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Primates, including humans, can typically recognize objects in visual images at a glance despite naturally occurring identity-preserving image transformations (e.g., changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechanistic models that quantitatively explain this behavior by predicting primate performance for each and every image. Here, we applied this stringent behavioral prediction test to the leading mechanistic models of primate vision (specifically, deep, convolutional, artificial neural networks; ANNs) by directly comparing their behavioral signatures against those of humans and rhesus macaque monkeys. Using high-throughput data collection systems for human and monkey psychophysics, we collected more than one million behavioral trials from 1472 anonymous humans and five male macaque monkeys for 2400 images over 276 binary object discrimination tasks. Consistent with previous work, we observed that state-of-the-art deep, feedforward convolutional ANNs trained for visual categorization (termed DCNN IC models) accurately predicted primate patterns of object-level confusion. However, when we examined behavioral performance for individual images within each object discrimination task, we found that all tested DCNN IC models were significantly nonpredictive of primate performance and that this prediction failure was not accounted for by simple image attributes nor rescued by simple model modifications. These results show that current DCNN IC models cannot account for the image-level behavioral patterns of primates and that new ANN models are needed to more precisely capture the neural mechanisms underlying primate object vision. To this end, large-scale, high-resolution primate behavioral benchmarks such as those obtained here could serve as direct guides for discovering such models. SIGNIFICANCE STATEMENT Recently, specific feedforward deep convolutional artificial neural networks (ANNs) models have dramatically advanced our quantitative understanding of the neural mechanisms underlying primate core object recognition. In this work, we tested the limits of those ANNs by systematically comparing the behavioral responses of these models with the behavioral responses of humans and monkeys at the resolution of individual images. Using these high-resolution metrics, we found that all tested ANN models significantly diverged from primate behavior. Going forward, these high-resolution, large-scale primate behavioral benchmarks could serve as direct guides for discovering better ANN models of the primate visual system., (Copyright © 2018 the authors 0270-6474/18/387255-15$15.00/0.)

While early cortical visual areas contain fine scale spatial organization of neuronal properties, such as orientation preference, the spatial organization of higher-level visual areas is less well understood. The fMRI demonstration of face-preferring regions in human ventral cortex and monkey inferior temporal cortex ("face patches") raises the question of how neural selectivity for faces is organized. Here, we targeted hundreds of spatially registered neural recordings to the largest fMRI-identified face-preferring region in monkeys, the middle face patch (MFP), and show that the MFP contains a graded enrichment of face-preferring neurons. At its center, as much as 93% of the sites we sampled responded twice as strongly to faces than to nonface objects. We estimate the maximum neurophysiological size of the MFP to be ∼6 mm in diameter, consistent with its previously reported size under fMRI. Importantly, face selectivity in the MFP varied strongly even between neighboring sites. Additionally, extremely face-selective sites were ∼40 times more likely to be present inside the MFP than outside. These results provide the first direct quantification of the size and neural composition of the MFP by showing that the cortical tissue localized to the fMRI defined region consists of a very high fraction of face-preferring sites near its center, and a monotonic decrease in that fraction along any radial spatial axis., Significance Statement: The underlying organization of neurons that give rise to the large spatial regions of activity observed with fMRI is not well understood. Neurophysiological studies that have targeted the fMRI identified face patches in monkeys have provided evidence for both large-scale clustering and a heterogeneous spatial organization. Here we used a novel x-ray imaging system to spatially map the responses of hundreds of sites in and around the middle face patch. We observed that face-selective signal localized to the middle face patch was characterized by a gradual spatial enrichment. Furthermore, strongly face-selective sites were ∼40 times more likely to be found inside the patch than outside of the patch., (Copyright © 2016 the authors 0270-6474/16/3612729-17$15.00/0.)

To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT ("face patches") did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. Significance statement: We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior., (Copyright © 2015 the authors 0270-6474/15/3513402-17$15.00/0.)

