The cost of search for multiple targets: Effects of practice and target similarity.

Menneer, Tamaryn; Cave, Kyle R.; Donnelly, Nick

doi:10.1037/a0015331

Cited by 90 publications

(173 citation statements)

References 34 publications

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“…did participants perform the dual-target search first, second or third, Figure 10 targets respectively). However, previous work has shown that the dual-target cost remains even after extensive practice (Menneer et al, 2009;Menneer et al, 2012). In the current experiments, perhaps the shedding of non-preferred unfamiliar face target is ameliorated, though not removed, by dedicated prior practice with the same faces appearing as single targets before performing the dual-target search.…”

Section: Dual-target Absent Ratiomentioning

confidence: 70%

“…The dual-target cost is found for simple as well as more complex stimuli and persists even after practice (Menneer, Cave, & Donnelly, 2009). In some circumstances, such as when searching for colour targets, overall speed decreases and accuracy falls when searching for two targets compared with single-target search, but both targets can be found with accuracy levels above chance.…”

Section: Dual-target Cost In Visual Search For Multiple Unfamiliar Facesmentioning

confidence: 99%

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Dual-target cost in visual search for multiple unfamiliar faces.

Mestry

Menneer

Cave

et al. 2017

Journal of Experimental Psychology: Human Perception and Perfor

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The efficiency of visual search for one (single-target) and either of two (dual-target) unfamiliar faces was explored to understand the manifestations of capacity and guidance limitations in face search. The visual similarity of distractor faces to target faces was manipulated using morphing (Experiments 1 and 2) and multidimensional scaling (Experiment 3). A dual-target cost was found in all experiments, evidenced by slower and less accurate search in dual- than single-target conditions. The dual-target cost was unequal across the targets, with performance being maintained on one target and reduced on the other, which we label “preferred” and “non-preferred” respectively. We calculated the capacity for each target face and show reduced capacity for representing the non-preferred target face. However, results show that the capacity for the non-preferred target can be increased when the dual-target condition is conducted after participants complete the single-target conditions. Analyses of eye movements revealed evidence for weak guidance of fixations in single-target search, and when searching for the preferred target in dual-target search. Overall, the experiments show dual-target search for faces is capacity- and guidance-limited, leading to superior search for 1 face over the other in dual-target search. However, learning faces individually may improve capacity with the second face.

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Section: Dual-target Absent Ratiomentioning

confidence: 70%

Section: Dual-target Cost In Visual Search For Multiple Unfamiliar Facesmentioning

confidence: 99%

Dual-target cost in visual search for multiple unfamiliar faces.

Mestry

Menneer

Cave

et al. 2017

Journal of Experimental Psychology: Human Perception and Perfor

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“…Modelling of these behavioural results suggested that exactly one attentional template can be active at any given time (see also Meneer, Cave, &Donnelly, 2009, andStroud et al, 2011, for additional behavioural evidence, and , for ERP evidence that attentional target selectivity is less effective during multiple-colour as compared to single-colour visual search). If attentional templates are working memory representations, and if working memory can hold several objects simultaneously, why should only a single search template be active at a time?…”

Section: Preparatory Attentional Templates and Visual Working Memorymentioning

confidence: 99%

EPS Mid-Career Award 2014

Eimer

2015

Quarterly Journal of Experimental Psychology

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In visual search, observers try to find known target objects among distractors in visual scenes where the location of the targets is uncertain. This review article discusses the attentional processes that are active during search and their neural basis. Four successive phases of visual search are described. During the initial preparatory phase, a representation of the current search goal is activated. Once visual input has arrived, information about the presence of target-matching features is accumulated in parallel across the visual field (guidance). This information is then used to allocate spatial attention to particular objects (selection), before representations of selected objects are activated in visual working memory (recognition). These four phases of attentional control in visual search are characterized both at the cognitive level and at the neural implementation level. It will become clear that search is a continuous process that unfolds in real time. Selective attention in visual search is described as the gradual emergence of spatially specific and temporally sustained biases for representations of task-relevant visual objects in cortical maps.3

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“…Given that such switch costs necessary to search through visual working memory require time (taking up to 300 -500 ms, Garavan, 1998, see also Oberauer, 2002 and there is a dual task cost of searching through multiple targets (e.g. Godwin et al, 2010, Menneer et al, 2007Menneer et al, 2009;Menneer et al 2010) people may instead chose a heuristic to more willingly trust CAD rather than search through the many possible target representations in Visual Working Memory. This led to good search when the CAD cue was correct, but inaccurate search when the CAD cue was wrong.…”

Section: Visual Working Memory and Cadmentioning

confidence: 99%

“…However, searching for multiple targets simultaneously has been shown to lead to dualtarget costs where response times are slower and accuracy falls (e.g. , Menneer, Barrett, Phillips, Donnelly & Cave, 2007Menneer, Cave, & Donnelly, 2009;. Mestry et al (2016) suggested that the reason for this decline in accuracy is that when searching for two possible targets, one of the targets becomes classified as the non-preferred target and is 'shed' (i.e., participants give up searching for the non-preferred target in favour of the preferred one).…”

Section: Introductionmentioning

confidence: 99%

Low prevalence search for cancers in mammograms: Evidence using laboratory experiments and computer aided detection.

Kunar

Watson

Taylor‐Phillips

et al. 2017

Journal of Experimental Psychology: Applied

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People miss a large proportion of targets when they only appear rarely. This Low Prevalence (LP) Effect could lead to serious consequences if it occurred in the real world task of searching for cancers in mammograms. Using a novel mammogram search task, we asked participants to search for a pre-specified cancer (Experiments 1-2) or a range of masses (Experiments 3-5) under high or low prevalence conditions. Experiment 1 showed that an LP Effect occurred using these stimuli. Experiment 2 tested an over-reliance hypothesis and showed that the use of Computer Aided Detection (CAD) led to fewer missed cancers with a valid CAD prompt yet, a large proportion of cancers were missed when CAD was incorrect. Experiment 3 -5showed that false alarms also increased when searching for a range of masses and that CAD reduced miss errors when it correctly cued the target but increased miss errors and false alarms when it did not. Furthermore, when a mass fell outside the CAD prompt it was more likely to be misidentified. No LP Effect was observed with the addition of CAD when people were asked to search for a range of targets. Theories and implications for mammogram search are discussed. 3 Public Significance StatementThis study suggests that search for low prevalence cancers in mammograms can be aided by Computer Aided Detection (CAD) but there is also a cost. CAD highlights areas in mammograms that are likely to contain a mass. When CAD successfully prompts a mass, observers are better at detecting it. However, observers also show an over reliance on CAD, and are more likely to miss cancers if the cue is incorrect.4

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The cost of search for multiple targets: Effects of practice and target similarity.

Cited by 90 publications

References 34 publications

Dual-target cost in visual search for multiple unfamiliar faces.

Dual-target cost in visual search for multiple unfamiliar faces.

EPS Mid-Career Award 2014

Low prevalence search for cancers in mammograms: Evidence using laboratory experiments and computer aided detection.

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