Population Models, Not Analyses, of Human Neuroscience Measurements

Abstract

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.

Representations in human primary visual cortex drift over time

“…Changes that occur independently within each neuron are likely to cancel out in an fMRI measurement. On the other hand, population measurements will effectively amplify changes in neural activity that are correlated across nearby neurons 26, 27 . It is possible that tuning changes occur independently for individual neurons, canceling out at the population level, as measured with fMRI.…”

Natural scene sampling reveals reliable coarse-scale orientation tuning in human V1

“…Specifically, at the stimulus edge there is more power for the radial orientation than for other orientations. These observations challenge the interpretation of a large body of studies over the past 20 years that were presumed to measure orientation-selective responses in humans 19 .…”

“…Such “synthetic” grating stimuli are optimal for driving individual V1 neurons because they can be presented in full-contrast and because the parameters of the stimuli (size, position, and spatial frequency) can be carefully tailored to the individual neuron being recorded. However, such gratings may be less appropriate when studying large neural populations, as with fMRI, since a single voxel reflects the pooled activity of many neurons with a wide range of selectivities, and therefore no single grating will be optimal for every neuron contributing to the voxel’s response 19 . Natural scene stimuli are inherently broadband along multiple dimensions, and hence may be more appropriate for studying population responses.…”

Layer-specific, retinotopically-diffuse modulation in human visual cortex by emotional faces

“…Feedforward, stimulus-driven patterns of activity have been studied extensively and in great detail in human visual cortex using fMRI (for reviews, see Ref 67 ). By contrast, relatively little is known about the role of feedback in human visual cortex.…”