Although the rhesus monkey is used widely as an animal model of human visual processing, it is not known whether invariant visual object recognition behavior is quantitatively comparable across monkeys and humans. To address this question, we systematically compared the core object recognition behavior of two monkeys with that of human subjects. To test true object recognition behavior (rather than image matching), we generated several thousand naturalistic synthetic images of 24 basic-level objects with high variation in viewing parameters and image background. Monkeys were trained to perform binary object recognition tasks on a match-to-sample paradigm. Data from 605 human subjects performing the same tasks on Mechanical Turk were aggregated to characterize "pooled human" object recognition behavior, as well as 33 separate Mechanical Turk subjects to characterize individual human subject behavior. Our results show that monkeys learn each new object in a few days, after which they not only match mean human performance but show a pattern of object confusion that is highly correlated with pooled human confusion patterns and is statistically indistinguishable from individual human subjects. Importantly, this shared human and monkey pattern of 3D object confusion is not shared with low-level visual representations (pixels, V1+; models of the retina and primary visual cortex) but is shared with a state-of-the-art computer vision feature representation. Together, these results are consistent with the hypothesis that rhesus monkeys and humans share a common neural shape representation that directly supports object perception., Significance Statement: To date, several mammalian species have shown promise as animal models for studying the neural mechanisms underlying high-level visual processing in humans. In light of this diversity, making tight comparisons between nonhuman and human primates is particularly critical in determining the best use of nonhuman primates to further the goal of the field of translating knowledge gained from animal models to humans. To the best of our knowledge, this study is the first systematic attempt at comparing a high-level visual behavior of humans and macaque monkeys., (Copyright © 2015 the authors 0270-6474/15/3512127-10$15.00/0.)

Maps obtained by functional magnetic resonance imaging (fMRI) are thought to reflect the underlying spatial layout of neural activity. However, previous studies have not been able to directly compare fMRI maps to high-resolution neurophysiological maps, particularly in higher level visual areas. Here, we used a novel stereo microfocal x-ray system to localize thousands of neural recordings across monkey inferior temporal cortex (IT), construct large-scale maps of neuronal object selectivity at subvoxel resolution, and compare those neurophysiology maps with fMRI maps from the same subjects. While neurophysiology maps contained reliable structure at the sub-millimeter scale, fMRI maps of object selectivity contained information at larger scales (>2.5 mm) and were only partly correlated with raw neurophysiology maps collected in the same subjects. However, spatial smoothing of neurophysiology maps more than doubled that correlation, while a variety of alternative transforms led to no significant improvement. Furthermore, raw spiking signals, once spatially smoothed, were as predictive of fMRI maps as local field potential signals. Thus, fMRI of the inferior temporal lobe reflects a spatially low-passed version of neurophysiology signals. These findings strongly validate the widespread use of fMRI for detecting large (>2.5 mm) neuronal domains of object selectivity but show that a complete understanding of even the most pure domains (e.g., faces vs nonface objects) requires investigation at fine scales that can currently only be obtained with invasive neurophysiological methods.

Functional magnetic resonance imaging (fMRI) has revealed multiple subregions in monkey inferior temporal cortex (IT) that are selective for images of faces over other objects. The earliest of these subregions, the posterior lateral face patch (PL), has not been studied previously at the neurophysiological level. Perhaps not surprisingly, we found that PL contains a high concentration of "face-selective" cells when tested with standard image sets comparable to those used previously to define the region at the level of fMRI. However, we here report that several different image sets and analytical approaches converge to show that nearly all face-selective PL cells are driven by the presence of a single eye in the context of a face outline. Most strikingly, images containing only an eye, even when incorrectly positioned in an outline, drove neurons nearly as well as full-face images, and face images lacking only this feature led to longer latency responses. Thus, bottom-up face processing is relatively local and linearly integrates features-consistent with parts-based models-grounding investigation of how the presence of a face is first inferred in the IT face processing hierarchy.

