Visual search

By Jeremy M. Wolfe. Current Biology, 2010

1,072 citations recorded by the source corpus.

Originally Published in Attention , H. Pashler (Ed.), London, UK: University College London Press, 1998. Acknowledgements: I thank Sara Bennett, Greg Gancarz, Todd Horowitz, and Patricia O'Neill for comments on earlier drafts of this paper. I am also grateful to Anne Treisman for various discussions and to Hal Pashler and an anonymous reviewed for excellent editorial comments.Some parts of this review are based heavily on my previous review of this literature in Wolfe (1994a). This work was supported by NIH-NEI grant RO1-EY05087 and by AFOSR grant F49620-93-1-0407.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Sensory Systems

View Paper: https://www.semanticscholar.org/paper/a7c250563c1db47e0fa29e497a189e1a23577424

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Citations in the corpus

One-Shot Instance Segmentation (2018, background): “This task can be seen as an example-based version of the typical instance segmentation setup and is closely related to the everyday problem of visual search which has been studied extensively in human perception [54, 64].”
Humans, but Not Deep Neural Networks, Often Miss Giant Targets in Scenes. (2017, background): “In terms of computations, the human brain rapidly extracts information about the scene background [31, 43], other objects in the scene [13, 18, 35], and depth information [44, 45].”
Eye can read your mind: decoding gaze fixations to reveal categorical search targets. (2013, background): “High-medium displays (TA-HM, 40 trials) depicted one high-similarity distractor and three medium-similarity distractors, and high-medium-low (TA-HML, 44 trials) displays depicted one lowsimilarity distractor, one high-similarity distractor, and two medium-similarity distractors.”
Locus of dimension weighting: Preattentive or postselective? (2006, background): “One influential account of how salient feature differences in the field may be detected is Guided Search (GS; Cave & Wolfe, 1990; Wolfe, 1994, 1998).”
Visual search: a retrospective. (2011, background): “The main manipulation in this subﬁeld is to vary the number of distractors among which the target is embedded and measure either the time to ﬁnd the target (reaction time; Neisser, 1964) or the accuracy in detecting the target when the element display is brieﬂy presented (Estes & Taylor, 1964).”
Interface transparency and the psychosemantics of most (2011, background): “2 (Wolfe 1998; Halberda et al. 2006).”
Exploring set size effects in scenes: Identifying the objects of search (2008, background): “On the one hand, there is a rich tradition of characterizing search behaviour in terms of set size (e.g., Palmer, 1995; Wolfe, 1998b), but the stimuli in these studies were arrays of individuated objects, not scenes.1 Consequently, they cannot address whether, or how, scene constraints affect the…”
What do eyes reveal about the mind?: Algorithmic inference of search targets from fixations (2015, background): “, [84,82,58,13,19]) that suggest neurons to be biased in a top-down manner to render the target more salient.”
Guided Search 6.0: An updated model of visual search (2021, background): “A paper like this one would be an obvious place to discuss the evidence for each candidate feature, but this has been done several times recently (Wolfe, 2014, 2018; Wolfe & Horowitz, 2004; Wolfe & Horowitz, 2017), so the exercise will not be repeated here.”
Training Object Class Detectors from Eye Tracking Data (2014, background): “This tendency is particularly pronounced in visual search, as finding an object typically requires fixating it [51].”
Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision? (2013, background): “as soon as they are widely separated in color space [202].”
Creativity, attention and the memory for brands: an outdoor advertising field study (2015, methodology): “A limited number of bottom-up features guide visual attention (Wolfe 1994, 1998).”
Cues of working together fuel intrinsic motivation (2014, background): “This visual-search task requires sustained attention (Wolfe, 1998), allows for a measure of task attention through memory for puzzle-relevant details (i.”
Evolutionary derived modulations of attention to two common fear stimuli: Serpents and hostile humans (2012, methodology): “This is measured by the slope coefficient of a leastsquare fitted line estimated from plots of RTs across number of distractors (see Wolfe, 1998).”
Visual influence on in-store buying decisions: an eye-track experiment on the visual influence of packaging design (2007, background): “Wolfe (1998) extends the theory and describes visual attention influenced by visual basic features that stand out and lead the search.”
Predicting the Driver's Focus of Attention: The DR(eye)VE Project (2017, background): “visual search) has been extensively studied for decades [66], [74].”