Responses to Contour Features in Macaque Area V4

Pasupathy, Anitha; Connor, Charles E.

doi:10.1152/jn.1999.82.5.2490

Cited by 486 publications

(407 citation statements)

References 44 publications

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“…It is well known that many V4 neurons are selective for color and/or orientation even when the stimulus in the receptive field is irrelevant to the current behavior (9,12,32). Feature attention enhances preexisting feature selectivity when the monkey is searching for a target with a feature for which the neuron is selective (2).…”

Section: Causes Of the Difference In V4 Responses In The Search And Smentioning

confidence: 99%

Feature attention evokes task-specific pattern selectivity in V4 neurons

Ipata

Gee

Goldberg

2012

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

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A hallmark of visual cortical neurons is their selectivity for stimulus pattern features, such as color, orientation, or shape. In most cases this feature selectivity is hard-wired, with selectivity manifest from the beginning of the response. Here we show that when a task requires that a monkey distinguish between patterns, V4 develops a selectivity for the sought-after pattern, which it does not manifest in a task that does not require the monkey to distinguish between patterns. When a monkey looks for a target object among an array of distractors, V4 neurons become selective for the target ∼50 ms after the visual latency independent of the impending saccade direction. However, when the monkey has to only make a saccade to the spatial location of the same objects without discriminating their pattern, V4 neurons do not distinguish the search target from the distractors. This selectivity for stimulus pattern develops roughly 40 ms after the same neurons’ selectivity for basic pattern features like orientation or color. We suggest that this late-developing selectivity is related to the phenomenon of feature attention and may contribute to the mechanisms by which the brain finds the target in visual search.

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Section: Causes Of the Difference In V4 Responses In The Search And Smentioning

confidence: 99%

Feature attention evokes task-specific pattern selectivity in V4 neurons

Ipata

Gee

Goldberg

2012

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

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“…The 2-s presentation was selected because we want to eliminate accommodation effects and assure enough time for steady fixation. We had already observed that deliberate fixation at the borders increased thresholds (15). The order of the stimulus pair was randomly selected.…”

Section: Methodsmentioning

confidence: 99%

“…However, these investigators do not address this specific theory. Other physiological studies have used traditional sine wave gratings, checkerboard-like stimuli or complex form stimuli (11)(12)(13) as well as faces and hands (14) to investigate cell responses from striate cortex to higher visual areas (15,16). Pasupathy and Connor (15) measured cell responses to angles and found "strong bias towards" responses to convex angles.…”

Section: Introductionmentioning

confidence: 99%

Narrow-band 1, 2, 3, 4, 8, 16 and 24 cycles/360º angular frequency filters

Simas

Santos

2002

Braz J Med Biol Res

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We measured human frequency response functions for seven angular frequency filters whose test frequencies were centered at 1, 2, 3, 4, 8, 16 or 24 cycles/360º using a supra-threshold summation method. The seven functions of 17 experimental conditions each were measured nine times for five observers. For the arbitrarily selected filter phases, the maximum summation effect occurred at test frequency for filters at 1, 2, 3, 4 and 8 cycles/360º. For both 16 and 24 cycles/360º test frequencies, maximum summation occurred at the lower harmonics. These results allow us to conclude that there are narrow-band angular frequency filters operating somehow in the human visual system either through summation or inhibition of specific frequency ranges. Furthermore, as a general result, it appears that addition of higher angular frequencies to lower ones disturbs low angular frequency perception (i.e., 1, 2, 3 and 4 cycles/360º), whereas addition of lower harmonics to higher ones seems to improve detection of high angular frequency harmonics (i.e., 8, 16 and 24 cycles/360º). Finally, we discuss the possible involvement of coupled radial and angular frequency filters in face perception using an example where narrow-band low angular frequency filters could have a major role.

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“…Therefore, it is conceivable that V1 may represent photographs and line drawings in a similar way. Visual areas V2, VP (known as V3v, for ventral V3, in some nomenclatures), and V4 are of interest because they build more complex representations based on the information in V1, including representations that rely on more global aspects of the image (16)(17)(18). The PPA and the RSC, which have been shown to prefer scenes over objects and other visual stimuli (19,20), are of interest in this analysis because they (and, to some extent, the LOC) contain information about scene category in their activity patterns that are closely linked to behavioral scene-categorization performance (12).…”

mentioning

confidence: 99%

Simple line drawings suffice for functional MRI decoding of natural scene categories

Walther

Chai

Caddigan

et al. 2011

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

220

203

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Humans are remarkably efficient at categorizing natural scenes. In fact, scene categories can be decoded from functional MRI (fMRI) data throughout the ventral visual cortex, including the primary visual cortex, the parahippocampal place area (PPA), and the retrosplenial cortex (RSC). Here we ask whether, and where, we can still decode scene category if we reduce the scenes to mere lines. We collected fMRI data while participants viewed photographs and line drawings of beaches, city streets, forests, highways, mountains, and offices. Despite the marked difference in scene statistics, we were able to decode scene category from fMRI data for line drawings just as well as from activity for color photographs, in primary visual cortex through PPA and RSC. Even more remarkably, in PPA and RSC, error patterns for decoding from line drawings were very similar to those from color photographs. These data suggest that, in these regions, the information used to distinguish scene category is similar for line drawings and photographs. To determine the relative contributions of local and global structure to the human ability to categorize scenes, we selectively removed long or short contours from the line drawings. In a category-matching task, participants performed significantly worse when long contours were removed than when short contours were removed. We conclude that global scene structure, which is preserved in line drawings, plays an integral part in representing scene categories.

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Responses to Contour Features in Macaque Area V4

Cited by 486 publications

References 44 publications

Feature attention evokes task-specific pattern selectivity in V4 neurons

Feature attention evokes task-specific pattern selectivity in V4 neurons

Narrow-band 1, 2, 3, 4, 8, 16 and 24 cycles/360º angular frequency filters

Simple line drawings suffice for functional MRI decoding of natural scene categories

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