FFA and OFA Encode Distinct Types of Face Identity Information

Representations of personally familiar voices are better resolved in the brain

The human voice is highly flexible, allowing for diverse expression during communication,1 but presents perceptual challenges through large acoustic variability.2,3,4,5,6,7,8,9,10,11 The ability to recognize an individual person's voice depends on the listener's ability to overcome this within-speaker variability to extract a single identity percept.2,18 Previous work has found that this process is greatly assisted by familiarity,6,9,13 with evidence suggesting that more extensive and varied exposure to a voice is associated with the formation of a more robust mental representation of it.4,8 Here, we used functional magnetic resonance imaging (fMRI) with representational similarity analysis14 to characterize how personal familiarity with a voice is reflected in neural representations. We measured and compared brain responses with voices of differing familiarity-a personally familiar voice, a voice familiarized through lab training, and a new (untrained) voice-while listeners identified these voices from naturally varying, spontaneous speech clips. Personally familiar voices elicited brain response patterns in voice-, face-, and person-selective corticesthat showed higher within- and between-speaker dissimilarity, compared with lower-familiarity lab-trained and untrained voices. These findings indicated that representations for the sounds of personally familiar voices are better resolved from each other in the brain, and they align with other research reporting intelligibility advantages for speech produced by familiar talkers.15,16,17,18 Overall, our findings suggest that extensive and varied exposure to personally familiar voices results in the development of finer-grained representations of those voices, which cannot be achieved via short-term lab training.

Inversion produces opposite size illusions for faces and food

Faces are important communicative signals in humans and face perception is believed to involve specialised mechanisms in the visual system. Several other categories of stimuli are also thought to involve specialised processes, including bodies, letters, places, and food. A recently described face size illusion shows that upright faces appear physically smaller than identical inverted faces. This illusion appears to be highly face-specific, not occurring for other stimulus categories, such as bodies, letters, and hands. In this study, we investigated whether an analogous size inversion illusion occurs for items of food, a category which has recently been found to also involve specialised processes in the visual system. The results provided a clear replication of the face size illusion, with upright faces seen as smaller than inverted faces. In contrast, items of food and everyday objects showed an effect in the opposite direction, appearing larger when upright than when inverted. These results provide further evidence for the highly face-selective nature of the face size illusion. They also provide evidence for a different size illusion which affects visual perception of food.

An fMRI dataset in response to large-scale short natural dynamic facial expression videos

Facial expression is among the most natural methods for human beings to convey their emotional information in daily life. Although the neural mechanisms of facial expression have been extensively studied employing lab-controlled images and a small number of lab-controlled video stimuli, how the human brain processes natural dynamic facial expression videos still needs to be investigated. To our knowledge, this type of data specifically on large-scale natural facial expression videos is currently missing. We describe here the natural Facial Expressions Dataset (NFED), an fMRI dataset including responses to 1,320 short (3-second) natural facial expression video clips. These video clips are annotated with three types of labels: emotion, gender, and ethnicity, along with accompanying metadata. We validate that the dataset has good quality within and across participants and, notably, can capture temporal and spatial stimuli features. NFED provides researchers with fMRI data for understanding of the visual processing of large number of natural facial expression videos.

Asymmetries in event-related potentials part 1: A systematic review of face processing studies

The human brain shows distinct lateralized activation patterns for a range of cognitive processes. One such function, which is thought to be lateralized to the right hemisphere (RH), is human face processing. Its importance for social communication and interaction has led to a plethora of studies investigating face processing in health and disease. Temporally highly resolved methods, like event-related potentials (ERPs), allow for a detailed characterization of different processing stages and their specific lateralization patterns. This systematic review aimed at disentangling some of the contradictory findings regarding the RH specialization in face processing focusing on ERP research in healthy participants. Two databases were searched for studies that investigated left and right electrodes while participants viewed (mostly neutral) facial stimuli. The included studies used a variety of different tasks, which ranged from passive viewing to memorizing faces. The final data selection highlights, that strongest lateralization to the RH was found for the N170, especially for right-handed young male participants. Left-handed, female, and older participants showed less consistent lateralization patterns. Other ERP components like the P1, P2, N2, P3, and the N400 were overall less clearly lateralized. The current review highlights that many of the assumed lateralization patterns are less clear than previously thought and that the variety of stimuli, tasks, and EEG setups used, might contribute to the ambiguous findings.

Fine-grained knowledge about manipulable objects is well-predicted by contrastive language image pre-training

Object recognition is an important ability that relies on distinguishing between similar objects (e.g., deciding which utensil(s) to use at different stages of meal preparation). Recent work describes the fine-grained organization of knowledge about manipulable objects via the study of the constituent dimensions that are most relevant to human behavior, for example, vision, manipulation, and function-based properties. A logical extension of this work concerns whether or not these dimensions are uniquely human, or can be approximated by deep learning. Here, we show that behavioral dimensions are generally well-predicted by CLIP-ViT - a multimodal network trained on a large and diverse set of image-text pairs. Moreover, this model outperforms comparison networks pre-trained on smaller, image-only datasets. These results demonstrate the impressive capacity of CLIP-ViT to approximate fine-grained object knowledge. We discuss the possible sources of this benefit relative to other models (e.g., multimodal vs. image-only pre-training, dataset size, architecture).

Assigning a social status from face adornments: an fMRI study

For at least 150,000 years, the human body has been culturally modified by the wearing of personal ornaments and probably by painting with red pigment. The present study used functional magnetic resonance imaging to explore the brain networks involved in attributing social status from face decorations. Results showed the fusiform gyrus, orbitofrontal cortex, and salience network were involved in social encoding, categorization, and evaluation. The hippocampus and parahippocampus were activated due to the memory and associative skills required for the task, while the inferior frontal gyrus likely interpreted face ornaments as symbols. Resting-state functional connectivity analysis clarified the interaction between these regions. The study highlights the importance of these neural interactions in the symbolic interpretation of social markers on the human face, which were likely active in early Homo species and intensified with Homo sapiens populations as more complex technologies were developed to culturalize the human face.

