computational neuroscience · 2022Article
- Searching in CCTV: effects of organisation in the multiplex.Cognitive research: principles and implications · 2021Article
- A novel fully convolutional network for visual saliency prediction.PeerJ. Computer science · 2020Article
- Salience Models: A Computational Cognitive Neuroscience Review.Vision (Basel, Switzerland) · 2019Review
- Cat and mouse search: the influence of scene and object analysis on eye movements when targets change locations during search.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017Article
- Joint Learning of Binocularly Driven Saccades and Vergence by Active Efficient Coding.Frontiers in neurorobotics · 2017Article
- How Well Can Saliency Models Predict Fixation Selection in Scenes Beyond Central Bias? A New Approach to Model Evaluation Using Generalized Linear Mixed Models.Frontiers in human neuroscience · 2017Article
- Article
- Control of gaze while walking: Task structure, reward, and uncertainty.Journal of vision · 2017Article
- Feature-based attention and spatial selection in frontal eye fields during natural scene search.Journal of neurophysiology · 2016Article
- Modeling peripheral visual acuity enables discovery of gaze strategies at multiple time scales during natural scene search.Journal of vision · 2015Article
- Preferential attention to animals and people is independent of the amygdala.Social cognitive and affective neuroscience · 2015Article
- Article
- Modeling task control of eye movements.Current biology : CB · 2014Review
- Predicting human visuomotor behaviour in a driving task.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2014Article
- Guidance of visual attention by semantic information in real-world scenes.Frontiers in psychology · 2014Review
- Finding and recognizing objects in natural scenes: complementary computations in the dorsal and ventral visual systems.Frontiers in computational neuroscience · 2014Article
- Modelling eye movements in a categorical search task.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2013Article
- The role of uncertainty and reward on eye movements in a virtual driving task.Journal of vision · 2012Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
When people try to find particular objects in natural scenes they make extensive use of knowledge about how and where objects tend to appear in a scene. Although many forms of such "top-down" knowledge have been incorporated into saliency map models of visual search, surprisingly, the role of object appearance has been infrequently investigated. Here we present an appearance-based saliency model derived in a Bayesian framework. We compare our approach with both bottom-up saliency algorithms as well as the state-of-the-art Contextual Guidance model of Torralba et al. (2006) at predicting human fixations. Although both top-down approaches use very different types of information, they achieve similar performance; each substantially better than the purely bottom-up models. Our experiments reveal that a simple model of object appearance can predict human fixations quite well, even making the same mistakes as people.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.