Perceptual similarity of visual patterns predicts dynamic neural activation patterns measured with MEG

Wardle, Susan G.; Kriegeskorte, Nikolaus; Grootswagers, Tijl; Khaligh‐Razavi, Seyed‐Mahdi; Carlson, Thomas A.

doi:10.1016/j.neuroimage.2016.02.019

Cited by 90 publications

(90 citation statements)

References 60 publications

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“…Importantly, Freeman et al (2013) later demonstrated that coarse scale biases that do not arise from the radial bias support orientation decoding. Although there is substantial evidence for a role of coarse-scale biases, the question of whether there is a contribution from information at the columnar level is still actively debated (e.g., Op de Beeck, 2010a;Freeman et al, 2013;Carlson and Wardle, 2015;Pratte et al, 2016) and has recently been extended from fMRI to MEG (Cichy et al, 2015;Stokes et al, 2015;Wardle et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Edge-Related Activity Is Not Necessary to Explain Orientation Decoding in Human Visual Cortex

et al. 2016

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Multivariate pattern analysis is a powerful technique; however, a significant theoretical limitation in neuroscience is the ambiguity in interpreting the source of decodable information used by classifiers. This is exemplified by the continued controversy over the source of orientation decoding from fMRI responses in human V1. Recently Carlson (2014) identified a potential source of decodable information by modeling voxel responses based on the Hubel and Wiesel (1972) ice-cube model of visual cortex. The model revealed that activity associated with the edges of gratings covaries with orientation and could potentially be used to discriminate orientation. Here we empirically evaluate whether "edge-related activity" underlies orientation decoding from patterns of BOLD response in human V1. First, we systematically mapped classifier performance as a function of stimulus location using population receptive field modeling to isolate each voxel's overlap with a large annular grating stimulus. Orientation was decodable across the stimulus; however, peak decoding performance occurred for voxels with receptive fields closer to the fovea and overlapping with the inner edge. Critically, we did not observe the expected second peak in decoding performance at the outer stimulus edge as predicted by the edge account. Second, we evaluated whether voxels that contribute most to classifier performance have receptive fields that cluster in cortical regions corresponding to the retinotopic location of the stimulus edge. Instead, we find the distribution of highly weighted voxels to be approximately random, with a modest bias toward more foveal voxels. Our results demonstrate that edge-related activity is likely not necessary for orientation decoding.

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Section: Introductionmentioning

confidence: 99%

Edge-Related Activity Is Not Necessary to Explain Orientation Decoding in Human Visual Cortex

et al. 2016

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“…HeinrichsGraham & Wilson, 2012; Lithari et al, 2016. It is also known that some lowlevel visual features, such as different phases of Gabor patches, produce different topographies as evidenced by topographical decoding accuracy (Wardle et al, 2016) . Having spatially segregated stimuli is perhaps the best way to facilitate different topographies (Vanegas et al, 2013) , which in turn will maximize the efficacy of the spatial filter.…”

Section: Recommendations For Ssep Experiments Design and Analysis Whenmentioning

confidence: 99%

Rhythmic entrainment source separation: Optimizing analyses of neural responses to rhythmic sensory stimulation

Cohen

Gulbinaite

2017

NeuroImage

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“…For example, if a participant were shown images of different kinds of animals, for example, pigs, sheep, cows, dogs and cats, RSA could test how similar the pattern of neural activity is for each pairwise combination of stimuli. From this, it might be possible to discern whether there is a greater degree of similarity in the neural activity in response to sheep, cows, and pigs than there is between the neural activity to cows and cats, and this might create clusters of neural similarity that might correspond to our semantic categories of pets and farm animals (see Contini et al 2017;Wardle et al 2016;Ritchie and Carlson 2016 for examples of how RSA has been used, and see Ritchie et al 2017 for a philosophical introduction to RSA).…”

Section: Resemblance and Cognitive Neuroimagingmentioning

confidence: 99%

From symbols to icons: the return of resemblance in the cognitive neuroscience revolution

Williams

Colling

2017

Synthese

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We argue that one important aspect of the "cognitive neuroscience revolution" identified by Boone and Piccinini (Synthese 193(5):1509-1534. doi:10.1007/ s11229-015-0783-4, 2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both "from below" and "from above"-that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves ("from below"), and from a greater appreciation of the problems solved by bio-cognitive systems, chiefly regulation and prediction ("from above"). We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of (predictive) dynamical models as a central postulate in neurocognitive research.

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Perceptual similarity of visual patterns predicts dynamic neural activation patterns measured with MEG

Cited by 90 publications

References 60 publications

Edge-Related Activity Is Not Necessary to Explain Orientation Decoding in Human Visual Cortex

Edge-Related Activity Is Not Necessary to Explain Orientation Decoding in Human Visual Cortex

Rhythmic entrainment source separation: Optimizing analyses of neural responses to rhythmic sensory stimulation

From symbols to icons: the return of resemblance in the cognitive neuroscience revolution

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