P1, N170, and N250 Event-related Potential Components Reflect Temporal Perception Processing in Face and Body Personal Identification

Human faces and bodies represent various socially important signals. Although adults encounter numerous new people in daily life, they can recognize hundreds to thousands of different individuals. However, the neural mechanisms that differentiate one person from another person are unclear. This study aimed to clarify the temporal dynamics of the cognitive processes of face and body personal identification using face-sensitive ERP components (P1, N170, and N250). The present study performed three blocks (face-face, face-body, and body-body) of different ERP adaptation paradigms. Furthermore, in the above three blocks, ERP components were used to compare brain biomarkers under three conditions (same person, different person of the same sex, and different person of the opposite sex). The results showed that the P1 amplitude for the face-face block was significantly greater than that for the body-body block, that the N170 amplitude for a different person of the same sex condition was greater than that for the same person condition in the right hemisphere only, and that the N250 amplitude gradually increased as the degree of face and body sex-social categorization grew closer (i.e., same person condition > different person of the same sex condition > different person of the opposite sex condition). These results suggest that early processing of the face and body processes the face and body separately and that structural encoding and personal identification of the face and body process the face and body collaboratively.

Neural correlates of retrospective memory confidence during face-name associative learning

The ability to accurately assess one's own memory performance during learning is essential for adaptive behavior, but the brain mechanisms underlying this metamemory function are not well understood. We investigated the neural correlates of memory accuracy and retrospective memory confidence in a face-name associative learning task using magnetoencephalography in healthy young adults (n = 32). We found that high retrospective confidence was associated with stronger occipital event-related fields during encoding and widespread event-related fields during retrieval compared to low confidence. On the other hand, memory accuracy was linked to medial temporal activities during both encoding and retrieval, but only in low-confidence trials. A decrease in oscillatory power at alpha/beta bands in the parietal regions during retrieval was associated with higher memory confidence. In addition, representational similarity analysis at the single-trial level revealed distributed but differentiable neural activities associated with memory accuracy and confidence during both encoding and retrieval. In summary, our study unveiled distinct neural activity patterns related to memory confidence and accuracy during associative learning and underscored the crucial role of parietal regions in metamemory.

Prediction error processing and sharpening of expected information across the face-processing hierarchy

The perception and neural processing of sensory information are strongly influenced by prior expectations. The integration of prior and sensory information can manifest through distinct underlying mechanisms: focusing on unexpected input, denoted as prediction error (PE) processing, or amplifying anticipated information via sharpened representation. In this study, we employed computational modeling using deep neural networks combined with representational similarity analyses of fMRI data to investigate these two processes during face perception. Participants were cued to see face images, some generated by morphing two faces, leading to ambiguity in face identity. We show that expected faces were identified faster and perception of ambiguous faces was shifted towards priors. Multivariate analyses uncovered evidence for PE processing across and beyond the face-processing hierarchy from the occipital face area (OFA), via the fusiform face area, to the anterior temporal lobe, and suggest sharpened representations in the OFA. Our findings support the proposition that the brain represents faces grounded in prior expectations.

Characterizing the discriminability of visual categorical information in strongly connected voxels

Functional brain responses are strongly influenced by connectivity. Recently, we demonstrated a major example of this: category discriminability within occipitotemporal cortex (OTC) is enhanced for voxel sets that share strong functional connectivity to distal brain areas, relative to those that share lesser connectivity. That is, within OTC regions, sets of 'most-connected' voxels show improved multivoxel pattern discriminability for tool-, face-, and place stimuli relative to voxels with weaker connectivity to the wider brain. However, understanding whether these effects generalize to other domains (e.g. body perception network), and across different levels of the visual processing streams (e.g. dorsal as well as ventral stream areas) is an important extension of this work. Here, we show that this so-called connectivity-guided decoding (CGD) effect broadly generalizes across a wide range of categories (tools, faces, bodies, hands, places). This effect is robust across dorsal stream areas, but less consistent in earlier ventral stream areas. In the latter regions, category discriminability is generally very high, suggesting that extraction of category-relevant visual properties is less reliant on connectivity to downstream areas. Further, CGD effects are primarily expressed in a category-specific manner: For example, within the network of tool regions, discriminability of tool information is greater than non-tool information. The connectivity-guided decoding approach shown here provides a novel demonstration of the crucial relationship between wider brain connectivity and complex local-level functional responses at different levels of the visual processing streams. Further, this approach generates testable new hypotheses about the relationships between connectivity and local selectivity.

Sex differences in development of functional connections in the face processing network

BACKGROUND AND PURPOSE

Understanding sex differences in typical development of the face processing network is important for elucidating disruptions during atypical development in sex-linked developmental disorders like autism spectrum disorder. Based on prior sex difference studies in other cognitive domains, this study examined whether females show increased integration of core and extended face regions with age for face viewing, while males would show increased segregation.

METHODS

This study used a cross-sectional design with typically developing children and adults (n = 133) and a functional MRI face localizer task. Psychophysiological interaction (PPI) analysis examined functional connectivity between canonical and extended face processing network regions with age, with greater segregation indexed by decreased core-extended region connectivity with age and greater integration indexed by increased core-extended region connectivity with age.

RESULTS

PPI analysis confirmed increased segregation for males-right fusiform face area (FFA) coupling to right inferior frontal gyrus (IFG) opercular when viewing faces and left amygdala when viewing objects decreased with age. Females showed increased integration with age (increased coupling of the right FFA to right IFG opercular region and right occipital face area [OFA] to right IFG orbital when viewing faces and objects, respectively) and increased segregation (decreased coupling with age of the right OFA with IFG opercular region when viewing faces).

