The macaque face patch system: a turtle’s underbelly for the brain

Face processing in the temporal lobe

Face perception is a socially important but complex process with many stages and many facets. There is substantial evidence from many sources that it involves a large extent of the temporal lobe, from the ventral occipitotemporal cortex and superior temporal sulci to anterior temporal regions. While early human neuroimaging work suggested a core face network consisting of the occipital face area, fusiform face area, and posterior superior temporal sulcus, studies in both humans and monkeys show a system of face patches stretching from posterior to anterior in both the superior temporal sulcus and inferotemporal cortex. Sophisticated techniques such as fMRI adaptation have shown that these face-activated regions show responses that have many of the attributes of human face processing. Lesions of some of these regions in humans lead to variants of prosopagnosia, the inability to recognize the identity of a face. Lesion, imaging, and electrophysiologic data all suggest that there is a segregation between identity and expression processing, though some suggest this may be better characterized as a distinction between static and dynamic facial information.

Modular community structure of the face network supports face recognition

Face recognition is dependent on computations conducted in specialized brain regions and the communication among them, giving rise to the face-processing network. We examined whether modularity of this network may underlie the vast individual differences found in human face recognition abilities. Modular networks, characterized by strong within and weaker between-network connectivity, were previously suggested to promote efficacy and reduce interference among cognitive systems and also correlated with better cognitive abilities. The study was conducted in a large sample (n = 409) with diffusion-weighted imaging, resting-state fMRI, and a behavioral face recognition measure. We defined a network of face-selective regions and derived a novel measure of communication along with structural and functional connectivity among them. The modularity of this network was positively correlated with recognition abilities even when controlled for age. Furthermore, the results were specific to the face network when compared with the place network or to spatially permuted null networks. The relation to behavior was also preserved at the individual-edge level such that a larger correlation to behavior was found within hemispheres and particularly within the right hemisphere. This study provides the first evidence of modularity-behavior relationships in the domain of face processing and more generally in visual perception.

Comparative connectomics of the primate social brain

Social interaction is thought to provide a selection pressure for human intelligence, yet little is known about its neurobiological basis and evolution throughout the primate lineage. Recent advances in neuroimaging have enabled whole brain investigation of brain structure, function, and connectivity in humans and non-human primates (NHPs), leading to a nascent field of comparative connectomics. However, linking social behavior to brain organization across the primates remains challenging. Here, we review the current understanding of the macroscale neural mechanisms of social behaviors from the viewpoint of system neuroscience. We first demonstrate an association between the number of cortical neurons and the size of social groups across primates, suggesting a link between neural information-processing capacity and social capabilities. Moreover, by capitalizing on recent advances in species-harmonized functional MRI, we demonstrate that portions of the mirror neuron system and default-mode networks, which are thought to be important for representation of the other's actions and sense of self, respectively, exhibit similarities in functional organization in macaque monkeys and humans, suggesting possible homologies. With respect to these two networks, we describe recent developments in the neurobiology of social perception, joint attention, personality and social complexity. Together, the Human Connectome Project (HCP)-style comparative neuroimaging, hyperscanning, behavioral, and other multi-modal investigations are expected to yield important insights into the evolutionary foundations of human social behavior.

Single-Unit Recordings Reveal the Selectivity of a Human Face Area

The exquisite capacity of primates to detect and recognize faces is crucial for social interactions. Although disentangling the neural basis of human face recognition remains a key goal in neuroscience, direct evidence at the single-neuron level is limited. We recorded from face-selective neurons in human visual cortex in a region characterized by functional magnetic resonance imaging (fMRI) activations for faces compared with objects. The majority of visually responsive neurons in this fMRI activation showed strong selectivity at short latencies for faces compared with objects. Feature-scrambled faces and face-like objects could also drive these neurons, suggesting that this region is not tightly tuned to the visual attributes that typically define whole human faces. These single-cell recordings within the human face processing system provide vital experimental evidence linking previous imaging studies in humans and invasive studies in animal models.SIGNIFICANCE STATEMENT We present the first recordings of face-selective neurons in or near an fMRI-defined patch in human visual cortex. Our unbiased multielectrode array recordings (i.e., no selection of neurons based on a search strategy) confirmed the validity of the BOLD contrast (faces-objects) in humans, a finding with implications for all human imaging studies. By presenting faces, feature-scrambled faces, and face-pareidolia (perceiving faces in inanimate objects) stimuli, we demonstrate that neurons at this level of the visual hierarchy are broadly tuned to the features of a face, independent of spatial configuration and low-level visual attributes.

