Multimodal evidence on shape and surface information in individual face processing

The neural dynamics of familiar face recognition

Humans are highly efficient at recognising familiar faces. However, previous EEG/ERP research has given a partial and fragmented account of the neural basis of this remarkable ability. We argue that this is related to insufficient consideration of fundamental characteristics of familiar face recognition. These include image-independence (recognition across different pictures), levels of familiarity (familiar faces vary hugely in duration and intensity of our exposure to them), automaticity (we cannot voluntarily withhold from recognising a familiar face), and domain-selectivity (the degree to which face familiarity effects are selective). We review recent EEG/ERP work, combining uni- and multivariate methods, that has systematically targeted these shortcomings. We present a theoretical account of familiar face recognition, dividing it into early visual, domain-sensitive and domain-general phases, and integrating image-independence and levels of familiarity. Our account incorporates classic and more recent concepts, such as multi-dimensional face representation and course-to-fine processing. While several questions remain to be addressed, this new account represents a major step forward in our understanding of the neurophysiological basis of familiar face recognition.

The Neural Dynamics of Face Ensemble and Central Face Processing

Extensive work has investigated the neural processing of single faces, including the role of shape and surface properties. However, much less is known about the neural basis of face ensemble perception (e.g., simultaneously viewing several faces in a crowd). Importantly, the contribution of shape and surface properties have not been elucidated in face ensemble processing. Furthermore, how single central faces are processed within the context of an ensemble remains unclear. Here, we probe the neural dynamics of ensemble representation using pattern analyses as applied to electrophysiology data in healthy adults (seven males, nine females). Our investigation relies on a unique set of stimuli, depicting different facial identities, which vary parametrically and independently along their shape and surface properties. These stimuli were organized into ensemble displays consisting of six surround faces arranged in a circle around one central face. Overall, our results indicate that both shape and surface properties play a significant role in face ensemble encoding, with the latter demonstrating a more pronounced contribution. Importantly, we find that the neural processing of the center face precedes that of the surround faces in an ensemble. Further, the temporal profile of center face decoding is similar to that of single faces, while those of single faces and face ensembles diverge extensively from each other. Thus, our work capitalizes on a new center-surround paradigm to elucidate the neural dynamics of ensemble processing and the information that underpins it. Critically, our results serve to bridge the study of single and ensemble face perception.

Dynamic Suppression of Average Facial Structure Shapes Neural Tuning in Three Macaque Face Patches

The visual perception of identity in humans and other primates is thought to draw upon cortical areas specialized for the analysis of facial structure. A prominent theory of face recognition holds that the brain computes and stores average facial structure, which it then uses to efficiently determine individual identity, though the neural mechanisms underlying this process are controversial. Here, we demonstrate that the dynamic suppression of average facial structure plays a prominent role in the responses of neurons in three fMRI-defined face patches of the macaque. Using photorealistic face stimuli that systematically varied in identity level according to a psychophysically based face space, we found that single units in the AF, AM, and ML face patches exhibited robust tuning around average facial structure. This tuning emerged after the initial excitatory response to the face and was expressed as the selective suppression of sustained responses to low-identity faces. The coincidence of this suppression with increased spike timing synchrony across the population suggests a mechanism of active inhibition underlying this effect. Control experiments confirmed that the diminished responses to low-identity faces were not due to short-term adaptation processes. We propose that the brain's neural suppression of average facial structure facilitates recognition by promoting the extraction of distinctive facial characteristics and suppressing redundant or irrelevant responses across the population.

