Hey Baby, what's “up”? One- and 3-Month-Olds Experience Faces Primarily Upright but Non-Upright Faces Offer the Best Views

Are people with autism prosopagnosic?

Difficulties in various face processing tasks have been well documented in autism spectrum disorder (ASD). Several meta-analyses and numerous case-control studies have indicated that this population experiences a moderate degree of impairment, with a small percentage of studies failing to detect any impairment. One possible account of this mixed pattern of findings is heterogeneity in face processing abilities stemming from the presence of a subpopulation of prosopagnosic individuals with ASD alongside those with normal face processing skills. Samples randomly drawn from such a population, especially relatively smaller ones, would vary in the proportion of participants with prosopagnosia, resulting in a wide range of group-level deficits from mild (or none) to severe across studies. We test this prosopagnosic subpopulation hypothesis by examining three groups of participants: adults with ASD, adults with developmental prosopagnosia (DP), and a comparison group. Our results show that the prosopagnosic subpopulation hypothesis does not account for the face impairments in the broader autism spectrum. ASD observers show a continuous and graded, rather than categorical, heterogeneity that span a range of face processing skills including many with mild to moderate deficits, inconsistent with a prosopagnosic subtype account. We suggest that pathogenic origins of face deficits for at least some with ASD differ from those of DP.

The development of upright face perception depends on evolved orientation-specific mechanisms and experience

Here we examine whether our impressive ability to perceive upright faces arises from evolved orientation-specific mechanisms, our extensive experience with upright faces, or both factors. To do so, we tested Claudio, a man with a congenital joint disorder causing his head to be rotated back so that it is positioned between his shoulder blades. As a result, Claudio has seen more faces reversed in orientation to his own face than matched to it. Controls exhibited large inversion effects on all tasks, but Claudio performed similarly with upright and inverted faces in both detection and identity-matching tasks, indicating these abilities are the product of evolved mechanisms and experience. In contrast, he showed clear upright superiority when detecting "Thatcherized" faces (faces with vertically flipped features), suggesting experience plays a greater role in this judgment. Together, these findings indicate that both evolved orientation-specific mechanisms and experience contribute to our proficiency with upright faces.

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

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

Look up to the body: An eye-tracking investigation of 7-months-old infants' visual exploration of emotional body expressions

The human body is an important source of information to infer a person's emotional state. Research with adult observers indicate that the posture of the torso, arms and hands provide important perceptual cues for recognising anger, fear and happy expressions. Much less is known about whether infants process body regions differently for different body expressions. To address this issue, we used eye tracking to investigate whether infants' visual exploration patterns differed when viewing body expressions. Forty-eight 7-months-old infants were randomly presented with static images of adult female bodies expressing anger, fear and happiness, as well as an emotionally-neutral posture. Facial cues to emotional state were removed by masking the faces. We measured the proportion of looking time, proportion and number of fixations, and duration of fixations on the head, upper body and lower body regions for the different expressions. We showed that infants explored the upper body more than the lower body. Importantly, infants at this age fixated differently on different body regions depending on the expression of the body posture. In particular, infants spent a larger proportion of their looking times and had longer fixation durations on the upper body for fear relative to the other expressions. These results extend and replicate the information about infant processing of emotional expressions displayed by human bodies, and they support the hypothesis that infants' visual exploration of human bodies is driven by the upper body.

The infant's face diet: Data on 3-month-old infant-perspective experience with faces video-recorded in their typical, daily environment

This dataset represents face experience coded frame-by-frame from nearly 170 hours of infant-perspective head-mounted-camera video, recorded during their daily life by 40 3-month-old infants. It includes information about the identity of the face (e.g., caregiver, relative), length of time the face was in the field of view, location in which the face occurred, and descriptions of the situation in which the infant experienced the face. Demographic information (e.g., age, gender) about the infants who recorded the videos is also provided. For elaboration on data collection methodology, interpretation, analysis, and discussion of early face experience captured by this dataset, please see our paper These are the people in your neighbourhood: Consistency and persistence in infants' exposure to caregivers', relatives', and strangers’ faces across contexts [1].

Social Origins of Cortical Face Areas

Recently acquired fMRI data from human and macaque infants provide novel insights into the origins of cortical networks specialized for perceiving faces. Data from both species converge: cortical regions responding preferentially to faces are present and spatially organized early in infancy, although fully selective face areas emerge much later. What explains the earliest cortical responses to faces? We review two proposed mechanisms: proto-organization for simple shapes in visual cortex, and an innate subcortical schematic face template. In addition, we propose a third mechanism: infants choose to look at faces to engage in positively valenced, contingent social interactions. Activity in medial prefrontal cortex during social interactions may, directly or indirectly, guide the organization of cortical face areas.

The Developing Infant Creates a Curriculum for Statistical Learning

New efforts are using head cameras and eye-trackers worn by infants to capture everyday visual environments from the point of view of the infant learner. From this vantage point, the training sets for statistical learning develop as the sensorimotor abilities of the infant develop, yielding a series of ordered datasets for visual learning that differ in content and structure between timepoints but are highly selective at each timepoint. These changing environments may constitute a developmentally ordered curriculum that optimizes learning across many domains. Future advances in computational models will be necessary to connect the developmentally changing content and statistics of infant experience to the internal machinery that does the learning.

Faces, people and the brain: The 45th Sir Frederic Bartlett Lecture

The fact that the face is a source of diverse social signals allows us to use face and person perception as a model system for asking important psychological questions about how our brains are organised. A key issue concerns whether we rely primarily on some form of generic representation of the common physical source of these social signals (the face) to interpret them, or instead create multiple representations by assigning different aspects of the task to different specialist components. Variants of the specialist components hypothesis have formed the dominant theoretical perspective on face perception for more than three decades, but despite this dominance of formally and informally expressed theories, the underlying principles and extent of any division of labour remain uncertain. Here, I discuss three important sources of constraint: first, the evolved structure of the brain; second, the need to optimise responses to different everyday tasks; and third, the statistical structure of faces in the perceiver's environment. I show how these constraints interact to determine the underlying functional organisation of face and person perception.