The centroid paradigm: Quantifying feature-based attention in terms of attention filters

Atten Percept Psychophys

et al. 2015

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The finding that an item of type A pops out from an array of distractors of type B typically is taken to support the inference that human vision contains a neural mechanism that is activated by items of type A but not by items of type B. Such a mechanism might be expected to yield a neural image in which items of type A produce high activation and items of type B low (or zero) activation. Access to such a neural image might further be expected to enable accurate estimation of the centroid of an ensemble of items of type A intermixed with to-be-ignored items of type B. Here, it is shown that as the number of items in stimulus displays is increased, performance in estimating the centroids of horizontal (vertical) items amid vertical (horizontal) distractors degrades much more quickly and dramatically than does performance in estimating the centroids of white (black) items among black (white) distractors. Together with previous findings, these results suggest that, although human vision does possess bottom-up neural mechanisms sensitive to abrupt local changes in bar-orientation, and although human vision does possess and utilize top-down global attention filters capable of selecting multiple items of one brightness or of one color from among others, it cannot use a top-down global attention filter capable of selecting multiple bars of a given absolute orientation and filtering bars of the opposite orientation in a centroid task.

“…A formal justification of this modeling method and a description of the methods used to estimate confidence intervals for model parameters is provided in Sun et al (2015).…”

Section: Estimating Model Parametersmentioning

confidence: 99%

Section: Design and Proceduresmentioning

confidence: 99%

Evidence against global attention filters selective for absolute bar-orientation in human vision

Inverso

Atten Percept Psychophys

et al. 2015

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“…Typically, a centroid judgment is assumed to be a statistical summary representation (SSR), that is, a statistic that accurately describes a property of a group of items even when there are so many items that the subject has accurate information only about few, if any, of the individual items (17). For example, in judging the centroid of 16 items, subjects achieve efficiencies of about 0.8 (6), which means that an ideal detector would have to know the precise location of 0.8 × 16 = 12.8 items to match the subjects' performances. This is three or four times the number of items that subjects can identify the locations of.…”

Section: Different Processing Levels: Attention To Color Precedes Attmentioning

confidence: 99%

“…Sun et al (6) introduced five quantitative measures to evaluate attention filters, two of which are used here. The "selectivity ratio" is defined as the filter weight of the target color divided by the mean weight of all of the distractor colors.…”

Section: Exp 1: Attention Filters For Four Sets Of Eight Single Colorsmentioning

confidence: 99%

Human attention filters for single colors

Proc. Natl. Acad. Sci. U.S.A.

Wright

et al. 2016

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The visual images in the eyes contain much more information than the brain can process. An important selection mechanism is featurebased attention (FBA). FBA is best described by attention filters that specify precisely the extent to which items containing attended features are selectively processed and the extent to which items that do not contain the attended features are attenuated. The centroid-judgment paradigm enables quick, precise measurements of such human perceptual attention filters, analogous to transmission measurements of photographic color filters. Subjects use a mouse to locate the centroid-the center of gravity-of a briefly displayed cloud of dots and receive precise feedback. A subset of dots is distinguished by some characteristic, such as a different color, and subjects judge the centroid of only the distinguished subset (e.g., dots of a particular color). The analysis efficiently determines the precise weight in the judged centroid of dots of every color in the display (i.e., the attention filter for the particular attended color in that context). We report 32 attention filters for single colors. Attention filters that discriminate one saturated hue from among seven other equiluminant distractor hues are extraordinarily selective, achieving attended/unattended weight ratios >20:1. Attention filters for selecting a color that differs in saturation or lightness from distractors are much less selective than attention filters for hue (given equal discriminability of the colors), and their filter selectivities are proportional to the discriminability distance of neighboring colors, whereas in the same range hue attentionfilter selectivity is virtually independent of discriminabilty.T he visual world on our planet is incredibly complex. The main mechanism for selecting a subset of the environmental visual information to process is orienting our head and body and pointing our eyes toward the most significant locations. Even then, each retinal image contains enormously more information than the brain can process. To further constrain the flow of information, perceptual attention processes select locations in space, intervals in time, objects that contain particular features, and ultimately particular complex objects for further processing. Here, we focus on attention to a particular color, an instance of feature-based attention (FBA). It is well established that FBA operates broadly across space, heightening sensitivity to the attended feature even at locations that are irrelevant to the task at hand (1-4). We exploit this global property of FBA in a centroid paradigm to derive human attention filters for color. Just as the physical description of a color filter describes the relative transmission of the filter for each wavelength of light, a color-attention filter describes the relative effectiveness with which each color in the retinal input ultimately influences performance. Using the Centroid Paradigm to Derive Attention FiltersIn a centroid task, a subject views a briefly presented stimulus con...

“…The centroid judgment paradigm adopted in this study was originally developed by Drew et al (26) to study feature-based attention, and was considerably enhanced by Sun et al (27,28). On all trials, subjects viewed a 300-ms flash of a display of 26 dots, 12 dots of one randomly chosen color (homogeneous set), plus 14 dots consisting of 2 each of 7 remaining colors (heterogeneous set; see Fig.…”

Section: Methodsmentioning

confidence: 99%

High-capacity preconscious processing in concurrent groupings of colored dots

Proc. Natl. Acad. Sci. U.S.A.

Wright

et al. 2018

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Grouping is a perceptual process in which a subset of stimulus components (a group) is selected for a subsequent—typically implicit—perceptual computation. Grouping is a critical precursor to segmenting objects from the background and ultimately to object recognition. Here, we study grouping by color. We present subjects with 300-ms exposures of 12 dots colored with the same but unknown identical color interspersed among 14 dots of seven different colors. To indicate grouping, subjects point-click the remembered centroid (“center of gravity”) of the set of homogeneous dots, of heterogeneous dots, or of all dots. Subjects accurately judge all of these centroids. Furthermore, after a single stimulus exposure, subjects can judge both the heterogeneous and homogeneous centroids, that is, subjects simultaneously group by similarity and by dissimilarity. The centroid paradigm reveals the relative weight of each dot among targets and distractors to the underlying grouping process, offering a more detailed, quantitative description of grouping than was previously possible. A change detection experiment reveals that conscious memory contains less than two dots and their locations, whereas an ideal detector would have to perfectly process at least 15 of 26 dots to match the subjects’ centroid judgments—indicating an extraordinary capacity for preconscious grouping. A different color set yielded identical results. Grouping theories that rely on predefined feature maps would fail to explain these results. Rather, the results indicate that preconscious grouping is automatic, flexible, and rapid, and a far more complex process than previously believed.