Face perception is a fundamental aspect of human social interaction, enabling us to distinguish individuals and interpret social cues. The underlying neural mechanisms involve a complex network of brain regions, primarily within the ventral visual stream. This article delves into the intricacies of this network, exploring its anatomical features, functional characteristics, and computational processes.
The Importance of Face Perception
The ability to perceive and interpret faces is crucial for normal social functioning. Faces provide essential visual information that we use daily to distinguish individuals. This ability is not unique to humans; it is evolutionarily relevant across species. Understanding the cognitive neuroscience of face perception involves examining how brain regions provide the representations proposed by classical theories of face perception in cognitive psychology.
Ventral Face Network: An Overview
The ventral face network, located in the occipito-temporal lobes, is specialized for processing faces. This network has both ventral and dorsal components, with distinct functional regions within each. This article focuses on the ventral component, emphasizing that it is embedded within the broader visual system. Processing in visual regions outside the face network, along with their interactions via white matter connections, contributes significantly to the efficiency of face processing.
Face-Selective Regions in the Ventral Occipito-Temporal Cortex
Functional magnetic resonance imaging (fMRI) studies have identified face-selective regions in the ventral occipito-temporal cortex. These regions exhibit higher neural responses to faces compared to other stimuli. Key regions include:
- IOG-faces: Located on the inferior occipital gyrus, also known as the occipital face area (OFA).
- pFus-faces: Situated on the posterior aspect of the fusiform gyrus, extending to the occipito-temporal sulcus, synonymous with fusiform face area one (FFA-1).
- mFus-faces: Found on the lateral aspect of the fusiform gyrus, overlapping the anterior tip of the mid-fusiform sulcus (MFS), also referred to as FFA-2.
These regions are typically found bilaterally and arranged from posterior to anterior.
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Functional Characteristics of the Ventral Face Network
The defining characteristic of the ventral face network is its heightened neural response to faces compared to other stimuli. This includes animate stimuli (bodies, animals), inanimate objects, scenes, characters, and textures. Within each region, responses to individual face exemplars are greater than those of other categories. This response pattern is consistent across sessions, tasks, and stimulus formats, including photographs, line drawings, and two-tone stimuli.
Modulation of Responses
While the ventral face network exhibits a preference for faces, its responses are modulated by various factors:
- Stimulus Properties: Position, size, illumination, contrast, and viewpoint influence neural activity. For example, upright faces elicit stronger responses than inverted faces, and centrally presented faces evoke more activity than peripheral ones.
- Top-Down Factors: Attention, expectation, and familiarity modulate responses.
Sensitivity to Face Identity
Functional magnetic resonance imaging (fMRI)-adaptation experiments have demonstrated that ventral face-selective regions are sensitive to face identity. Repeating the same face reduces responses due to neural adaptation, while increasing dissimilarity among faces elevates responses. Sensitivity to face identity is greater for upright faces. Changes in facial features and the metric relationships between features lead to recovery from fMRI-adaptation, reflecting perceived changes in identity.
Neural Responses and Face Perception
Neural responses in ventral face-selective regions correlate with individual perception. For instance, activity in mFus- and pFus-faces is lowest when faces are missed, intermediate when detected but not identified, and highest when identified. These regions are causally involved in face perception.
In summary, neural responses in the ventral face network are generally higher for faces than nonfaces, and their activity is linked to face perception. However, these responses are modulated by stimulus properties and top-down factors.
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Structural Organization of the Ventral Face Network
The cortical location of functional regions within the ventral face network is remarkably consistent across individuals, typically within a centimeter. This consistency suggests that the underlying structure of the cortex influences the organization of these regions. One prominent feature is the mid-fusiform sulcus (MFS), which is closely associated with mFus-faces/FFA-2.
Cortical Folding and Cytoarchitectonic Boundaries
The mid-fusiform sulcus (MFS) is closely coupled with mFus-faces/FFA-2. The MFS also aligns with the anterior cytoarchitectonic boundary between FG3 and FG4, further supporting the link between cortical folding and functional organization.
Microanatomical Features of the Ventral Face Network
The microscopic structure of neural tissue within each region of the ventral face network is critical to its function. Variations in neuronal size, density, and connectivity may contribute to the distinct functional roles of each region.