Although popular accounts suggest that neurons along the ventral visual processing stream become increasingly selective for particular objects, this appears at odds with the fact that inferior temporal cortical (IT) neurons are broadly tuned. To explore this apparent contradiction, we compared processing in two ventral stream stages (visual cortical areas V4 and IT) in the rhesus macaque monkey. We confirmed that IT neurons are indeed more selective for conjunctions of visual features than V4 neurons and that this increase in feature conjunction selectivity is accompanied by an increase in tolerance ("invariance") to identity-preserving transformations (e.g., shifting, scaling) of those features. We report here that V4 and IT neurons are, on average, tightly matched in their tuning breadth for natural images ("sparseness") and that the average V4 or IT neuron will produce a robust firing rate response (>50% of its peak observed firing rate) to ∼10% of all natural images. We also observed that sparseness was positively correlated with conjunction selectivity and negatively correlated with tolerance within both V4 and IT, consistent with selectivity-building and invariance-building computations that offset one another to produce sparseness. Our results imply that the conjunction-selectivity-building and invariance-building computations necessary to support object recognition are implemented in a balanced manner to maintain sparseness at each stage of processing.

Neurons at the top of primate ventral visual stream [inferior temporal cortex (IT)] have selectivity for objects that is highly tolerant to variation in the object's appearance on the retina. Previous nonhuman primate (Macaca mulatta) studies suggest that this neuronal tolerance is at least partly supported by the natural temporal contiguity of visual experience, because altering that temporal contiguity can robustly alter adult IT position and size tolerance. According to that work, it is the statistics of the subject's visual experience, not the subject's reward, that instruct the specific images that IT treats as equivalent. But is reward necessary for gating this type of learning in the ventral stream? Here we show that this is not the case--temporal tolerance learning proceeds at the same rate, regardless of reward magnitude and regardless of the temporal co-occurrence of reward, even in a behavioral task that does not require the subject to engage the object images. This suggests that the ventral visual stream uses autonomous, fully unsupervised mechanisms to constantly leverage all visual experience to help build its invariant object representation.

Our ability to recognize objects despite large changes in position, size, and context is achieved through computations that are thought to increase both the shape selectivity and the tolerance ("invariance") of the visual representation at successive stages of the ventral pathway [visual cortical areas V1, V2, and V4 and inferior temporal cortex (IT)]. However, these ideas have proven difficult to test. Here, we consider how well population activity patterns at two stages of the ventral stream (V4 and IT) discriminate between, and generalize across, different images. We found that both V4 and IT encode natural images with similar fidelity, whereas the IT population is much more sensitive to controlled, statistical scrambling of those images. Scrambling sensitivity was proportional to receptive field (RF) size in both V4 and IT, suggesting that, on average, the number of visual feature conjunctions implemented by a V4 or IT neuron is directly related to its RF size. We also found that the IT population could better discriminate between objects across changes in position, scale, and context, thus directly demonstrating a V4-to-IT gain in tolerance. This tolerance gain could be accounted for by both a decrease in single-unit sensitivity to identity-preserving transformations (e.g., an increase in RF size) and an increase in the maintenance of rank-order object selectivity within the RF. These results demonstrate that, as visual information travels from V4 to IT, the population representation is reformatted to become more selective for feature conjunctions and more tolerant to identity preserving transformations, and they reveal the single-unit response properties that underlie that reformatting.

The spatial organization of the brain's object and face representations in the temporal lobe is critical for understanding high-level vision and cognition but is poorly understood. Recently, exciting progress has been made using advanced imaging and physiology methods in humans and nonhuman primates, and the combination of such methods may be particularly powerful. Studies applying these methods help us to understand how neuronal activity, optical imaging, and functional magnetic resonance imaging signals are related within the temporal lobe, and to uncover the fine-grained and large-scale spatial organization of object and face representations in the primate brain.

