Why rare targets are slow: Evidence that the target probability effect has an attentional locus

Hon, Nicholas; Tan, Chia-Howe

doi:10.3758/s13414-013-0434-0

Cited by 19 publications

(23 citation statements)

References 29 publications

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“…Second, by tracking eye movements, we discovered that the RT benefit for high-prevalence targets was a result of more efficient scanning and faster decision making. Simply put, relative to low-prevalence targets, high-prevalence items were both found and appreciated more quickly (see Hon & Tan, 2013). And, finally, in Experiment 4a, even when rare targets were directly fixated, they were still more likely to be missed relative to common targets (for similar results in different paradigms, see Godwin, Menneer, Riggs, Cave, & Donnelly, 2015; Solman, Cheyne, & Smilek, 2012).…”

Section: Discussionmentioning

confidence: 99%

Failures of perception in the low-prevalence effect: Evidence from active and passive visual search.

Hout¹,

Walenchok²,

Goldinger³

et al. 2015

Journal of Experimental Psychology: Human Perception and Perfor

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In visual search, rare targets are missed disproportionately often. This low-prevalence effect (LPE) is a robust problem with demonstrable societal consequences. What is the source of the LPE? Is it a perceptual bias against rare targets or a later process, such as premature search termination or motor response errors? In 4 experiments, we examined the LPE using standard visual search (with eye tracking) and 2 variants of rapid serial visual presentation (RSVP) in which observers made present/absent decisions after sequences ended. In all experiments, observers looked for 2 target categories (teddy bear and butterfly) simultaneously. To minimize simple motor errors, caused by repetitive absent responses, we held overall target prevalence at 50%, with 1 low-prevalence and 1 high-prevalence target type. Across conditions, observers either searched for targets among other real-world objects or searched for specific bears or butterflies among within-category distractors. We report 4 main results: (a) In standard search, high-prevalence targets were found more quickly and accurately than low-prevalence targets. (b) The LPE persisted in RSVP search, even though observers never terminated search on their own. (c) Eye-tracking analyses showed that high-prevalence targets elicited better attentional guidance and faster perceptual decisions. And (d) even when observers looked directly at low-prevalence targets, they often (12%–34% of trials) failed to detect them. These results strongly argue that low-prevalence misses represent failures of perception when early search termination or motor errors are controlled.

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Section: Discussionmentioning

confidence: 99%

Failures of perception in the low-prevalence effect: Evidence from active and passive visual search.

Hout¹,

Walenchok²,

Goldinger³

et al. 2015

Journal of Experimental Psychology: Human Perception and Perfor

View full text Add to dashboard Cite

show abstract

“…The occurrence rate of target features is an important determiner of performance in visual search. In target present-absent tasks, participants frequently miss the target if most trials are target-absent trials (Wolfe, Horowitz, Van Wert et al, 2007;Wolfe, Horowitz, & Kenner, 2005), and they are less accurate in detecting a rarely present target relative to a more frequently present target (Godwin, Menneer, Cave et al, 2010;Godwin, Menneer, Riggs, Cave, & Donnelly, 2015;Hon & Tan, 2013;Hout, Walenchok, Goldinger, & Wolfe, 2015). More recent work using target identification tasks has extended the target frequency effect to situations in which one of two targets is always present.…”

Section: Introductionmentioning

confidence: 99%

Rapid and selective updating of the target template in visual search

2017

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Frequent target stimuli are detected more rapidly than infrequent ones. Here, we examined whether the frequency effect reflected durable attentional biases toward frequent target features, and whether the effect was confined to featural properties that defined the target. Participants searched for two specific target colors among distractors of heterogeneous colors and reported the line orientation of the target. The target was more often in one specific feature (e.g., a specific color or a specific orientation) than another in a training phase. This frequency difference was removed or reversed in a testing phase. Experiments 1 and 2 showed that when frequency differences were introduced to the target's defining feature, participants more rapidly found the high-frequency target than the low-frequency target. However, changes in attention were not durable-the search advantage vanished immediately when the frequency differences were removed. Experiments 3-5 showed that only featural properties that defined the target facilitated search of the more frequent feature. Features that did not define the target, such as the target feature that participants reported, sped up response but did not facilitate search. These data showed that when searching for multiple targets in a feature search task, people selectively and rapidly adapt to the frequency in the target's defining feature.

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“…In recent years, exciting new research has uncovered incidental visual statistical learning (VSL) and monetary reward as additional drivers of selective attention (Anderson, 2013; Chelazzi, Perlato, Santandrea, & Della Libera, 2013; Chun & Jiang, 1998; Hutchinson & Turk-Browne, 2012; Raymond & O’Brien, 2009; Turk-Browne, 2012). Features or locations frequently associated with a search target are prioritized, even when participants are unaware of the statistical regularities (Chun & Jiang, 1998, 1999; Hon & Tan, 2013). Similarly, features or locations associated with higher monetary reward are prioritized, often without explicit awareness (Anderson, Laurent, & Yantis, 2011; Chelazzi et al, 2014; Le Pelley, Pearson, Griffiths, & Beesley, 2014).…”

Section: Introductionmentioning

confidence: 99%

Modulation of spatial attention by goals, statistical learning, and monetary reward

Jiang

Sha

Remington

2015

Atten Percept Psychophys

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This study documented the relative strength of task goals, visual statistical learning, and monetary reward in guiding spatial attention. Using a difficult T-among-L search task, we cued spatial attention to one visual quadrant by (i) instructing people to prioritize it (goal-driven attention), (ii) placing the target frequently there (location probability learning), or (iii) associating that quadrant with greater monetary gain (reward-based attention). Results showed that successful goal-driven attention exerted the strongest influence on search RT. Incidental location probability learning yielded a smaller though still robust effect. Incidental reward learning produced negligible guidance for spatial attention. The 95 % confidence intervals of the three effects were largely nonoverlapping. To understand these results, we simulated the role of location repetition priming in probability cuing and reward learning. Repetition priming underestimated the strength of location probability cuing, suggesting that probability cuing involved long-term statistical learning of how to shift attention. Repetition priming provided a reasonable account for the negligible effect of reward on spatial attention. We propose a multiple-systems view of spatial attention that includes task goals, search habit, and priming as primary drivers of top-down attention.

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Why rare targets are slow: Evidence that the target probability effect has an attentional locus

Cited by 19 publications

References 29 publications

Failures of perception in the low-prevalence effect: Evidence from active and passive visual search.

Failures of perception in the low-prevalence effect: Evidence from active and passive visual search.

Rapid and selective updating of the target template in visual search

Modulation of spatial attention by goals, statistical learning, and monetary reward

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