Historically, visual search models were mainly evaluated based on their account of mean reaction times (RTs) and accuracy data. More recently, Wolfe, Palmer, and Horowitz (2010) have demonstrated that the shape of the entire RT distributions imposes important constraints on visual search theories and can falsify even successful models such as guided search, raising a challenge to computational theories of search. Competitive guided search is a novel model that meets this important challenge.

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... model is an adaptation of guided search, featuring a series of item selection and identification iterations with guidance towards targets. The main novelty of the model is its termination rule: A quit unit, which aborts the search upon selection, competes with items for selection and is inhibited by the saliency map of the visual display. As the trial proceeds, the quit unit both increases in strength and suffers less saliency-based inhibition and hence the conditional probability of quitting the trial accelerates. The model is fitted to data the data from three classical search task that have been traditionally considered to be governed by qualitatively different mechanisms, including a spatial configuration, a conjunction, and a feature search . The model is mathematically tractable and it accounts for the properties of RT distributions and for error rates in all three search tasks, providing a unifying theoretical framework for visual search. Guided search (Wolfe et al., 1989; Wolfe, 1994 Wolfe, , 2007 is one of the most popular/prominent models of visual search. It assumes a two-stage processing architecture, where in a first stage the display is processed in parallel (see also Hoffman, 1979) . This parallel processing stage results in a salience value for each location in the display. Salience here is understood as a measure of local conspicuity: how distinct a visual item is, compared to all other items, with respect to color,