Categorical influences on chromatic search asymmetries

Wright, Oliver

doi:10.1080/13506285.2012.715600

Cited by 5 publications

(16 citation statements)

References 46 publications

(121 reference statements)

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“…Figure 9). Hence, our results with equally discriminable color confirmed those observed with colors whose differences were not controlled in a meaningful way (Hanley & Roberson, 2011;O. Wright, 2012).…”

Section: Discussionsupporting

confidence: 87%

“…Following the psychophysical terminology, we may call the color that constitutes the point of reference, the test color, and the color that is compared to this reference the comparison color (Krauskopf & Gegenfurtner, 1992;Witzel & Gegenfurtner, 2013). Then, those previous studies (Hanley & Roberson, 2011;O. Wright, 2012) and our own studies provide basically the same result on asymmetries: Categorical patterns only occur when the comparison color of within pairs is unambiguously within the category, and not close to the boundary, where category membership is fuzzy (e.g., Figure 8 in Olkkonen et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

“…Asymmetries in the target-distractor relationship. Previous studies (Hanley & Roberson, 2011;O. Wright, 2012) reported asymmetries in performance depending on whether one or the other color in a within-category pair was the target.…”

Section: Afc Control Namingmentioning

confidence: 93%

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Categorical perception for red and brown.

Witzel

Gegenfurtner

2016

Journal of Experimental Psychology: Human Perception and Perfor

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Recent studies suggest that the widely accepted evidence in support of categorical perception of color may be a confound of effects due to low-level sensory mechanisms that are unrelated to color categories. To reveal genuine category effects, we investigated the category boundary least prone to spurious effects of low-level mechanisms: the boundary between red and brown. We tested for low-level sensory and high-level cognitive effects of categories on color discrimination, while carefully controlling potential factors of color vision that are not related to color categories. First, we established the red-brown boundary through a naming task and measured just-noticeable differences (JNDs) for colors across the boundary. If low-level sensitivity to color differences was categorical, JNDs should decrease toward the boundary. However, this was not the case. Second, we measured performance in terms of response times and error rates in a speeded discrimination task with color pairs that were equalized in discriminability based on the empirical JNDs. There was a boost of performance (lower response times and error rates) for identifying color differences in equally discriminable color pairs, when the colors crossed the boundary. Given the particularity of the red-brown boundary, these results prove that the observed effects were due to color categories rather than low-level visual mechanisms. These findings support the idea that category effects are due to a shift of attention to the linguistic distinction between categories, rather than being a pure product of perception. These category effects do not depend on the hemispheric lateralization of language. (PsycINFO Database Record

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

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Categorical perception for red and brown.

Witzel

Gegenfurtner

2016

Journal of Experimental Psychology: Human Perception and Perfor

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“…That is, there might be a representation in the network that could be used to steer attention away from books and tires, but would not exclude horses or, perhaps, bushes or piles of old clothes. This is speculative, but it is appealing to think that shape guidance might be a rough abstraction from the relatively early stages of the processes that perform object recognition, just as color guidance appears to be a relatively crude abstraction from the processes that allow you to assess the precise hue, saturation, and value of an attended color patch (Nagy & Cone, 1993;Wright, 2012).…”

Section: What Are the Basic Features That Guide Visual Search?mentioning

confidence: 99%

Guided Search 6.0: An updated model of visual search

2021

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This paper describes Guided Search 6.0 (GS6), a revised model of visual search. When we encounter a scene, we can see something everywhere. However, we cannot recognize more than a few items at a time. Attention is used to select items so that their features can be "bound" into recognizable objects. Attention is "guided" so that items can be processed in an intelligent order. In GS6, this guidance comes from five sources of preattentive information: (1) top-down and (2) bottom-up feature guidance, (3) prior history (e.g., priming), (4) reward, and (5) scene syntax and semantics. These sources are combined into a spatial "priority map," a dynamic attentional landscape that evolves over the course of search. Selective attention is guided to the most active location in the priority map approximately 20 times per second. Guidance will not be uniform across the visual field. It will favor items near the point of fixation. Three types of functional visual field (FVFs) describe the nature of these foveal biases. There is a resolution FVF, an FVF governing exploratory eye movements, and an FVF governing covert deployments of attention. To be identified as targets or rejected as distractors, items must be compared to target templates held in memory. The binding and recognition of an attended object is modeled as a diffusion process taking > 150 ms/item. Since selection occurs more frequently than that, it follows that multiple items are undergoing recognition at the same time, though asynchronously, making GS6 a hybrid of serial and parallel processes. In GS6, if a target is not found, search terminates when an accumulating quitting signal reaches a threshold. Setting of that threshold is adaptive, allowing feedback about performance to shape subsequent searches. Simulation shows that the combination of asynchronous diffusion and a quitting signal can produce the basic patterns of response time and error data from a range of search experiments.

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“…But it might be the case that language influenceslowerlevelcognitiveprocesses,suchasperception. Some studies have found evidence of Whorfian influences on visual search tasks (e.g., Drivonikou et al, 2007;Gilbert et al, 2006;Roberson, Pak, & Hanley, 2008;Wright 2012). Since search tasks tap perceptual processes more directly than tasks like XAB, evidence of Whorfian effects in their performance implies that language can influence lower level cognitive processes.…”

Section: Discussionmentioning

confidence: 99%

Whorfian effects on colour memory are not reliable

Wright

Davies

Franklin

2015

Quarterly Journal of Experimental Psychology

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The Whorfian hypothesis suggests that differences between languages cause differences in cognitive processes. Support for this idea comes from studies that find that patterns of colour memory errors made by speakers of different languages align with differences in colour lexicons. The current study provides a large-scale investigation of the relationship between colour language and colour memory, adopting a cross-linguistic and developmental approach. Colour memory on a delayed matching-to-sample (XAB) task was investigated in 2 language groups with differing colour lexicons, for 3 developmental stages and 2 regions of colour space. Analyses used a Bayesian technique to provide simultaneous assessment of two competing hypotheses (H1-Whorfian effect present, H0-Whorfian effect absent). Results of the analyses consistently favoured H0. The findings suggest that Whorfian effects on colour memory are not reliable and that the importance of such effects should not be overestimated.

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Categorical influences on chromatic search asymmetries

Cited by 5 publications

References 46 publications

Categorical perception for red and brown.

Categorical perception for red and brown.

Guided Search 6.0: An updated model of visual search

Whorfian effects on colour memory are not reliable

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