The Effects of Target Typicality on Guidance and Verification in Categorical Search

Maxfield, Justin; Stadler, Westri; Zelinsky, Gregory J.

doi:10.1167/13.9.524

Cited by 3 publications

(4 citation statements)

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“…One explanation might be attributable to a limitation on visual working memory: If only a finite number of feature values can be encoded into the target template (e.g., Olivers, Peters, Houtkamp, & Roelfsema, 2011), and hue information is more useful than other feature dimensions, orientation and shape may be selectively excluded from this limited set of guiding features. Another possibility is that precise visual details from a target preview may be relied upon less commonly than what has been believed; this possibility was suggested by recent work using a categorical search task (Maxfield, Stadler, & Zelinsky, 2013; Schmidt & Zelinsky, 2009; Yang & Zelinsky, 2009; Yu, Maxfield, & Zelinsky, 2016). If participants construct a categorical representation of the target to guide their search, when previewed with a red sports car they might search for any red sports car rather than the specific car shown at preview.…”

Section: Discussionmentioning

confidence: 99%

Specifying the precision of guiding features for visual search.

Alexander¹,

Nahvi²,

Zelinsky³

2019

Journal of Experimental Psychology: Human Perception and Perfor

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Visual search is the task of finding things with uncertain locations. Despite decades of research, the features that guide visual search remain poorly specified, especially in realistic contexts. This study tested the role of two features—shape and orientation—both in the presence and absence of hue information. We conducted five experiments to describe preview–target mismatch effects, decreases in performance caused by differences between the image of the target as it appears in the preview and as it appears in the actual search display. These mismatch effects provide direct measures of feature importance, with larger performance decrements expected for more important features. Contrary to previous conclusions, our data suggest that shape and orientation only guide visual search when color is not available. By varying the probability of mismatch in each feature dimension, we also show that these patterns of feature guidance do not change with the probability that the previewed feature will be invalid. We conclude that the target representations used to guide visual search are much less precise than previously believed, with participants encoding and using color and little else.

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Section: Discussionmentioning

confidence: 99%

Specifying the precision of guiding features for visual search.

Alexander¹,

Nahvi²,

Zelinsky³

2019

Journal of Experimental Psychology: Human Perception and Perfor

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“…All images were closely cropped using a rectangular marquee to depict only the object and a minimal amount of background. Because object typicality can affect categorization and search (Murphy & Brownell, 1985, Maxfield et al, 2014), targets were selected to be typical members of their category at the subordinate, basic, and superordinate levels. We did this by having 45 participants complete a preliminary norming task in which 240 images (5 exemplars from each of 48 subordinate categories) were rated for both typicality and image agreement (Snodgrass & Vanderwart, 1980) at each hierarchical level using a 1 (high typicality/image agreement) to 7 (low typicality/image agreement) scale.…”

Section: Behavioral Methodsmentioning

confidence: 99%

“…We evaluate the visual representation of common object categories using a categorical search task (Maxfield, Stadler, & Zelinsky, 2014; Schmidt & Zelinsky, 2009; Zelinsky, Adeli, Peng, Samaras, 2013; Zelinsky, Peng, Berg, & Samaras, 2013; Zelinsky, Peng, & Samaras, 2013). Categorical search differs from standard visual search in that targets are designated by category (e.g., the word “dog”) instead of by a picture pre-cue (e.g., an image of a specific dog), a situation that rarely exists outside the laboratory.…”

Section: Introductionmentioning

confidence: 99%

Searching for Category-Consistent Features

Yu¹,

Maxfield

Zelinsky

2016

Psychol Sci

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A generative model of category representation is introduced that uses computer vision methods to extract category-consistent features (CCFs) directly from images of category exemplars. The model was trained on 4800 images of common objects, and CCFs were obtained for 68 categories spanning subordinate, basic, and superordinate levels in a category hierarchy. Participants searched for these same categories. Targets cued at the subordinate level were preferentially fixated, but fixated targets were verified faster following a basic-level cue. The subordinate-level advantage in guidance is explained by the number of target category CCFs, a measure of category specificity that decreases with movement up the category hierarchy. The basic-level advantage in verification is explained by multiplying CCF number by sibling distance, a measure of category distinctiveness. With this model the visual representations of real-world object categories, each learned from the vast numbers of image exemplars accumulated throughout our everyday experience, can finally be studied.

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“…WM is necessary for all visual detection task, where a predefined target object is kept online, to be detected at a later stage in a search display that may or may not be presented among other (distractor) objects (e.g., Olivers, Meijer, & Theeuwes, 2006; Soto, Heinke, Humphreys, & Blanco, 2005). The detection can be less efficient when the target and distractor objects share one or more features (Cowan, 1988; Duncan & Humphreys, 1989; Maxfield, Stadler, & Zelinsky, 2013). In any visual search, a template of the cue or target object is activated, and the search is based on the match between the cue template and the actual target.…”

mentioning

confidence: 99%

Cross-modal information transfer and the effect of concurrent task-load.

Balani¹

2020

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Our everyday lives offer plenty of situations where complex processing of information takes place, in which information needs to transfer across modalities to achieve a behavioral goal. The study examined the differential effects on object detection by a visual, verbal or an auditory cue held in Working Memory (WM), and the role of concurrent cognitive task-load on the final detection of that cue. Three experiments, all using same stimuli set but in different modalities, subjects held in memory a representation of a novel cue for a speeded detection in a search display at the end of each trial. The cue stimulus could be an image (visual), the name (verbal) or the sound (auditory) of a common animal or object. A mental arithmetic task was interleaved between the cue presentation and the cue detection. The results showed that information held in WM, either in verbal or auditory form, can efficiently transfer across modalities to complete a visual detection task for a representation of the initial WM-cue. The speed of detection was not affected by the cross-modal transfer of cue information but there was some detrimental effect on detection that could distinctively be attributed to the cognitive task-load. Together, these findings may provide some evidence for the role of Episodic Buffer component of WM (Baddeley, 2000) in integrating multimodal information originated from different sources, hence supporting the notion of the supramodal nature of WM. The results have been discussed in light of Baddeley's (1974, 2000) and Cowan's (1988, 1999) theoretical WM frameworks.

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The Effects of Target Typicality on Guidance and Verification in Categorical Search

Cited by 3 publications

References 0 publications

Specifying the precision of guiding features for visual search.

Specifying the precision of guiding features for visual search.

Searching for Category-Consistent Features

Cross-modal information transfer and the effect of concurrent task-load.

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