CONCLUSIONS

Development of core and extended face processing network connectivity follows sexually dimorphic paths. These differential changes mostly occur across childhood and adolescence, with males experiencing segregation and females both segregation and integration changes in connectivity.

The anterior fusiform gyrus: The ghost in the cortical face machine

Face-selective regions in the human ventral occipito-temporal cortex (VOTC) have been defined for decades mainly with functional magnetic resonance imaging. This face-selective VOTC network is traditionally divided in a posterior 'core' system thought to subtend face perception, and regions of the anterior temporal lobe as a semantic memory component of an extended general system. In between these two putative systems lies the anterior fusiform gyrus and surrounding sulci, affected by magnetic susceptibility artifacts. Here we suggest that this methodological gap overlaps with and contributes to a conceptual gap between (visual) perception and semantic memory for faces. Filling this gap with intracerebral recordings and direct electrical stimulation reveals robust face-selectivity in the anterior fusiform gyrus and a crucial role of this region, especially in the right hemisphere, in identity recognition for both familiar and unfamiliar faces. Based on these observations, we propose an integrated theoretical framework for human face (identity) recognition according to which face-selective regions in the anterior fusiform gyrus join the dots between posterior and anterior cortical face memories.

Disentangled deep generative models reveal coding principles of the human face processing network

Despite decades of research, much is still unknown about the computations carried out in the human face processing network. Recently, deep networks have been proposed as a computational account of human visual processing, but while they provide a good match to neural data throughout visual cortex, they lack interpretability. We introduce a method for interpreting brain activity using a new class of deep generative models, disentangled representation learning models, which learn a low-dimensional latent space that "disentangles" different semantically meaningful dimensions of faces, such as rotation, lighting, or hairstyle, in an unsupervised manner by enforcing statistical independence between dimensions. We find that the majority of our model's learned latent dimensions are interpretable by human raters. Further, these latent dimensions serve as a good encoding model for human fMRI data. We next investigate the representation of different latent dimensions across face-selective voxels. We find that low- and high-level face features are represented in posterior and anterior face-selective regions, respectively, corroborating prior models of human face recognition. Interestingly, though, we find identity-relevant and irrelevant face features across the face processing network. Finally, we provide new insight into the few "entangled" (uninterpretable) dimensions in our model by showing that they match responses in the ventral stream and carry information about facial identity. Disentangled face encoding models provide an exciting alternative to standard "black box" deep learning approaches for modeling and interpreting human brain data.

Task-related modulation of motor response to emotional bodies: A TMS motor-evoked potential study

Exposure to emotional body postures during perceptual decision-making tasks has been linked to transient suppression of motor reactivity, supporting the monitoring of emotionally relevant information. However, it remains unclear whether this effect occurs implicitly, i.e., when emotional information is irrelevant to the task. To investigate this issue, we used single-pulse transcranial magnetic stimulation (TMS) to assess motor excitability while healthy participants were asked to categorize pictures of body expressions as emotional or neutral (emotion recognition task) or as belonging to a male or a female actor (gender recognition task) while receiving TMS over the motor cortex at 100 and 125 ms after picture onset. Results demonstrated that motor-evoked potentials (MEPs) were reduced for emotional body postures relative to neutral postures during the emotion recognition task. Conversely, MEPs increased for emotional body postures relative to neutral postures during the gender recognition task. These findings indicate that motor inhibition, contingent upon observing emotional body postures, is selectively associated with actively monitoring emotional features. In contrast, observing emotional body postures prompts motor facilitation when task-relevant features are non-emotional. These findings contribute to embodied cognition models that link emotion perception and action tendencies.

Decoding the temporal representation of facial expression in face-selective regions

The ability of humans to discern facial expressions in a timely manner typically relies on distributed face-selective regions for rapid neural computations. To study the time course in regions of interest for this process, we used magnetoencephalography (MEG) to measure neural responses participants viewed facial expressions depicting seven types of emotions (happiness, sadness, anger, disgust, fear, surprise, and neutral). Analysis of the time-resolved decoding of neural responses in face-selective sources within the inferior parietal cortex (IP-faces), lateral occipital cortex (LO-faces), fusiform gyrus (FG-faces), and posterior superior temporal sulcus (pSTS-faces) revealed that facial expressions were successfully classified starting from ∼100 to 150 ms after stimulus onset. Interestingly, the LO-faces and IP-faces showed greater accuracy than FG-faces and pSTS-faces. To examine the nature of the information processed in these face-selective regions, we entered with facial expression stimuli into a convolutional neural network (CNN) to perform similarity analyses against human neural responses. The results showed that neural responses in the LO-faces and IP-faces, starting ∼100 ms after the stimuli, were more strongly correlated with deep representations of emotional categories than with image level information from the input images. Additionally, we observed a relationship between the behavioral performance and the neural responses in the LO-faces and IP-faces, but not in the FG-faces and lpSTS-faces. Together, these results provided a comprehensive picture of the time course and nature of information involved in facial expression discrimination across multiple face-selective regions, which advances our understanding of how the human brain processes facial expressions.