Neural processing of facial identity and expression in adults with and without autism: A multi-method approach

The ability to recognize faces and facial expressions is a common human talent. It has, however, been suggested to be impaired in individuals with autism spectrum disorder (ASD). The goal of this study was to compare the processing of facial identity and emotion between individuals with ASD and neurotypicals (NTs). Behavioural and functional magnetic resonance imaging (fMRI) data from 46 young adults (aged 17-23 years, N_ASD = 22, N_NT = 24) was analysed. During fMRI data acquisition, participants discriminated between short clips of a face transitioning from a neutral to an emotional expression. Stimuli included four identities and six emotions. We performed behavioural, univariate, multi-voxel, adaptation and functional connectivity analyses to investigate potential group differences. The ASD-group did not differ from the NT-group on behavioural identity and expression processing tasks. At the neural level, we found no differences in average neural activation, neural activation patterns and neural adaptation to faces in face-related brain regions. In terms of functional connectivity, we found that amygdala seems to be more strongly connected to inferior occipital cortex and V1 in individuals with ASD. Overall, the findings indicate that neural representations of facial identity and expression have a similar quality in individuals with and without ASD, but some regions containing these representations are connected differently in the extended face processing network.

Temporal Dynamics of the Neural Representation of Social Relationships

Humans can rapidly encode information from faces to support social judgments and facilitate interactions with others. We can also recall complex knowledge about those individuals, such as their social relationships with others, but the time course of this process has not been examined in detail. This study addressed the temporal dynamics of emerging visual and social relationship information using EEG and representational similarity analysis. Participants (female = 23, male = 10) became familiar with a 10-person social network, and were then shown faces of that network's members while EEG was recorded. To examine the temporal dynamics of the cognitive processes related to face perception, we compared the similarity structure of neural pattern responses to models of visual processing, face shape similarity, person identity, and social relationships. We found that all types of information are associated with neural patterns after a face is seen. Visual models became significant early after image onset, and identity across a change in facial expression was uniquely associated with neural patterns at several points throughout the time course. Additionally, a model reflecting perceived frequency of social interaction was present beginning at ∼110 ms, even in the absence of an explicit task to think about the relationships among the network members. This study highlights the speed and salience of social information relating to group dynamics that are present in the brain during person perception.SIGNIFICANCE STATEMENT We live our lives in social groups where complex relationships form among and around us. It is likely that some of the information about social relationships that we observe is integral during person perception, to better help us interact in differing situations with a variety of people. However, when exactly this information becomes relevant has been unclear. In this study, we present evidence that information reflecting observed relationships among a social network is spontaneously represented in whole-brain patterns shortly following presentation of a face. These results are consistent with neuroimaging studies showing spontaneous spatial representation of social network characteristics, and contribute novel insights into the timing of these neural processes.

The Face of Image Reconstruction: Progress, Pitfalls, Prospects

Recent research has demonstrated that neural and behavioral data acquired in response to viewing face images can be used to reconstruct the images themselves. However, the theoretical implications, promises, and challenges of this direction of research remain unclear. We evaluate the potential of this research for elucidating the visual representations underlying face recognition. Specifically, we outline complementary and converging accounts of the visual content, the representational structure, and the neural dynamics of face processing. We illustrate how this research addresses fundamental questions in the study of normal and impaired face recognition, and how image reconstruction provides a powerful framework for uncovering face representations, for unifying multiple types of empirical data, and for facilitating both theoretical and methodological progress.

Short-range and long-range neuronal oscillatory coupling in multiple frequency bands during face perception

Neuronal oscillatory activity has been considered to play a key role in face processing through its functional effect on information flow and exchange in human brain. Specifically, most neuronal oscillatory activity is measured in different rhythm based on the electrophysiological signal at single channel level. Although, the neuronal oscillatory coupling between neuronal assembles is associated with the information flow and exchange between brain regions, few studies focus on this type of neuronal oscillatory activity in face processing. In this study, the neuronal oscillatory coupling was investigated based on electroencephalographic (EEG) data of 20 participants, which were recorded when the participants were in a face/non-face perceptual task. The phase lag index (PLI) was used to assess the neuronal oscillatory coupling between brain regions in typical frequency bands. Enhanced short-range coupling was observed in theta (4-8 Hz) and alpha (8-12 Hz) band over the frontal region, in gamma1 (30-49 Hz) band over the left posterior and occipito-temporal regions, and in gamma2 (51-75 Hz) over the right temporal region during face perception compared with non-face perception. Long-range coupling was increased in theta and gamma band over the right hemisphere during face perception. Moreover, increased long-range coupling was observed in alpha band over the left and right hemisphere respectively during face perception. The results suggested that frequency-specific neuronal oscillatory coupling within and between regions of frontal cortex and the ventral visual pathway played an important role in face perception, which might reflect underlying neural mechanism of face perception.