White Matter Connections and Face Perception
White matter connections play a crucial role in integrating information across different brain regions. Long-range connections between the ventral face network and other visual areas, as well as prefrontal regions, are essential for efficient face processing. These connections facilitate the flow of information necessary for recognizing and interpreting faces in various contexts.
Basic Computations of the Ventral Face Network: Population Receptive Fields (pRFs)
Population receptive fields (pRFs) provide insights into how individual neurons within the ventral face network encode spatial information. By measuring pRF properties, researchers can understand how each region represents different parts of the visual field and how these representations contribute to overall face perception.
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Synthesis of Neural Features and Computational Roles
Synthesizing the functional, anatomical, and computational aspects of the ventral face network is crucial for developing a comprehensive model of face perception. Understanding how these different features interact will help elucidate the mechanisms underlying our ability to recognize and interpret faces.
The Role of Ageing in Facial Emotion Processing
Facial emotions are vital for non-verbal communication, and the brain employs two routes for their analysis: a cortical route (including the Fusiform Face Area) for detailed, conscious processing and a subcortical route (including the amygdala) for fast, unconscious analysis. The cortical route processes high spatial frequencies (HSF) related to facial sex and identity, while the subcortical route handles low spatial frequencies (LSF) associated with emotional expressions.
Age-Related Changes in Emotion Processing
Ageing can affect the roles of these cerebral routes. Studies suggest that while LSF processing is important in foetuses and newborns, infants rely more on HSF for emotional processing. Older adults exhibit a "positivity bias," where negative information has less impact on their attention and memory. This bias may result from a decline in subcortical route activity and increased prefrontal cortex activity, enhancing top-down cognitive control.
Hybrid Faces and Subliminal Emotion Processing
Hybrid faces, which combine LSF emotional expressions with HSF neutral expressions, are used to study the two cerebral routes. These stimuli can reveal how the brain processes conflicting information, with the subcortical route influencing unconscious emotional judgments. Research using hybrid faces has shown that even when participants cannot explicitly identify the emotions in the LSF, happy expressions are judged as more friendly.
Spatial Frequency and Global vs. Local Analysis
LSF is linked to global stimulus analysis (e.g., spatial orientation), while HSF is involved in local element processing (e.g., facial details). The "face-inversion effect" demonstrates that global and local analyses are distinct processes. Emotional expressions are related to the global processing of stimuli, suggesting that facial emotions are rapidly processed by the subcortical route, which relies on global processing and LSF.
The Impact of Age on Subcortical Route Functioning
Studies using hybrid faces aim to understand how the subcortical route functions in ageing. A go/no-go task, where participants categorize emotional versus neutral faces, is used to compare younger and older adults. The expectation is that older adults will perform worse with LSF and hybrid faces due to a decline in subcortical activity. Conversely, no age difference is expected for unfiltered and HSF stimuli, as these activate the cortical route.
Craniofacial Phenotyping and Syndrome Diagnosis
Craniofacial abnormalities are present in 30% of genetic syndromes, making craniofacial phenotyping crucial for syndrome delineation and diagnosis. Traditional methods rely on two-dimensional images, but new tools provide three-dimensional, dynamic visualizations.
3D Visualization Tools
Interactive web applications offer 3D visualizations of craniofacial effects for various syndromes. Users can visualize syndrome facial appearance estimates, compare phenotypes, and upload 3D facial scans. These tools also provide morphological similarity maps to compare syndromes.
Quantitative Analysis and Demographic Influences
Adjusting characteristic phenotypes for demographic factors such as age and sex is a natural application of quantitative analysis. Syndromic phenotypes can vary widely depending on these metrics. Quantitative studies, whether 2D or 3D, capture means and represent the range of variation within clinical populations.
Model-Driven Approach to Syndromic Morphology
A model-driven approach produces age- and sex-specific estimates of dense syndromic morphology and texture for numerous syndromes. This can be used as a detailed reference for visualizing syndromic effects, comparing morphology, and understanding similarities between phenotypes.
Enrollment and Data Acquisition
Subjects are enrolled at outpatient clinics and patient group meetings. Three-dimensional facial images are acquired using stereophotogrammetry scanners, and an atlas-based approach is used to register meshes.
