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In GS6, this guidance comes from five sources of preattentive information: (1) top-down and (2) bottom-up feature guidance, (3) prior history (e.g., priming), (4) reward, and (5) scene syntax and semantics. These sources are combined into a spatial priority map, a dynamic attentional landscape that evolves over the course of search.
A large body of work describes the evidence that different stimulus features can guide search (e.g., color, motion, etc.). Other work documents that there are plausible features that do not guide search: for example, intersection type (Wolfe & DiMase, 2003) or surface material (Wolfe & Myers, 2010).
Attention is "guided" so that items can be processed in an intelligent order. In GS6, this guidance comes from five sources of preattentive information: (1) top-down and (2) bottom-up feature guidance, (3) prior history (e.g., priming), (4) reward, and (5) scene syntax and semantics.
5 Φεβ 2021 · The most recent Guided Search model (GS4, Wolfe, 2007) combines serial and parallel processes, and adopts these from Optimal Foraging Theory within a Bayesian framework in which observers are continually updating their estimates of target probability in the current display.
1 Ιαν 2010 · Guided Search (Wolfe, Cave, & Franzel, 1989) borrowed this essentially linear architecture and added the notion that the preattentive stage could guide the deployment of attention in the...
5 Φεβ 2021 · Wolfe et al. propose the theory of "guided search" [8], a first attempt to incorporate users' goals into viewing. For example, if the viewer's goal is to search largest values, s/he can...
The guided search model makes testable predictions. One such prediction is that triple conjunctions (Quinlan & Hum-phreys, 1987) should be easier to find than standard conjunc-tions. If the parallel processes can guide subsequent serial search, then three parallel sources of guidance ought to be better than two.