Biological visual systems have the remarkable ability to recognize objects despite confounding factors such as object position, size, pose, and lighting. In primates, this ability likely results from neuronal responses at the highest stage of the ventral visual stream [inferior temporal cortex (IT)] that signal object identity while tolerating these factors. However, for even the apparently simplest IT tolerance ("invariance"), tolerance to object position on the retina, little is known about how this feat is achieved. One possibility is that IT position tolerance is innate in that discriminatory power for newly learned objects automatically generalizes across position. Alternatively, visual experience plays a role in developing position tolerance. To test these ideas, we trained adult monkeys in a difficult object discrimination task in which their visual experience with novel objects was restricted to a single retinal position. After training, we recorded the spiking activity of an unbiased population of IT neurons and found that it contained significantly greater selectivity among the newly learned objects at the experienced position compared with a carefully matched, non-experienced position. Interleaved testing with other objects shows that this difference cannot be attributed to a bias in spatial attention or neuronal sampling. We conclude from these results that, at least under some conditions, full transfer of IT neuronal selectivity across retinal position is not automatic. This finding raises the possibility that visual experience plays a role in building neuronal tolerance in the ventral visual stream and the recognition abilities it supports.

Object recognition requires both selectivity among different objects and tolerance to vastly different retinal images of the same object, resulting from natural variation in (e.g.) position, size, illumination, and clutter. Thus, discovering neuronal responses that have object selectivity and tolerance to identity-preserving transformations is fundamental to understanding object recognition. Although selectivity and tolerance are found at the highest level of the primate ventral visual stream [the inferotemporal cortex (IT)], both properties are highly varied and poorly understood. If an IT neuron has very sharp selectivity for a unique combination of object features ("diagnostic features"), this might automatically endow it with high tolerance. However, this relationship cannot be taken as given; although some IT neurons are highly object selective and some are highly tolerant, the empirical connection of these key properties is unknown. In this study, we systematically measured both object selectivity and tolerance to different identity-preserving image transformations in the spiking responses of a population of monkey IT neurons. We found that IT neurons with high object selectivity typically have low tolerance (and vice versa), regardless of how object selectivity was quantified and the type of tolerance examined. The discovery of this trade-off illuminates object selectivity and tolerance in IT and unifies a range of previous, seemingly disparate results. This finding also argues against the idea that diagnostic conjunctions of features guarantee tolerance. Instead, it is naturally explained by object recognition models in which object selectivity is built through AND-like tuning mechanisms.

Visual object recognition relies critically on learning. However, little is known about the effect of object learning in human visual cortex, and in particular how the spatial distribution of training effects relates to the distribution of object and face selectivity across the cortex before training. We scanned human subjects with high-resolution functional magnetic resonance imaging (fMRI) while they viewed novel object classes, both before and after extensive training to discriminate between exemplars within one of these object classes. Training increased the strength of the response in visual cortex to trained objects compared with untrained objects. However, training did not simply induce a uniform increase in the response to trained objects: the magnitude of this training effect varied substantially across subregions of extrastriate cortex, with some showing a twofold increase in response to trained objects and others (including the right fusiform face area) showing no significant effect of training. Furthermore, the spatial distribution of training effects could not be predicted from the spatial distribution of either pretrained responses or face selectivity. Instead, training changed the spatial distribution of activity across the cortex. These findings support a dynamic view of the ventral visual pathway in which the cortical representation of an object category is continuously modulated by experience.

The highest stages of the visual ventral pathway are commonly assumed to provide robust representation of object identity by disregarding confounding factors such as object position, size, illumination, and the presence of other objects (clutter). However, whereas neuronal responses in monkey inferotemporal cortex (IT) can show robust tolerance to position and size changes, previous work shows that responses to preferred objects are usually reduced by the presence of nonpreferred objects. More broadly, we do not yet understand multiple object representation in IT. In this study, we systematically examined IT responses to pairs and triplets of objects in three passively viewing monkeys across a broad range of object effectiveness. We found that, at least under these limited clutter conditions, a large fraction of the response of each IT neuron to multiple objects is reliably predicted as the average of its responses to the constituent objects in isolation. That is, multiple object responses depend primarily on the relative effectiveness of the constituent objects, regardless of object identity. This average effect becomes virtually perfect when populations of IT neurons are pooled. Furthermore, the average effect cannot simply be explained by attentional shifts but behaves as a primarily feedforward response property. Together, our observations are most consistent with mechanistic models in which IT neuronal outputs are normalized by summed synaptic drive into IT or spiking activity within IT and suggest that normalization mechanisms previously revealed at earlier visual areas are operating throughout the ventral visual stream.