Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human vision.

In the target article, Bowers et al. dispute deep artificial neural network (ANN) models as the currently leading models of human vision without producing alternatives. They eschew the use of public benchmarking platforms to compare vision models with the brain and behavior, and they advocate for a fragmented, phenomenon-specific modeling approach. These are unconstructive to scientific progress. We outline how the Brain-Score community is moving forward to add new model-to-human comparisons to its community-transparent suite of benchmarks.

Inconsistencies between human and macaque lesion data can be resolved with a stimulus-computable model of the ventral visual stream.

Decades of neuroscientific research has sought to understand medial temporal lobe (MTL) involvement in perception. Apparent inconsistencies in the literature have led to competing interpretations of the available evidence; critically, findings from human participants with naturally occurring MTL damage appear to be inconsistent with data from monkeys with surgical lesions. Here, we leverage a 'stimulus-computable' proxy for the primate ventral visual stream (VVS), which enables us to formally evaluate perceptual demands across stimulus sets, experiments, and species. With this modeling framework, we analyze a series of experiments administered to monkeys with surgical, bilateral damage to perirhinal cortex (PRC), an MTL structure implicated in visual object perception. Across experiments, PRC-lesioned subjects showed no impairment on perceptual tasks; this originally led us(Eldridge et al., 2018) to conclude that PRC is not involved in perception. Here, we find that a 'VVS-like' model predicts both PRC-intact and -lesioned choice behaviors, suggesting that a linear readout of the VVS should be sufficient for performance on these tasks. Evaluating these computational results alongside findings from human experiments, we suggest that results from (Eldridge et al., 2018) alone cannot be used as evidence against PRC involvement in perception. These data indicate that experimental findings from human and non-human primates are consistent. As such, what appeared to be discrepancies between species was in fact due to reliance on informal accounts of perceptual processing.

When the ventral visual stream is not enough: A deep learning account of medial temporal lobe involvement in perception.

The medial temporal lobe (MTL) supports a constellation of memory-related behaviors. Its involvement in perceptual processing, however, has been subject to enduring debate. This debate centers on perirhinal cortex (PRC), an MTL structure at the apex of the ventral visual stream (VVS). Here we leverage a deep learning framework that approximates visual behaviors supported by the VVS (i.e., lacking PRC). We first apply this approach retroactively, modeling 30 published visual discrimination experiments: excluding non-diagnostic stimulus sets, there is a striking correspondence between VVS-modeled and PRC-lesioned behavior, while each is outperformed by PRC-intact participants. We corroborate and extend these results with a novel experiment, directly comparing PRC-intact human performance to electrophysiological recordings from the macaque VVS: PRC-intact participants outperform a linear readout of high-level visual cortex. By situating lesion, electrophysiological, and behavioral results within a shared computational framework, this work resolves decades of seemingly inconsistent findings surrounding PRC involvement in perception.

Gradients of connectivity distance are anchored in primary cortex.

Connectivity between distant cortical areas is a valuable, yet costly feature of cortical organization and is predominantly found between regions of heteromodal association cortex. The recently proposed 'tethering hypothesis' describes the emergence of long-distance connections in association cortex as a function of their spatial separation from primary cortical regions. Here, we investigate this possibility by characterizing the distance between functionally connected areas along the cortical surface. We found a systematic relationship between an area's characteristic connectivity distance and its distance from primary cortical areas. Specifically, the further a region is located from primary sensorimotor regions, the more distant are its functional connections with other areas of the cortex. The measure of connectivity distance also captured major functional subdivisions of the cerebral cortex: unimodal, attention, and higher-order association regions. Our findings provide evidence for the anchoring role of primary cortical regions in establishing the spatial distribution of cortical properties that are related to functional specialization and differentiation.

A high resolution 7-Tesla resting-state fMRI test-retest dataset with cognitive and physiological measures.

Here we present a test-retest dataset of functional magnetic resonance imaging (fMRI) data acquired at rest. 22 participants were scanned during two sessions spaced one week apart. Each session includes two 1.5 mm isotropic whole-brain scans and one 0.75 mm isotropic scan of the prefrontal cortex, giving a total of six time-points. Additionally, the dataset includes measures of mood, sustained attention, blood pressure, respiration, pulse, and the content of self-generated thoughts (mind wandering). This data enables the investigation of sources of both intra- and inter-session variability not only limited to physiological changes, but also including alterations in cognitive and affective states, at high spatial resolution. The dataset is accompanied by a detailed experimental protocol and source code of all stimuli used.