Functionally homologous representation of vocalizations in the auditory cortex of humans and macaques

How the evolution of speech has transformed the human auditory cortex compared to other primates remains largely unknown. While primary auditory cortex is organized largely similarly in humans and macaques,¹ the picture is much less clear at higher levels of the anterior auditory pathway,² particularly regarding the processing of conspecific vocalizations (CVs). A "voice region" similar to the human voice-selective areas^3,4 has been identified in the macaque right anterior temporal lobe with functional MRI;⁵ however, its anatomical localization, seemingly inconsistent with that of the human temporal voice areas (TVAs), has suggested a "repositioning of the voice area" in recent human evolution.⁶ Here we report a functional homology in the cerebral processing of vocalizations by macaques and humans, using comparative fMRI and a condition-rich auditory stimulation paradigm. We find that the anterior temporal lobe of both species possesses cortical voice areas that are bilateral and not only prefer conspecific vocalizations but also implement a representational geometry categorizing them apart from all other sounds in a species-specific but homologous manner. These results reveal a more similar functional organization of higher-level auditory cortex in macaques and humans than currently known.

Linear Integration of Sensory Evidence over Space and Time Underlies Face Categorization

Visual object recognition relies on elaborate sensory processes that transform retinal inputs to object representations, but it also requires decision-making processes that read out object representations and function over prolonged time scales. The computational properties of these decision-making processes remain underexplored for object recognition. Here, we study these computations by developing a stochastic multifeature face categorization task. Using quantitative models and tight control of spatiotemporal visual information, we demonstrate that human subjects (five males, eight females) categorize faces through an integration process that first linearly adds the evidence conferred by task-relevant features over space to create aggregated momentary evidence and then linearly integrates it over time with minimum information loss. Discrimination of stimuli along different category boundaries (e.g., identity or expression of a face) is implemented by adjusting feature weights of spatial integration. This linear but flexible integration process over space and time bridges past studies on simple perceptual decisions to complex object recognition behavior.SIGNIFICANCE STATEMENT Although simple perceptual decision-making such as discrimination of random dot motion has been successfully explained as accumulation of sensory evidence, we lack rigorous experimental paradigms to study the mechanisms underlying complex perceptual decision-making such as discrimination of naturalistic faces. We develop a stochastic multifeature face categorization task as a systematic approach to quantify the properties and potential limitations of the decision-making processes during object recognition. We show that human face categorization could be modeled as a linear integration of sensory evidence over space and time. Our framework to study object recognition as a spatiotemporal integration process is broadly applicable to other object categories and bridges past studies of object recognition and perceptual decision-making.

Face Recognition by Humans and Machines: Three Fundamental Advances from Deep Learning

Deep learning models currently achieve human levels of performance on real-world face recognition tasks. We review scientific progress in understanding human face processing using computational approaches based on deep learning. This review is organized around three fundamental advances. First, deep networks trained for face identification generate a representation that retains structured information about the face (e.g., identity, demographics, appearance, social traits, expression) and the input image (e.g., viewpoint, illumination). This forces us to rethink the universe of possible solutions to the problem of inverse optics in vision. Second, deep learning models indicate that high-level visual representations of faces cannot be understood in terms of interpretable features. This has implications for understanding neural tuning and population coding in the high-level visual cortex. Third, learning in deep networks is a multistep process that forces theoretical consideration of diverse categories of learning that can overlap, accumulate over time, and interact. Diverse learning types are needed to model the development of human face processing skills, cross-race effects, and familiarity with individual faces.

Interspecies activation correlations reveal functional correspondences between marmoset and human brain areas

The common marmoset has enormous promise as a nonhuman primate model of human brain functions. While resting-state functional MRI (fMRI) has provided evidence for a similar organization of marmoset and human cortices, the technique cannot be used to map the functional correspondences of brain regions between species. This limitation can be overcome by movie-driven fMRI (md-fMRI), which has become a popular tool for noninvasively mapping the neural patterns generated by rich and naturalistic stimulation. Here, we used md-fMRI in marmosets and humans to identify whole-brain functional correspondences between the two primate species. In particular, we describe functional correlates for the well-known human face, body, and scene patches in marmosets. We find that these networks have a similar organization in both species, suggesting a largely conserved organization of higher-order visual areas between New World marmoset monkeys and humans. However, while face patches in humans and marmosets were activated by marmoset faces, only human face patches responded to the faces of other animals. Together, the results demonstrate that higher-order visual processing might be a conserved feature between humans and New World marmoset monkeys but that small, potentially important functional differences exist.

A computational neuroethology perspective on body and expression perception

Survival prompts organisms to prepare adaptive behavior in response to environmental and social threat. However, what are the specific features of the appearance of a conspecific that trigger such adaptive behaviors? For social species, the prime candidates for triggering defense systems are the visual features of the face and the body. We propose a novel approach for studying the ability of the brain to gather survival-relevant information from seeing conspecific body features. Specifically, we propose that behaviorally relevant information from bodies and body expressions is coded at the levels of midlevel features in the brain. These levels are relatively independent from higher-order cognitive and conscious perception of bodies and emotions. Instead, our approach is embedded in an ethological framework and mobilizes computational models for feature discovery.

