The McCollough effect with plaids and gratings: Evidence for a plaid-selective visual mechanism

Robinson, Alan; MacLeod, Don

doi:10.1167/11.1.26

Cited by 7 publications

(6 citation statements)

References 27 publications

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“…However, we note that BOLD responses may also be driven by neural assemblies tuned to other spatial properties. One possibility is the existence of explicit "plaid detectors" as suggested by recent psychophysical studies (McGovern and Peirce 2010;Peirce and Taylor 2006;Robinson and MacLeod 2011). If there are such mechanisms, supported perhaps by neurons like those described by Anzai et al (2007) tuned to combinations of orientations, then these would also be expected to contribute to the BOLD response to plaids.…”

Section: Model Name Equationmentioning

confidence: 93%

Gain control in the response of human visual cortex to plaids

McDonald

Mannion

Clifford

2012

Journal of Neurophysiology

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McDonald JS, Mannion DJ, Clifford CWG. Gain control in the response of human visual cortex to plaids. J Neurophysiol 107: 2570-2580, 2012. First published February 29, 2012 doi:10.1152/jn.00616.2011.-A recent intrinsic signal optical imaging study in tree shrew showed, surprisingly, that the population response of V1 to plaid patterns comprising grating components of equal contrast is predicted by the average of the responses to the individual components (MacEvoy SP, Tucker TR, Fitzpatrick D. Nat Neurosci 12: 637-645, 2009). This prompted us to compare responses to plaids and gratings in human visual cortex as a function of contrast and orientation. We found that the functional MRI (fMRI) blood oxygenation level-dependent (BOLD) responses of areas V1-V3 to a plaid comprising superposed grating components of equal contrast are significantly higher than the responses to a single component. Furthermore, the orientation response profile of a plaid is poorly predicted from a linear combination of the responses to its components. Together, these results indicate that the model of MacEvoy et al. (2009) cannot, without modification, account for the fMRI BOLD response to plaids in human visual cortex.contrast; spatial; functional magnetic resonance imaging; vision THE DYNAMIC RESPONSE RANGE of neurons is limited compared with the range of natural contrasts. Gain control allows neural sensitivity to be dynamically adjusted to suit the prevailing ambient contrasts (Crowder et al. 2008;Durant et al. 2007). This adjustment could be achieved through temporal sampling, i.e., adaptation (Gardner et al. 2005;Ohzawa et al. 1982), or spatial sampling in the area of the classic receptive field (Morrone et al. 1982) and extra classic receptive field (Levitt and Lund 1997) of a neuron. For two decades, divisive normalization has been the dominant model of contrast gain control (Brouwer and Heeger 2011;Heeger 1992). Principally intended to explain neuronal response saturation and crossorientation suppression-suppression of response to optimally oriented stimuli by an overlaid stimulus of a different orientation-the original divisive normalization model proposed that neuronal response is inhibited by a large pool of neurons tuned to different orientations and spatial frequencies. Although subsequent research has challenged the notion that inhibitory neural circuitry is responsible for the divisive gain behavior Freeman et al. 2002;Li et al. 2006;Priebe and Ferster 2006), the model retains considerable descriptive power and attractive theoretical properties.Two recent studies boast population, rather than isolated single-unit, measures of cross-orientation suppression (Busse et al. 2009;MacEvoy et al. 2009). Busse et al. (2009) found magnitudes of suppression in cat and human V1 consistent with traditional divisive normalization. However, MacEvoy et al. (2009) demonstrated that the response of tree shrew V1 to plaids is reliably predicted by the contrast-weighted average of the responses to the two components. In the case of two grati...

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Section: Model Name Equationmentioning

confidence: 93%

Gain control in the response of human visual cortex to plaids

McDonald

Mannion

Clifford

2012

Journal of Neurophysiology

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“…Psychophysicists have long suspected that the visual cortex contains early mechanisms specialized in processing plaids. For example, adaptation effects to plaids are distinguishable from those to gratings [2,3]. Moreover, plaid is a pop-out feature in visual search, suggesting that plaid processing is pre-attentive at an early stage of visual processing [1,4,5].…”

Section: Co-existence Of Plaid Facilitation and Cross-orientation Inhibition In A Small Portion Of Orientation-tuned Neuronsmentioning

confidence: 99%

Plaid Detectors in Macaque V1 Revealed by Two-Photon Calcium Imaging

Guan

Zhang

et al. 2020

Current Biology

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“…We are not the only group to have suggested such mechanisms. Robinson and MacLeod (2011) also used adaptation with compound gratings to show a version of the McCollough effect with plaid patterns. Nam, Solomon, Morgan, Wright, and Chubb (2009) showed pop-out effects for plaids in a visual search task, which they put down to a preattentive mechanism for which the whole is greater than the sum of the parts.…”

Section: Selective Mechanisms For Simple Conjunctions: Plaidsmentioning

confidence: 99%

Understanding mid-level representations in visual processing

Peirce

2015

Journal of Vision

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It is clear that early visual processing provides an image-based representation of the visual scene: Neurons in Striate cortex (V1) encode nothing about the meaning of a scene, but they do provide a great deal of information about the image features within it. The mechanisms of these "low-level" visual processes are relatively well understood. We can construct plausible models for how neurons, up to and including those in V1, build their representations from preceding inputs down to the level of photoreceptors. It is also clear that at some point we have a semantic, "high-level" representation of the visual scene because we can describe verbally the objects that we are viewing and their meaning to us. A huge number of studies are examining these "high-level" visual processes each year. Less well studied are the processes of "mid-level" vision, which presumably provide the bridge between these "low-level" representations of edges, colors, and lights and the "high-level" semantic representations of objects, faces, and scenes. This article and the special issue of papers in which it is published consider the nature of "mid-level" visual processing and some of the reasons why we might not have made as much progress in this domain as we would like.

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The McCollough effect with plaids and gratings: Evidence for a plaid-selective visual mechanism

Cited by 7 publications

References 27 publications

Gain control in the response of human visual cortex to plaids

Gain control in the response of human visual cortex to plaids

Plaid Detectors in Macaque V1 Revealed by Two-Photon Calcium Imaging

Understanding mid-level representations in visual processing

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