Multivariate Analysis of Electrophysiological Signals Reveals the Temporal Properties of Visuomotor Computations for Precision Grips

The frontoparietal networks underlying grasping movements have been extensively studied, especially using fMRI. Accordingly, whereas much is known about their cortical locus much less is known about the temporal dynamics of visuomotor transformations. Here, we show that multivariate EEG analysis allows for detailed insights into the time course of visual and visuomotor computations of precision grasps. Male and female human participants first previewed one of several objects and, upon its reappearance, reached to grasp it with the thumb and index finger along one of its two symmetry axes. Object shape classifiers reached transient accuracies of 70% at ∼105 ms, especially based on scalp sites over visual cortex, dropping to lower levels thereafter. Grasp orientation classifiers relied on a system of occipital-to-frontal electrodes. Their accuracy rose concurrently with shape classification but ramped up more gradually, and the slope of the classification curve predicted individual reaction times. Further, cross-temporal generalization revealed that dynamic shape representation involved early and late neural generators that reactivated one another. In contrast, grasp computations involved a chain of generators attaining a sustained state about 100 ms before movement onset. Our results reveal the progression of visual and visuomotor representations over the course of planning and executing grasp movements.SIGNIFICANCE STATEMENT Grasping an object requires the brain to perform visual-to-motor transformations of the object's properties. Although much of the neuroanatomic basis of visuomotor transformations has been uncovered, little is known about its time course. Here, we orthogonally manipulated object visual characteristics and grasp orientation, and used multivariate EEG analysis to reveal that visual and visuomotor computations follow similar time courses but display different properties and dynamics.

Elucidating the Neural Representation and the Processing Dynamics of Face Ensembles

Extensive behavioral work has documented the ability of the human visual system to extract summary representations from face ensembles (e.g., the average identity of a crowd of faces). Yet, the nature of such representations, their underlying neural mechanisms, and their temporal dynamics await elucidation. Here, we examine summary representations of facial identity in human adults (of both sexes) with the aid of pattern analyses, as applied to EEG data, along with behavioral testing. Our findings confirm the ability of the visual system to form such representations both explicitly and implicitly (i.e., with or without the use of specific instructions). We show that summary representations, rather than individual ensemble constituents, can be decoded from neural signals elicited by ensemble perception, we describe the properties of such representations by appeal to multidimensional face space constructs, and we visualize their content through neural-based image reconstruction. Further, we show that the temporal profile of ensemble processing diverges systematically from that of single faces consistent with a slower, more gradual accumulation of perceptual information. Thus, our findings reveal the representational basis of ensemble processing, its fine-grained visual content, and its neural dynamics.SIGNIFICANCE STATEMENT Humans encounter groups of faces, or ensembles, in a variety of environments. Previous behavioral research has investigated how humans process face ensembles as well as the types of summary representations that can be derived from them, such as average emotion, gender, and identity. However, the neural mechanisms mediating these processes are unclear. Here, we demonstrate that ensemble representations, with different facial identity summaries, can be decoded and even visualized from neural data through multivariate analyses. These results provide, to our knowledge, the first detailed investigation into the status and the visual content of neural ensemble representations of faces. Further, the current findings shed light on the temporal dynamics of face ensembles and its relationship with single-face processing.

Representing faces in 3D

How do we recognize the individual faces of our family members, friends and acquaintances across the variation that is common in daily life? Zhan and colleagues demonstrate the importance of three-dimensional structure in the representations of known individuals and argue that texture—the surface properties of faces—plays little role in representation.