Syndromic Severity Modeling
Syndromic severity is modeled by projecting principal component scores onto a normalized syndrome coefficient vector. This allows the estimation of a syndromic severity shape component.
Interface and Submission
The interface allows users to submit facial meshes to compare an individual's morphology with syndrome atlas estimates. It supports various 3D formats.
Model Assessment and Validation
Model predictions are assessed by calculating residuals and comparing facial shapes to model predictions. The degree to which the model generates realistic instances of syndromes is also evaluated.
Classification Sensitivity
Classification sensitivity is assessed using high-dimensional regularized discriminant analysis (HDRDA). The majority of syndromes classify with high sensitivity, indicating that model predictions do not produce extreme deviations from the intended phenotypes.
Web Application and Features
A web application has been developed to provide interactive visualizations of syndromic morphology. The application includes features for visualizing phenotypic heterogeneity, modifying severity, and displaying gestalts with or without texture.
Influence of Stimulus Size on Emotional Face Perception
Encountering an individual with an angry expression requires rapid recognition and response. Research supports the preferential processing of emotional facial expressions, although this is moderated by factors like task demands. Physical distance and accurate representation of emotional cues modulate face processing.
Stimulus Size and Neurophysiological Processing
Stimulus size can affect face processing in several ways. Early visual processing is influenced by stimulus features like luminance and spatial frequencies, which are affected by retinal size. Additionally, stimulus size correlates with perceived physical proximity.
Prior Research
Previous research suggests that perception of biologically relevant stimuli is enhanced as stimulus size increases. Physiological activations, such as pupil diameter and heart rate, are affected by the size of emotional faces. The size of faces also modulates emotion judgments and eye movements.
Event-Related Potentials (ERPs)
Event-related potentials (ERPs) have been used to examine the influence of stimulus size on emotional stimuli processing. Interactions between stimulus size and emotional valence have been reported at mid-latency components. Early effects (P1) have been reported for looming angry faces, and later effects (P3) have been modulated by size for pain-administered faces.
Specific ERP Components
- P1: An occipital positivity sensitive to size and contrast configuration.
- N170: A negative deflection related to face perception, enhanced for faces compared to other objects.
- EPN: Reflects selective attention to hedonic and arousing stimuli.
- LPC/LPP: Modulated by size and emotional content, sensitive to task demands.
Hypotheses
It is hypothesized that different stages of face processing will be differentially affected by stimulus size, with stronger size effects in early processing and size-emotion interactions in later processing.
Methods and Materials
Faces were selected from the Göttingen face database (GFD) and the Radboud face database, with some manipulated using generative adversarial networks (GANs) to create artificial expressions. Stimuli were presented in three sizes, and scrambled versions were created as control stimuli.
Creating Fake Expressions
Artificial faces were created to have a larger stimulus set with comparable attributes. GANs were used to generate happy, angry, and neutral expressions from previously non-expressive faces.
Facial Modeling and Measurement Based on Topographical Features
Measuring human faces is essential for recognition and genetic phenotyping. Anthropometric landmarks are conventional measurement points, but digital scans are increasingly used despite homology difficulties.
Alternative Basis for Facial Measurement
An alternative basis for facial measurement is introduced, which provides richer information density, derives homology from shared facial topography, and quantifies local morphological variation.
Parametric Model
A parametric model is demonstrated, matching a broad range of facial variation by adjusting 71 parameters. The model's surface can be adjusted to match photogrammetric surface meshes, providing an efficient means for facial shape encoding.
Evaluation
The utility of this method is evaluated by applying multivariate biometrical methods and comparing the results with those derived by morphometric analysis of landmarks.
Human Face as a Mosaic of Homologous Features
By considering the human face as a mosaic of homologous features, anatomical description reduces to their individual descriptions.
Topographical Description
Topographic terms are intuitive and reflect how we visually perceive surfaces. The topography of the human face is constrained by its underlying anatomy and physiology.
Parametric Surface Modeling and Deformation
A surface is commonly represented by a dense set of point measurements organized into a polygonal mesh. Statistical analysis of face scans can be subjected to principal components analysis to create a parametric âface space.â