Scene context guides eye movements during visual search.

By Mark B. Neider, Gregory J. Zelinsky. Vision Research, 2006

332 citations recorded by the source corpus.

How does scene context guide search behavior to likely target locations? We had observers search for scene-constrained and scene-unconstrained targets, and found that scene-constrained targets were detected faster and with fewer eye movements. Observers also directed more initial saccades to target-consistent scene regions and devoted more time to searching these regions. However, final checking fixations on target-inconsistent regions were common in target-absent trials, suggesting that scene context does not strictly confine search to likely target locations. We interpret these data as evidence for a rapid top-down biasing of search behavior by scene context to the target-consistent regions of a scene.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f6b95f0db5c14493c2c68e0e318b24fcd9917017

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Citations in the corpus

Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, neutral): “All these methods require considerable A u t h o r ' s p e r s o n a l”
Modeling Search for People in 900 Scenes: A combined source model of eye guidance (2009, positive): “They find evidence that scene segmentation and flexible semantic cues can be used very rapidly to bias search to regions associated with the target (see also Eckstein et al., 2006; Neider & Zelinsky, 2006).”
Humans, but Not Deep Neural Networks, Often Miss Giant Targets in Scenes. (2017, background): “The human brain rapidly processes a scene and utilizes relationships among objects and global properties of scenes to guide search toward likely target locations and facilitate target detection [4–6, 8, 10, 13–16].”
An eye movement analysis of multiple object tracking in a realistic environment (2008, background): “…eye movements, we ordinarily do make eye movements as we search, and analyses of these movements have revealed a great deal about underlying search processes (Chen & Zelinsky, 2006; Neider & Zelinsky, 2006a, 2006b; Zelinsky, 1996; Zelinsky, Rao, Hayhoe, & Ballard, 1997; Zelinsky & Sheinberg, 1997).”
Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. (2006, neutral): “…to play a key role in modulating where attention and eye movement go (Chun & Jiang, 1998; Davenport & Potter, 2004; DeGraef, 1992; Henderson, 2003; Neider & Zelinski, 2006; Noton & Stark, 1971; Oliva, Torralba, Castelhano & Henderson, 2004; Palmer, 1975; Tsotsos, Culhane, Wai, Lai, Davis & Nuflo,…”
Adaptive Gaze Control in Natural Environments (2009, background): “It has been demonstrated that visual search in two-dimensional images of natural scenes is influenced by learned scene statistics (Eckstein et al., 2006; Neider and Zelinsky, 2006; Torralba et al., 2006).”
The 35th Sir Frederick Bartlett Lecture: Eye movements and attention in reading, scene perception, and visual search (2009, background): “…eccentricity, and target–distractor similarity, and it handles a number of important findings on eye movements and visual search (X. Chen & Zelinsky, 2006; Dickinson & Zelinsky, 2005, 2007; Neider & Zelinsky, 2006a, 2006b; Zelinsky, 1996, 2001; Zelinsky & Loschky, 2005; Zelinsky & Sheinberg, 1997).”
Semantic Image Retrieval via Active Grounding of Visual Situations (2017, background): “This interaction of topdown expectations and bottom-up perception enables a human viewer to very quickly locate relevant aspects of the situation [4], [5].”
Exploring set size effects in scenes: Identifying the objects of search (2008, background): “…affecting search have done so without appeal to the set size concept (e.g., Brockmole & Henderson, 2006; Henderson, Weeks, & Hollingworth, 1999; Neider & Zelinsky, 2006b; Toet, Kooi, Bijl, & Valeton, 1998; Torralba et al., 2006), leaving unaddressed any interaction between these factors and…”
Breaking through Fast-Forwarding: Brand Information and Visual Attention (2008, background): “Nielsen Media Research estimates that 20% of U.S. homes had DVRs as of mid-2007 and predicts that this number will rise to greater than 40% by the close of the decade.”