Visual Search and Attention

Verghese, Preeti

doi:10.1016/s0896-6273(01)00392-0

Cited by 296 publications

(98 citation statements)

References 51 publications

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“…Similarly, Signal Detection Theory models of visual search posit a parallel, unlimited-capacity first stage followed by a simple decision rule (J. Palmer, 1998;Verghese, 2001). Despite their differences, these models agree on the basic processes of visual search: visual information is input; once an item reaches a criterion threshold, the item is selected; the selected item is identified and matched to target specifications; and a response is selected and executed.…”

Section: Discussionmentioning

confidence: 99%

The role of presentation method and depth singletons in visual search for objects moving in depth

2012

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Are objects moving in depth searched for efficiently? Previous studies have reported conflicting results, with some finding efficient search for only approaching motion (Franconeri & Simons, 2003), and others reporting that both approaching and receding motion are found more efficiently than static targets (Skarratt, Cole, & Gellatly, 2009). This may be due to presentation protocol differences and a confounding variable. We systematically tested the effect of the motion-in-depth presentation method and the effect of a confounding unique depth singleton on search performance. Simulating motion in depth using size scaling, changing binocular disparity, or a calibrated combination of these two depth cues, we found that search performance was affected by presentation method and that a combination of size scaling and changing disparity gives rise to the most compelling motion-in-depth perception. Exploiting this finding in Experiment 2, we found that removing the depth singleton does not affect motion-in-depth search performance. Overall, we found that search is more efficient for targets moving in depth than static targets. Approaching and receding motion had an equal advantage over static targets in target selection, shown through shallower search slopes. However, approaching motion had lower intercepts, consistent with an advantage over receding motion in later stages of processing associated with target identification and response.

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Section: Discussionmentioning

confidence: 99%

The role of presentation method and depth singletons in visual search for objects moving in depth

2012

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“…1 Different curves are produced by different levels of discriminability between target and distractors. For searches in which the target can be identified by a single perceptual attribute (e.g., orientation or size), a MAX rule captures the data quite well (Davis et al, 2006; Eckstein et al, 2000; Palmer et al, 1993; Verghese, 2001). Furthermore, Green and Swets (1966) demonstrated that the MAX rule yields the highest probability of a correct response, which may explain why it is the most widely used decision rule in one-stage noise-limited modeling of visual search.…”

Section: The Crossover Predictionmentioning

confidence: 99%

“…In contrast, unlimited-capacity models do away with the attentional bottleneck, and instead conceptualize visual search as detection of a signal among multiple noise sources in a single stage (e.g., Carrasco & McElree, 2001; Davis et al, 2006; Dosher, Han, & Lu, 2004; Eckstein, Thomas, Palmer, & Shimozaki, 2000; Kinchla, 1974; Palmer, 1995; Palmer & McLean, 1995; Shaw, 1980; Verghese, 2001). The observer compares a signal, derived from processing of the entire display, to a criterion.…”

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confidence: 99%

Signal detection evidence for limited capacity in visual search

Palmer

Fencsik

Flusberg

et al. 2011

Atten Percept Psychophys

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The nature of capacity limits (if any) in visual search has been a topic of controversy for decades. In 30 years of work, researchers have attempted to distinguish between two broad classes of visual search models. Attention-limited models have proposed two stages of perceptual processing: an unlimited-capacity preattentive stage, and a limited-capacity selective attention stage. Conversely, noise-limited models have proposed a single, unlimited-capacity perceptual processing stage, with decision processes influenced only by stochastic noise. Here, we use signal detection methods to test a strong prediction of attention-limited models. In standard attention-limited models, performance of some searches (feature searches) should only be limited by a preattentive stage. Other search tasks (e.g., spatial configuration search for a “2” among “5”s) should be additionally limited by an attentional bottleneck. We equated average accuracies for a feature and a spatial configuration search over set sizes of 1–8 for briefly presented stimuli. The strong prediction of attention-limited models is that, given overall equivalence in performance, accuracy should be better on the spatial configuration search than on the feature search for set size 1, and worse for set size 8. We confirm this crossover interaction and show that it is problematic for at least one class of one-stage decision models.

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“…Consequently, the set-size effect can also be explained by the decrease of spatial resolution with eccentricity (Carrasco et al, 1995; Carrasco et al, 1998a; Carrasco & Frieder, 1997; Palmer et al, 1993; Verghese & Nakayama, 1994). Finally, many studies have shown that signal-detection theory can explain visual-search performance in a variety of detection, discrimination, and localization tasks in feature and conjunction searches (e.g., Cameron, Tai, Eckstein, & Carrasco, 2004; Dosher et al, 2010; Eckstein, 1998; Eckstein, Thomas, Palmer, & Shimozaki, 2000; Palmer et al, 1993; Verghese, 2001). …”

Section: Introductionmentioning

confidence: 99%

Distinct perceptual rhythms for feature and conjunction searches

Dugué

Xue²,

Carrasco

2017

Journal of Vision

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Feature and conjunction searches are widely used to study attentional deployment. However, the spatiotemporal behavior of attention integration in these tasks remains under debate. Are multiple search stimuli processed in parallel or sequentially? Does sampling of visual information and attentional deployment differ between these two types of search? If so, how? We used an innovative methodology to estimate the distribution of attention on a single-trial basis for feature and conjunction searches. Observers performed feature- and conjunction-search tasks. They had to detect and discriminate a tilted low-spatial-frequency grating among three low-spatial-frequency vertical gratings (feature search) or low-spatial-frequency vertical gratings and high-spatial-frequency tilted gratings (conjunction search). After a variable delay, two probes were flashed at random locations. Performance in reporting the probes was used to infer attentional deployment to those locations. By solving a second-degree equation, we determined the probability of probe report at the most (P1) and least (P2) attended locations on a given trial. Were P1 and P2 equal, we would conclude that attention had been uniformly distributed across all four locations. Otherwise, we would conclude that visual information sampling and attentional deployment had been nonuniformly distributed. Our results show that processing was nonuniformly distributed across the four locations in both searches, and was modulated periodically over time at ∼5 Hz for the conjunction search and ∼12 Hz for the feature search. We argue that the former corresponds to the periodicity of attentional deployment during the search, whereas the latter corresponds to ongoing sampling of visual information. Because different locations were not simultaneously processed, this study rules out a strict parallel model for both search types.

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Visual Search and Attention

Cited by 296 publications

References 51 publications

The role of presentation method and depth singletons in visual search for objects moving in depth

The role of presentation method and depth singletons in visual search for objects moving in depth

Signal detection evidence for limited capacity in visual search

Distinct perceptual rhythms for feature and conjunction searches

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