Five Factors that Guide Attention in Visual Search

By Jeremy M. Wolfe, Todd Horowitz. Nature Human Behaviour, 2017

787 citations recorded by the source corpus.

How do we find what we are looking for? Even when the desired target is in the current field of view, we need to search because fundamental limits on visual processing make it impossible to recognize everything at once. Searching involves directing attention to objects that might be the target. This deployment of attention is not random. It is guided to the most promising items and locations by five factors discussed here: bottom-up salience, top-down feature guidance, scene structure and meaning, the previous history of search over timescales ranging from milliseconds to years, and the relative value of the targets and distractors. Modern theories of visual search need to incorporate all five factors and specify how these factors combine to shape search behaviour. An understanding of the rules of guidance can be used to improve the accuracy and efficiency of socially important search tasks, from security screening to medical image perception.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/2698aafcf86aeb52f96714f8a546e546875f1afb

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Citations in the corpus

Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention (2020, background): “While attention is at least as important for reading text as it is for viewing images [13, 78], integration of human gaze into neural attention mechanisms for natural language processing (NLP) tasks remains under-explored.”
The Science of Visual Data Communication: What Works (2021, methodology): “…as categorically different colors is often the first choice because the visual system processes color differences more efficiently than shape differences across the two-dimensional visual plane, as measured by performance in visual-search and texturesegmentation tasks (Wolfe & Horowitz, 2017).”
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.”
V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs (2023, positive): “To accomplish this task efficiently, top-down feature guidance and contextual scene guidance are two fundamental factors, guiding humans’ visual search process [51].”
Forty years after Feature Integration Theory (2020, background): “Color is a preattentive feature (Wolfe & Horowitz, 2017).”
Humans, but Not Deep Neural Networks, Often Miss Giant Targets in Scenes. (2017, background): “The guidance of eye movements and covert attention is fundamental to successful visual search [5, 7, 24–26].”
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents (2018, background): “, 2015), these models do not do a good job predicting task-driven search behavior (Wolfe and Horowitz, 2017).”