Novel approaches to functional lateralization: Assessing information in activity patterns across hemispheres and more accurately identifying structural homologues

Functional lateralization is typically measured by comparing activation levels across the right and left hemispheres of the brain. Significant additional information, however, exists within distributed multi-voxel patterns of activity - a format not detectable by traditional activation-based analysis of functional magnetic resonance imaging (fMRI) data. We introduce and test two methods -one anatomical, one functional- that allow hemispheric information asymmetries to be detected. We first introduce and apply a novel tool that draws on brain 'surface fingerprints' to pair every location in one hemisphere with its hemispheric homologue. We use anatomical data to show that this approach is more accurate than the common distance-from-midline method for comparing bilateral regions. Next, we introduce a complementary analysis method that quantifies multivariate laterality in functional data. This new 'multivariate Laterality Index' (mLI) reflects both quantitative and qualitative information-differences across homologous activity patterns. We apply the technique here to functional data collected as participants viewed faces and non-faces. Using the previously generated surface fingerprints to pair-up homologous searchlights in each hemisphere, we use the novel multivariate laterality technique to identify face-information asymmetries across right and left counterparts of the fusiform gyrus, inferior temporal gyrus, superior parietal lobule, and early visual areas. The typical location of the fusiform face area has greater information asymmetry for faces than for shapes. More generally, we argue that the field should consider an information-based approach to lateralization.

Intracerebral electrical stimulation of the face-selective right lateral fusiform gyrus transiently impairs face identity recognition

Neuroimaging and intracranial electrophysiological studies have consistently shown the largest and most consistent face-selective neural activity in the middle portion of the human right lateral fusiform gyrus ('fusiform face area(s)', FFA). Yet, direct evidence for the critical role of this region in face identity recognition (FIR) is still lacking. Here we report the first evidence of transient behavioral impairment of FIR during focal electrical stimulation of the right FFA. Upon stimulation of an electrode contact within this region, subject CJ, who shows typical FIR ability outside of stimulation, was transiently unable to point to pictures of famous faces among strangers and to match pictures of famous or unfamiliar faces presented simultaneously for their identity. Her performance at comparable tasks with other visual materials (written names, pictures of buildings) remained unaffected by stimulation at the same location. During right FFA stimulation, CJ consistently reported that simultaneously presented faces appeared as being the same identity, with little or no distortion of the spatial face configuration. Independent electrophysiological recordings showed the largest neural face-selective and face identity activity at the critical electrode contacts. Altogether, this extensive multimodal case report supports the causal role of the right FFA in FIR.

Modeling face recognition in the predictive coding framework: A combined computational modeling and functional imaging study

The learning of new facial identities and the recognition of familiar faces are crucial processes for social interactions. Recently, a combined computational modeling and functional magnetic resonance imaging (fMRI) study used predictive coding as a biologically plausible framework to model face identity learning and to relate specific model parameters with brain activity (Apps and Tsakiris, Nat Commun 4, 2698, 2013). On the one hand, it was shown that behavioral responses on a two-option face recognition task could be predicted by the level of contextual and facial familiarity in a computational model derived from predictive-coding principles. On the other hand, brain activity in specific brain regions was associated with these parameters. More specifically, brain activity in the superior temporal sulcus (STS) varied with contextual familiarity, whereas activity in the fusiform face area (FFA) covaried with the prediction error parameter that updated facial familiarity. Literature combining fMRI assessments and computational modeling in humans still needs to be expanded. Furthermore, prior results are largely not replicated. The present study was, therefore, specifically set up to replicate these previous findings. Our results support the original findings in two critical aspects. First, on a group level, the behavioral responses were modeled best by the same computational model reported by the original authors. Second, we showed that estimates of these model parameters covary with brain activity in specific, face-sensitive brain regions. Our results thus provide further evidence that the functional properties of the face perception network conform to central principles of predictive coding. However, our study yielded diverging findings on specific computational model parameters reflected in brain activity. On the one hand, we did not find any evidence of a computational involvement of the STS. On the other hand, our results showed that activity in the right FFA was associated with multiple computational model parameters. Our data do not provide evidence for functional segregation between particular face-sensitive brain regions, as previously proposed.

Modeling naturalistic face processing in humans with deep convolutional neural networks

Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The ways in which the internal face representations in DCNNs relate to human cognitive representations and brain activity are not well understood. Nearly all previous studies focused on static face image processing with rapid display times and ignored the processing of naturalistic, dynamic information. To address this gap, we developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces). We used this naturalistic dataset to compare representational geometries estimated from DCNNs, behavioral responses, and brain responses. We found that DCNN representational geometries were consistent across architectures, cognitive representational geometries were consistent across raters in a behavioral arrangement task, and neural representational geometries in face areas were consistent across brains. Representational geometries in late, fully connected DCNN layers, which are optimized for individuation, were much more weakly correlated with cognitive and neural geometries than were geometries in late-intermediate layers. The late-intermediate face-DCNN layers successfully matched cognitive representational geometries, as measured with a behavioral arrangement task that primarily reflected categorical attributes, and correlated with neural representational geometries in known face-selective topographies. Our study suggests that current DCNNs successfully capture neural cognitive processes for categorical attributes of faces but less accurately capture individuation and dynamic features.

Modeling Biological Face Recognition with Deep Convolutional Neural Networks

Deep convolutional neural networks (DCNNs) have become the state-of-the-art computational models of biological object recognition. Their remarkable success has helped vision science break new ground, and recent efforts have started to transfer this achievement to research on biological face recognition. In this regard, face detection can be investigated by comparing face-selective biological neurons and brain areas to artificial neurons and model layers. Similarly, face identification can be examined by comparing in vivo and in silico multidimensional "face spaces." In this review, we summarize the first studies that use DCNNs to model biological face recognition. On the basis of a broad spectrum of behavioral and computational evidence, we conclude that DCNNs are useful models that closely resemble the general hierarchical organization of face recognition in the ventral visual pathway and the core face network. In two exemplary spotlights, we emphasize the unique scientific contributions of these models. First, studies on face detection in DCNNs indicate that elementary face selectivity emerges automatically through feedforward processing even in the absence of visual experience. Second, studies on face identification in DCNNs suggest that identity-specific experience and generative mechanisms facilitate this particular challenge. Taken together, as this novel modeling approach enables close control of predisposition (i.e., architecture) and experience (i.e., training data), it may be suited to inform long-standing debates on the substrates of biological face recognition.

