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

Inverso, Matthew; Sun, Peng; Chubb, Charlie; Wright, Charles E.; Sperling, George

doi:10.1167/15.12.924

Cited by 3 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Very surprisingly, subjects were unable to find the centroid of four, six, or eight verticals among an equal number of distractors, although they succeeded easily with black versus white attentional selection with the same stimulus sets. The point here is not the point of Inverso et al (18), that there is something uniquely difficult about absolute vertical-versus-horizontal attentional selection, but rather that there is a different judgment process when the number of targets is less than four.…”

Section: Different Processing Levels: Attention To Color Precedes Attmentioning

confidence: 79%

“…Inverso et al (18) found that subjects are easily and accurately able to find the centroid of two vertical lines randomly placed among two horizontal distractors and, similarly, the centroid of two horizontal lines among two vertical distractors. Very surprisingly, subjects were unable to find the centroid of four, six, or eight verticals among an equal number of distractors, although they succeeded easily with black versus white attentional selection with the same stimulus sets.…”

Section: Different Processing Levels: Attention To Color Precedes Attmentioning

confidence: 99%

See 1 more Smart Citation

Human attention filters for single colors

Sun

Chubb

Wright

et al. 2016

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

Self Cite

View full text Add to dashboard Cite

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...

show abstract

Section: Different Processing Levels: Attention To Color Precedes Attmentioning

confidence: 79%

Section: Different Processing Levels: Attention To Color Precedes Attmentioning

confidence: 99%

Human attention filters for single colors

Sun

Chubb

Wright

et al. 2016

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ensemble representation of the overall feature distribution can be a potential basis for such categorization. It is important to note that spatial overlap in some cases interferes with individual subset processing, even if the subsets are perfectly segmentable 20 , 22 , 23 , 26 . This may indicate some additional difficulties with the suppression of irrelevant subsets and, hence, suggests the role of attention in multi-location selection.…”

Section: Discussionmentioning

confidence: 99%

“…Rather, the discrimination could be based only on more localized subset analysis which necessarily implies rapid categorical parsing: e.g., detecting that ‘categories’ of long-steep and short-flat lines in standard stimuli are replaced by ‘categories’ of long-flat and short-steep lines in deviant stimuli. The results of the previous behavioral study with similar stimulation 23 predict that such discrimination will occur only for textures consisting solely of highly distinct, segmentable features that can provide clear, non-confusable categories. Thus, the vMMN (if present) in our paradigm would most likely reflect an ability to automatically detect the change in the statistics of multiple intermixed objects parsed into separate categories.…”

Section: Introductionmentioning

confidence: 91%

“…The visual system can easily and rapidly parse a lot of interleaved objects into different color subsets and independently calculate ensemble summaries for each group separately 7 , 20 , 21 . Orientation can also serve as a cue for parsing into subsets, though not ideally 22 , 23 . To be capable of such independent computations, the visual system has to rapidly “decide” that some elements are similar enough to include them in the same pool to process as an ensemble, whereas others are substantially different to exclude them from that pool.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatially intermixed objects of different categories are parsed automatically

Khvostov

Lukashevich

Utochkin

2021

Sci Rep

View full text Add to dashboard Cite

Our visual system is able to separate spatially intermixed objects into different categorical groups (e.g., berries and leaves) using the shape of feature distribution: Determining whether all objects belong to one or several categories depends on whether the distribution has one or several peaks. Despite the apparent ease of rapid categorization, it is a very computationally demanding task, given severely limited “bottlenecks” of attention and working memory capable of processing only a few objects at a time. Here, we tested whether this rapid categorical parsing is automatic or requires attention. We used the visual mismatch negativity (vMMN) ERP component known as a marker of automatic sensory discrimination. 20 volunteers (16 female, mean age—22.7) participated in our study. Loading participants’ attention with a central task, we observed a substantial vMMN response to unattended background changes of categories defined by certain length-orientation conjunctions. Importantly, this occurred in conditions where the distributions of these features had several peaks and, hence, supported categorical separation. These results suggest that spatially intermixed objects are parsed into distinct categories automatically and give new insight into how the visual system can bypass the severe processing restrictions and form rich perceptual experience.

show abstract

FVS 2.0: A unifying framework for understanding the factors of visual-attentional processing.

Huang¹

2022

Psychological Review

View full text Add to dashboard Cite

Across a broad range of stimulus types and tasks (16 stimulus types × 26 tasks, 1744 observers in total), the present study employed an individual-item differences analysis to extract the factors of visual-attentional processing. Three orthogonal factors were identified and they can be summarized as an FVS 2.0 framework: featural, visual, and spatial strengths. Apart from one exception (low-level motion), the FVS 2.0 framework accounts for the vast majority (95.4%) of the variances in the 25 tasks. Therefore, the three straightforward factors provide a unifying framework for understanding the relationship between stimulus types as well as those between tasks. Combining these and other related results, the role of preattentive features seems to be rather different from the traditional view: visual features are general-purpose, exclusive, innate, constancy-based, and keyword-like. A GEICK conjecture is proposed which suggests that the features are conscious-level keywords generated by the specific brain area of V4 and/or IT and then used by all other brain areas.

show abstract

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

Cited by 3 publications

References 0 publications

Human attention filters for single colors

Human attention filters for single colors

Spatially intermixed objects of different categories are parsed automatically

FVS 2.0: A unifying framework for understanding the factors of visual-attentional processing.

Contact Info

Product

Resources

About