Mapping Spatial Frequency Preferences in the Human Visual Cortex

Broderick, William F.; Benson, Noah C.; Simoncelli, Eero P.; Winawer, Jonathan

doi:10.1167/18.10.253

Cited by 2 publications

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“…Consistent with previous studies [33,34] ROI-level tuning curves were found to empirically obey a log-Gaussian relationship. We thus performed nonlinear regressions to fit curves of this form to each tuning curve (curves Figures 6A-6C).…”

Section: Quantification and Statistical Analysissupporting

confidence: 89%

“…A unique and essential aspect of our approach is that the training objective also constrained the network to exhibit brain-like responses to visual stimulation. Units at higher levels of the network were encouraged to exhibit lower spatial frequency preference [33,34], a more steep receptive field size-eccentricity relation [30,32,39,40], and greater foveal coverage [32,41] during vision than units at lower levels. We emphasize that these constraints were placed on activity generated by the network during vision only.…”

Section: A Deep Generative Network Exhibits Distinct Codes For Seen and Mental Imagesmentioning

confidence: 99%

“…Basic coding properties vary systematically with ascension of this hierarchy, with representations becoming less spatially precise and more abstract. For example, units in high-level visual areas have larger receptive fields (for a fixed eccentricity) [18,[30][31][32] and tuning functions with lower spatial frequency preference [33,34] than units in low-level visual areas. If high-level visual areas serve as the effective input during imagery, then these coding properties of seen stimuli in higher visual areas should be imparted to (or inherited by) lower visual areas during imagery.…”

Section: Introductionmentioning

confidence: 99%

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Generative Feedback Explains Distinct Brain Activity Codes for Seen and Mental Images

et al. 2020

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Section: Quantification and Statistical Analysissupporting

confidence: 89%

Section: A Deep Generative Network Exhibits Distinct Codes For Seen and Mental Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generative Feedback Explains Distinct Brain Activity Codes for Seen and Mental Images

et al. 2020

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Population Models, Not Analyses, of Human Neuroscience Measurements

Gardner

Merriam

2021

Annu. Rev. Vis. Sci.

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Selectivity for many basic properties of visual stimuli, such as orientation, is thought to be organized at the scale of cortical columns, making it difficult or impossible to measure directly with noninvasive human neuroscience measurement. However, computational analyses of neuroimaging data have shown that selectivity for orientation can be recovered by considering the pattern of response across a region of cortex. This suggests that computational analyses can reveal representation encoded at a finer spatial scale than is implied by the spatial resolution limits of measurement techniques. This potentially opens up the possibility to study a much wider range of neural phenomena that are otherwise inaccessible through noninvasive measurement. However, as we review in this article, a large body of evidence suggests an alternative hypothesis to this superresolution account: that orientation information is available at the spatial scale of cortical maps and thus easily measurable at the spatial resolution of standard techniques. In fact, a population model shows that this orientation information need not even come from single-unit selectivity for orientation tuning, but instead can result from population selectivity for spatial frequency. Thus, a categorical error of interpretation can result whereby orientation selectivity can be confused with spatial frequency selectivity. This is similarly problematic for the interpretation of results from numerous studies of more complex representations and cognitive functions that have built upon the computational techniques used to reveal stimulus orientation. We suggest in this review that these interpretational ambiguities can be avoided by treating computational analyses as models of the neural processes that give rise to measurement. Building upon the modeling tradition in vision science using considerations of whether population models meet a set of core criteria is important for creating the foundation for a cumulative and replicable approach to making valid inferences from human neuroscience measurements. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Mapping Spatial Frequency Preferences in the Human Visual Cortex

Cited by 2 publications

References 0 publications

Generative Feedback Explains Distinct Brain Activity Codes for Seen and Mental Images

Generative Feedback Explains Distinct Brain Activity Codes for Seen and Mental Images

Population Models, Not Analyses, of Human Neuroscience Measurements

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