Increasing associative plasticity in temporo-occipital back-projections improves visual perception of emotions

The posterior superior temporal sulcus (pSTS) is a critical node in a network specialized for perceiving emotional facial expressions that is reciprocally connected with early visual cortices (V1/V2). Current models of perceptual decision-making increasingly assign relevance to recursive processing for visual recognition. However, it is unknown whether inducing plasticity into reentrant connections from pSTS to V1/V2 impacts emotion perception. Using a combination of electrophysiological and neurostimulation methods, we demonstrate that strengthening the connectivity from pSTS to V1/V2 selectively increases the ability to perceive facial expressions associated with emotions. This behavior is associated with increased electrophysiological activity in both these brain regions, particularly in V1/V2, and depends on specific temporal parameters of stimulation that follow Hebbian principles. Therefore, we provide evidence that pSTS-to-V1/V2 back-projections are instrumental to perception of emotion from facial stimuli and functionally malleable via manipulation of associative plasticity.

Equivalent processing of facial expression and identity by macaque visual system and task-optimized neural network

Both the primate visual system and artificial deep neural network (DNN) models show an extraordinary ability to simultaneously classify facial expression and identity. However, the neural computations underlying the two systems are unclear. Here, we developed a multi-task DNN model that optimally classified both monkey facial expressions and identities. By comparing the fMRI neural representations of the macaque visual cortex with the best-performing DNN model, we found that both systems: (1) share initial stages for processing low-level face features which segregate into separate branches at later stages for processing facial expression and identity respectively, and (2) gain more specificity for the processing of either facial expression or identity as one progresses along each branch towards higher stages. Correspondence analysis between the DNN and monkey visual areas revealed that the amygdala and anterior fundus face patch (AF) matched well with later layers of the DNN's facial expression branch, while the anterior medial face patch (AM) matched well with later layers of the DNN's facial identity branch. Our results highlight the anatomical and functional similarities between macaque visual system and DNN model, suggesting a common mechanism between the two systems.

Multivariate functional neuroimaging analyses reveal that strength-dependent face expectations are represented in higher-level face-identity areas

Perception is an active inference in which prior expectations are combined with sensory input. It is still unclear how the strength of prior expectations is represented in the human brain. The strength, or precision, of a prior could be represented with its content, potentially in higher-level sensory areas. We used multivariate analyses of functional resonance imaging data to test whether expectation strength is represented together with the expected face in high-level face-sensitive regions. Participants were trained to associate images of scenes with subsequently presented images of different faces. Each scene predicted three faces, each with either low, intermediate, or high probability. We found that anticipation enhances the similarity of response patterns in the face-sensitive anterior temporal lobe to response patterns specifically associated with the image of the expected face. In contrast, during face presentation, activity increased for unexpected faces in a typical prediction error network, containing areas such as the caudate and the insula. Our findings show that strength-dependent face expectations are represented in higher-level face-identity areas, supporting hierarchical theories of predictive processing according to which higher-level sensory regions represent weighted priors.

Dissociation and hierarchy of human visual pathways for simultaneously coding facial identity and expression

Humans have an extraordinary ability to recognize facial expression and identity from a single face simultaneously and effortlessly, however, the underlying neural computation is not well understood. Here, we optimized a multi-task deep neural network to classify facial expression and identity simultaneously. Under various optimization training strategies, the best-performing model consistently showed 'share-separate' organization. The two separate branches of the best-performing model also exhibited distinct abilities to categorize facial expression and identity, and these abilities increased along the facial expression or identity branches toward high layers. By comparing the representational similarities between the best-performing model and functional magnetic resonance imaging (fMRI) responses in the human visual cortex to the same face stimuli, the face-selective posterior superior temporal sulcus (pSTS) in the dorsal visual cortex was significantly correlated with layers in the expression branch of the model, and the anterior inferotemporal cortex (aIT) and anterior fusiform face area (aFFA) in the ventral visual cortex were significantly correlated with layers in the identity branch of the model. Besides, the aFFA and aIT better matched the high layers of the model, while the posterior FFA (pFFA) and occipital facial area (OFA) better matched the middle and early layers of the model, respectively. Overall, our study provides a task-optimization computational model to better understand the neural mechanism underlying face recognition, which suggest that similar to the best-performing model, the human visual system exhibits both dissociated and hierarchical neuroanatomical organization when simultaneously coding facial identity and expression.

Multiple traces and altered signal-to-noise in systems consolidation: Evidence from the 7T fMRI Natural Scenes Dataset

How do the neural correlates of recognition change over time? We study natural scene image recognition spanning a year with 7-Tesla functional magnetic resonance imaging (fMRI) of the human brain. We find that the medial temporal lobe (MTL) contribution to recognition persists over 200 d, supporting multiple-trace theory and contradicting a trace transfer (from MTL to cortex) point of view. We then test the hypothesis that the signal-to-noise ratio of traces increases over time, presumably a consequence of synaptic “desaturation” in the weeks following learning. The fMRI trace signature associates with the rate of removal of competing traces and reflects a time-related enhancement of image-feature selectivity. We conclude that multiple MTL traces and improved signal-to-noise may underlie systems-level memory consolidation.