Joint encoding of facial identity, orientation, gaze, and expression in the middle dorsal face area

The last two decades have established that a network of face-selective areas in the temporal lobe of macaque monkeys supports the visual processing of faces. Each area within the network contains a large fraction of face-selective cells. And each area encodes facial identity and head orientation differently. A recent brain-imaging study discovered an area outside of this network selective for naturalistic facial motion, the middle dorsal (MD) face area. This finding offers the opportunity to determine whether coding principles revealed inside the core network would generalize to face areas outside the core network. We investigated the encoding of static faces and objects, facial identity, and head orientation, dimensions which had been studied in multiple areas of the core face-processing network before, as well as facial expressions and gaze. We found that MD populations form a face-selective cluster with a degree of selectivity comparable to that of areas in the core face-processing network. MD encodes facial identity robustly across changes in head orientation and expression, it encodes head orientation robustly against changes in identity and expression, and it encodes expression robustly across changes in identity and head orientation. These three dimensions are encoded in a separable manner. Furthermore, MD also encodes the direction of gaze in addition to head orientation. Thus, MD encodes both structural properties (identity) and changeable ones (expression and gaze) and thus provides information about another animal's direction of attention (head orientation and gaze). MD contains a heterogeneous population of cells that establish a multidimensional code for faces.

Effective connectivity and spatial selectivity-dependent fMRI changes elicited by microstimulation of pulvinar and LIP

The thalamic pulvinar and the lateral intraparietal area (LIP) share reciprocal anatomical connections and are part of an extensive cortical and subcortical network involved in spatial attention and oculomotor processing. The goal of this study was to compare the effective connectivity of dorsal pulvinar (dPul) and LIP and to probe the dependency of microstimulation effects on task demands and spatial tuning properties of a given brain region. To this end, we applied unilateral electrical microstimulation in the dPul (mainly medial pulvinar) and LIP in combination with event-related BOLD fMRI in monkeys performing fixation and memory-guided saccade tasks. Microstimulation in both dPul and LIP enhanced task-related activity in monosynaptically-connected fronto-parietal cortex and along the superior temporal sulcus (STS) including putative face patch locations, as well as in extrastriate cortex. LIP microstimulation elicited strong activity in the opposite homotopic LIP while no homotopic activation was found with dPul stimulation. Both dPul and LIP stimulation also elicited activity in several heterotopic cortical areas in the opposite hemisphere, implying polysynaptic propagation of excitation. Despite extensive activation along the intraparietal sulcus evoked by LIP stimulation, there was a difference in frontal and occipital connectivity elicited by posterior and anterior LIP stimulation sites. Comparison of dPul stimulation with the adjacent but functionally dissimilar ventral pulvinar also showed distinct connectivity. On the level of single trial timecourses within each region of interest (ROI), most ROIs did not show task-dependence of stimulation-elicited response modulation. Across ROIs, however, there was an interaction between task and stimulation, and task-specific correlations between the initial spatial selectivity and the magnitude of stimulation effect were observed. Consequently, stimulation-elicited modulation of task-related activity was best fitted by an additive model scaled down by the initial response amplitude. In summary, we identified overlapping and distinct patterns of thalamocortical and corticocortical connectivity of pulvinar and LIP, highlighting the dorsal bank and fundus of STS as a prominent node of shared circuitry. Spatial task-specific and partly polysynaptic modulations of cue and saccade planning delay period activity in both hemispheres exerted by unilateral pulvinar and parietal stimulation provide insight into the distributed interhemispheric processing underlying spatial behavior.

Spatial Integration in Normal Face Processing and Its Breakdown in Congenital Prosopagnosia

Congenital prosopagnosia (CP), a life-long impairment in face processing that occurs in the absence of any apparent brain damage, provides a unique model in which to explore the psychological and neural bases of normal face processing. The goal of this review is to offer a theoretical and conceptual framework that may account for the underlying cognitive and neural deficits in CP. This framework may also provide a novel perspective in which to reconcile some conflicting results that permits the expansion of the research in this field in new directions. The crux of this framework lies in linking the known behavioral and neural underpinnings of face processing and their impairments in CP to a model incorporating grid cell-like activity in the entorhinal cortex. Moreover, it stresses the involvement of active, spatial scanning of the environment with eye movements and implicates their critical role in face encoding and recognition. To begin with, we describe the main behavioral and neural characteristics of CP, and then lay down the building blocks of our proposed model, referring to the existing literature supporting this new framework. We then propose testable predictions and conclude with open questions for future research stemming from this model. 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.

A new no-report paradigm reveals that face cells encode both consciously perceived and suppressed stimuli

A powerful paradigm to identify neural correlates of consciousness is binocular rivalry, wherein a constant visual stimulus evokes a varying conscious percept. It has recently been suggested that activity modulations observed during rivalry may represent the act of report rather than the conscious percept itself. Here, we performed single-unit recordings from face patches in macaque inferotemporal (IT) cortex using a no-report paradigm in which the animal’s conscious percept was inferred from eye movements. We found that large proportions of IT neurons represented the conscious percept even without active report. Furthermore, on single trials we could decode both the conscious percept and the suppressed stimulus. Together, these findings indicate that (1) IT cortex possesses a true neural correlate of consciousness and (2) this correlate consists of a population code wherein single cells multiplex representation of the conscious percept and veridical physical stimulus, rather than a subset of cells perfectly reflecting consciousness.