The brain mechanisms of memory consolidation remain elusive. Here, we examine blood-oxygen-level-dependent (BOLD) correlates of image recognition through the scope of multiple influential systems consolidation theories. We utilize the longitudinal Natural Scenes Dataset, a 7-Tesla functional magnetic resonance imaging human study in which ∼135,000 trials of image recognition were conducted over the span of a year among eight subjects. We find that early- and late-stage image recognition associates with both medial temporal lobe (MTL) and visual cortex when evaluating regional activations and a multivariate classifier. Supporting multiple-trace theory (MTT), parts of the MTL activation time course show remarkable fit to a 20-y-old MTT time-dynamical model predicting early trace intensity increases and slight subsequent interference (R² > 0.90). These findings contrast a simplistic, yet common, view that memory traces are transferred from MTL to cortex. Next, we test the hypothesis that the MTL trace signature of memory consolidation should also reflect synaptic “desaturation,” as evidenced by an increased signal-to-noise ratio. We find that the magnitude of relative BOLD enhancement among surviving memories is positively linked to the rate of removal (i.e., forgetting) of competing traces. Moreover, an image-feature and time interaction of MTL and visual cortex functional connectivity suggests that consolidation mechanisms improve the specificity of a distributed trace. These neurobiological effects do not replicate on a shorter timescale (within a session), implicating a prolonged, offline process. While recognition can potentially involve cognitive processes outside of memory retrieval (e.g., re-encoding), our work largely favors MTT and desaturation as perhaps complementary consolidative memory mechanisms.

Let's face it: The lateralization of the face perception network as measured with fMRI is not clearly right dominant

The neural face perception network is distributed across both hemispheres. However, the dominant role in humans is virtually unanimously attributed to the right hemisphere. Interestingly, there are, to our knowledge, no imaging studies that systematically describe the distribution of hemispheric lateralization in the core system of face perception across subjects in large cohorts so far. To address this, we determined the hemispheric lateralization of all core system regions (i.e., occipital face area (OFA), fusiform face area (FFA), posterior superior temporal sulcus (pSTS)) in 108 healthy subjects using functional magnetic resonance imaging (fMRI). We were particularly interested in the variability of hemispheric lateralization across subjects and explored how many subjects can be classified as right-dominant based on the fMRI activation pattern. We further assessed lateralization differences between different regions of the core system and analyzed the influence of handedness and sex on the lateralization with a generalized mixed effects regression model. As expected, brain activity was on average stronger in right-hemispheric brain regions than in their left-hemispheric homologues. This asymmetry was, however, only weakly pronounced in comparison to other lateralized brain functions (such as language and spatial attention) and strongly varied between individuals. Only half of the subjects in the present study could be classified as right-hemispheric dominant. Additionally, we did not detect significant lateralization differences between core system regions. Our data did also not support a general leftward shift of hemispheric lateralization in left-handers. Only the interaction of handedness and sex in the FFA revealed that specifically left-handed men were significantly more left-lateralized compared to right-handed males. In essence, our fMRI data did not support a clear right-hemispheric dominance of the face perception network. Our findings thus ultimately question the dogma that the face perception network - as measured with fMRI - can be characterized as "typically right lateralized".

Twenty years of investigation with the case of prosopagnosia PS to understand human face identity recognition. Part II: Neural basis

Patient PS sustained her dramatic brain injury in 1992, the same year as the first report of a neuroimaging study of human face recognition. The present paper complements the review on the functional nature of PS's prosopagnosia (part I), illustrating how her case study directly, i.e., through neuroimaging investigations of her brain structure and activity, but also indirectly, through neural studies performed on other clinical cases and neurotypical individuals, inspired and constrained neural models of human face recognition. In the dominant right hemisphere for face recognition in humans, PS's main lesion concerns (inputs to) the inferior occipital gyrus (IOG), in a region where face-selective activity is typically found in normal individuals ('Occipital Face Area', OFA). Her case study initially supported the criticality of this region for face identity recognition (FIR) and provided the impetus for transcranial magnetic stimulation (TMS), intracerebral electrical stimulation, and cortical surgery studies that have generally supported this view. Despite PS's right IOG lesion, typical face-selectivity is found anteriorly in the middle portion of the fusiform gyrus, a hominoid structure (termed the right 'Fusiform Face Area', FFA) that is widely considered to be the most important region for human face recognition. This finding led to the original proposal of direct anatomico-functional connections from early visual cortices to the FFA, bypassing the IOG/OFA (lulu), a hypothesis supported by further neuroimaging studies of PS, other neurological cases and neuro-typical individuals with original visual stimulation paradigms, data recordings and analyses. The proposal of a lack of sensitivity to face identity in PS's right FFA due to defective reentrant inputs from the IOG/FFA has also been supported by other cases, functional connectivity and cortical surgery studies. Overall, neural studies of, and based on, the case of prosopagnosia PS strongly question the hierarchical organization of the human neural face recognition system, supporting a more flexible and dynamic view of this key social brain function.

Higher-dimensional neural representations predict better episodic memory

Episodic memory enables humans to encode and later vividly retrieve information about our rich experiences, yet the neural representations that support this mental capacity are poorly understood. Using a large fMRI dataset (n = 468) of face-name associative memory tasks and principal component analysis to examine neural representational dimensionality (RD), we found that the human brain maintained a high-dimensional representation of faces through hierarchical representation within and beyond the face-selective regions. Critically, greater RD was associated with better subsequent memory performance both within and across participants, and this association was specific to episodic memory but not general cognitive abilities. Furthermore, the frontoparietal activities could suppress the shared low-dimensional fluctuations and reduce the correlations of local neural responses, resulting in greater RD. RD was not associated with the degree of item-specific pattern similarity, and it made complementary contributions to episodic memory. These results provide a mechanistic understanding of the role of RD in supporting accurate episodic memory.

Medial prefrontal and occipito-temporal activity at encoding determines enhanced recognition of threatening faces after 1.5 years

Studies demonstrated that faces with threatening emotional expressions are better remembered than non-threatening faces. However, whether this memory advantage persists over years and which neural systems underlie such an effect remains unknown. Here, we employed an individual difference approach to examine whether the neural activity during incidental encoding was associated with differential recognition of faces with emotional expressions (angry, fearful, happy, sad and neutral) after a retention interval of > 1.5 years (N = 89). Behaviorally, we found a better recognition for threatening (angry, fearful) versus non-threatening (happy and neutral) faces after a delay of > 1.5 years, which was driven by forgetting of non-threatening faces compared with immediate recognition after encoding. Multivariate principal component analysis (PCA) on the behavioral responses further confirmed the discriminative recognition performance between threatening and non-threatening faces. A voxel-wise whole-brain analysis on the concomitantly acquired functional magnetic resonance imaging (fMRI) data during incidental encoding revealed that neural activity in bilateral inferior occipital gyrus (IOG) and ventromedial prefrontal/orbitofrontal cortex (vmPFC/OFC) was associated with the individual differences in the discriminative emotional face recognition performance measured by an innovative behavioral pattern similarity analysis (BPSA). The left fusiform face area (FFA) was additionally determined using a regionally focused analysis. Overall, the present study provides evidence that threatening facial expressions lead to persistent face recognition over periods of > 1.5 years, and that differential encoding-related activity in the medial prefrontal cortex and occipito-temporal cortex may underlie this effect.

Acquired Prosopagnosia with Structurally Intact and Functional Fusiform Face Area and with Face Identity-Specific Configuration Processing Deficits

Prosopagnosia or loss of face perception and recognition is still poorly understood and rare single cases of acquired prosopagnosia can provide a unique window on the behavioural and brain basis of normal face perception. The present study of a new case of acquired prosopagnosia with bilateral occipito-temporal lesions but a structurally intact FFA and OFA investigated whether the lesion overlapped with the face network and whether the structurally intact FFA showed a face selective response. We also investigated the behavioral correlates of the neural findings and assessed configural processing in the context of facial and non-facial identity recognition, expression recognition and memory, also focusing on the face-selectivity of each specific deficit. The findings reveal a face-selective response in the FFA, despite lesions in the face perception network. At the behavioural level, the results showed impaired configural processing for facial identity, but not for other stimulus categories and not for facial expression recognition. These findings challenge a critical role of the FFA for face identity processing and support a domain-specific account of configural processing.

A Genuine Interindividual Variability in Number and Anatomical Localization of Face-Selective Regions in the Human Brain

Neuroimaging studies have reported regions with more neural activation to face than nonface stimuli in the human occipitotemporal cortex for three decades. Here we used a highly sensitive and reliable frequency-tagging functional magnetic resonance imaging paradigm measuring high-level face-selective neural activity to assess interindividual variability in the localization and number of face-selective clusters. Although the majority of these clusters are located in the same cortical gyri and sulci across 25 adult brains, a volume-based analysis of unsmoothed data reveals a large amount of interindividual variability in their spatial distribution and number, particularly in the ventral occipitotemporal cortex. In contrast to the widely held assumption, these face-selective clusters cannot be objectively related on a one-to-one basis across individual brains, do not correspond to a single cytoarchitectonic region, and are not clearly demarcated by estimated posteroanterior cytoarchitectonic borders. Interindividual variability in localization and number of cortical face-selective clusters does not appear to be due to the measurement noise but seems to be genuine, casting doubt on definite labeling and interindividual correspondence of face-selective "areas" and questioning their a priori definition based on cytoarchitectony or probabilistic atlases of independent datasets. These observations challenge conventional models of human face recognition based on a fixed number of discrete neurofunctional information processing stages.

Selective responses to faces, scenes, and bodies in the ventral visual pathway of infants

Three of the most robust functional landmarks in the human brain are the selective responses to faces in the fusiform face area (FFA), scenes in the parahippocampal place area (PPA), and bodies in the extrastriate body area (EBA). Are the selective responses of these regions present early in development or do they require many years to develop? Prior evidence leaves this question unresolved. We designed a new 32-channel infant magnetic resonance imaging (MRI) coil and collected high-quality functional MRI (fMRI) data from infants (2-9 months of age) while they viewed stimuli from four conditions-faces, bodies, objects, and scenes. We find that infants have face-, scene-, and body-selective responses in the location of the adult FFA, PPA, and EBA, respectively, powerfully constraining accounts of cortical development.

"I Spy with my Little Eye, Something that is a Face…": A Brain Network for Illusory Face Detection

The most basic aspect of face perception is simply detecting the presence of a face, which requires the extraction of features that it has in common with other faces. Putatively, it is caused by matching high-dimensional sensory input with internal face templates, achieved through a top-down mediated coupling between prefrontal regions and brain areas in the occipito-temporal cortex ("core system of face perception"). Illusory face detection tasks can be used to study these top-down influences. In the present functional magnetic resonance imaging study, we showed that illusory face perception activated just as real faces the core system, albeit with atypical left-lateralization of the occipital face area. The core system was coupled with two distinct brain regions in the lateral prefrontal (inferior frontal gyrus, IFG) and orbitofrontal cortex (OFC). A dynamic causal modeling (DCM) analysis revealed that activity in the core system during illusory face detection was upregulated by a modulatory face-specific influence of the IFG, not as previously assumed by the OFC. Based on these findings, we were able to develop the most comprehensive neuroanatomical framework of illusory face detection until now.

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.

Two 'what' pathways for action and object recognition

The ventral visual stream is conceived as a pathway for object recognition. However, we also recognize the actions an object can be involved in. Here, we show that action recognition critically depends on a pathway in lateral occipitotemporal cortex, partially overlapping and topographically aligned with object representations that are precursors for action recognition. By contrast, object features that are more relevant for object recognition, such as color and texture, are typically found in ventral occipitotemporal cortex. We argue that occipitotemporal cortex contains similarly organized lateral and ventral 'what' pathways for action and object recognition, respectively. This account explains a number of observed phenomena, such as the duplication of object domains and the specific representational profiles in lateral and ventral cortex.

The role of stimulus-based cues and conceptual information in processing facial expressions of emotion

Face shape and surface textures are two important cues that aid in the perception of facial expressions of emotion. Additionally, this perception is also influenced by high-level emotion concepts. Across two studies, we use representational similarity analysis to investigate the relative roles of shape, surface, and conceptual information in the perception, categorisation, and neural representation of facial expressions. In Study 1, 50 participants completed a perceptual task designed to measure the perceptual similarity of expression pairs, and a categorical task designed to measure the confusability between expression pairs when assigning emotion labels to a face. We used representational similarity analysis and constructed three models of the similarities between emotions using distinct information. Two models were based on stimulus-based cues (face shapes and surface textures) and one model was based on emotion concepts. Using multiple linear regression, we found that behaviour during both tasks was related with the similarity of emotion concepts. The model based on face shapes was more related with behaviour in the perceptual task than in the categorical, and the model based on surface textures was more related with behaviour in the categorical than the perceptual task. In Study 2, 30 participants viewed facial expressions while undergoing fMRI, allowing for the measurement of brain representational geometries of facial expressions of emotion in three core face-responsive regions (the Fusiform Face Area, Occipital Face Area, and Superior Temporal Sulcus), and a region involved in theory of mind (Medial Prefrontal Cortex). Across all four regions, the representational distances between facial expression pairs were related to the similarities of emotion concepts, but not to either of the stimulus-based cues. Together, these results highlight the important top-down influence of high-level emotion concepts both in behavioural tasks and in the neural representation of facial expressions.

Alterations in the Occipital Cortex of Drug-Naïve Adults With Major Depressive Disorder: A Surface-Based Analysis of Surface Area and Cortical Thickness

OBJECTIVE

Advances in surface-based morphometric methods have allowed researchers to separate cortical volume into cortical thickness (CTh) and surface area (SA). Although CTh alterations in major depressive disorder (MDD) have been observed in numerous studies, few studies have described significant SA alterations. Our study aimed to measure patients' SAs and to compare it with their CTh to examine whether SA exhibits alteration patterns that differ from those of CTh in drug-naïve patients with MDD.

METHODS

A total of 71 drug-naïve MDD patients and 111 healthy controls underwent structural magnetic resonance imaging, and SA and CTh were analyzed between the groups.

RESULTS

We found a smaller SA in the left superior occipital gyrus (L-SOG) in drug-naïve patients with MDD. In the CTh analysis, the bilateral fusiform gyrus, left middle occipital gyrus, left temporal superior gyrus, and right posterior cingulate showed thinner cortices in patients with MDD, while the CTh of the bilateral SOG, right straight gyrus, right posterior cingulate, and left lingual gyrus were increased.

CONCLUSION

Compared with the bilateral occipito-temporal changes in CTh, SA alterations in patients with MDD were confined to the L-SOG. These findings may improve our understanding of the neurobiological mechanisms of SA alteration in relation to MDD.

Oxytocin Modulates Neural Individuation/Categorization Processing of Faces in Early Face-Selective Areas

Oxytocin (OT) is known as a neuropeptide that promotes social adaptation. Individuating racial in-group members and viewing racial out-groups in categories is an adaptive strategy that evolved to aid effective social interaction. Nevertheless, whether OT modulates the neural individuation/categorization processing of racial in-group and out-group faces remain unknown. After intranasal OT or placebo administration, 46 male participants (OT: 24, placebo: 22) were presented with face pairs with the same or different identities or races in rapid succession. The neural repetition suppression (RS) effects to identity and race were measured using functional magnetic resonance imaging (fMRI) as indices of individuation/categorization face-processing. The results showed that while OT increased the RS effect to race, it decreased the RS effect to identity in the right fusiform face area. As for the left occipital face area, OT enlarged the differential RS effects to identities of in-group and out-group faces. Additionally, OT modulated the association of interdependence self-construal and the RS effects on identity and race. These findings bring to light preliminary evidence that OT can regulate neuronal specificity of identity and race in early face-selective regions and benefit adaptive individuation/categorization face-processing.

Separate and Shared Neural Basis of Face Memory and Face Perception in Developmental Prosopagnosia

Developmental prosopagnosia (DP), also known as face blindness, is a cognitive disorder with a severe deficit in recognizing faces. However, the heterogeneous nature of DP leads to a longstanding debate on which stages the deficit occurs, face perception (e.g., matching two consecutively presented faces) or face memory (e.g., matching a face to memorized faces). Here, we used the individual difference approach with functional magnetic resonance imaging to explore the neural substrates of DPs' face perception and face memory that may illuminate DPs' heterogeneity. Specifically, we measured the behavioral performance of face perception and face memory in a large sample of individuals suffering DP (N = 64) and then associated the behavioral performance with their face-selective neural responses in the core face network (CFN) and the extended face network (EFN), respectively. Behaviorally, we found that DP individuals were impaired in both face perception and face memory; however, there was only a weak correlation between the performances of two tasks. Consistent with this observation, the neural correlate of DPs' performance in face memory task was localized in the bilateral fusiform face area, whereas DPs' performance in face perception task was correlated with the face selectivity in the right posterior superior temporal sulcus, suggesting that the neural substrates in the CFN for face memory and face perception were separate in DP. In contrast, shared neural substrates of deficits in face perception and face memory tasks were identified in the EFN, including the right precuneus and the right orbitofrontal cortex. In summary, our study provides one of the first empirical evidence that the separate and shared neural substrates of face perception and face memory were identified in the CFN and EFN, respectively, which may help illuminating DP's heterogeneous nature.