Putting names to faces can sometimes be challenging, but humans are generally extremely good at recognising faces. Computers, on the other hand, often find it difficult to categorize a face as a face. Indeed, a major challenge in face recognition arises because faces come in many different shapes and sizes. Moreover, both the lighting conditions and the orientation of the head can change, which makes the challenge even more difficult.

Young infants also show a preference for pictures of human faces over nonsense images, which suggests that the ability to recognise faces is at least partly hard-wired. Neuroimaging studies have revealed that face recognition depends on activity in specific regions of the right hemisphere of the brain, and adults who sustain damage to these regions lose their face recognition skills.

De Heering and Rossion have now provided the first evidence that the right hemisphere is specialized for distinguishing between natural images of faces and ‘non-face objects’ in infants as young as 4 to 6 months. By using scalp electrodes to record electrical activity in the brain as the infants viewed images on a screen, De Heering and Rossion showed that photographs of human faces triggered a distinct pattern of electrical activity in the right hemisphere: this pattern was clearly different to the patterns triggered by photographs of animals or objects.

A consistent response was triggered by faces of different genders and expressions, and by faces presented from various viewpoints and under different lighting conditions. In a control experiment, De Heering and Rossion demonstrated that low-level visual features such as differences in luminance or contrast do not contribute to this selective response to faces.

These results argue against the idea that face perception only becomes assigned to the right hemisphere of the brain when children learn to read (that is, when language processing begins to occupy parts of the left hemisphere). By generating significant responses in a short period of time (just five minutes or less), the protocol developed by De Heering and Rossion has the potential to prove very useful to researchers investigating developmental changes to the perception of visual images during childhood.

A fundamental function of the human brain is to organize sensory events into distinct classes, that is, perceptual categorization (Rosch, 2007). This function is well illustrated in vision, the dominant sensory modality in humans: visual categorization in natural scenes occurs extremely rapidly (Thorpe et al., 1996) and in the near absence of attention (Li et al., 2002). Yet, visual categorization is extremely challenging. For instance, categorizing a visual stimulus as a face—arguably the most significant visual stimulus for human social ecology—requires to isolate the face from its natural background scene (‘figure-ground segregation’, Appelbaum et al., 2006; Peterson, 2014) and distinguish the face from the wide range of non-face stimuli in the environment which share visual properties with faces. Moreover, a common response (i.e., generalization) should be given to faces appearing under various viewing conditions (i.e., changes of head orientation, size, illumination, etc) and varying greatly in terms of gender, age, expression, ethnic origin, so on. Despite this challenge, human performance at face categorization is impressive (Crouzet et al., 2010), surpassing even the most sophisticated automatic systems (Scheirer et al., 2014).

Up to now, the ontogeny of face categorization remains largely unknown. Classical studies have reported preference for facelike over non-facelike patterns at birth (Goren et al., 1975; Johnson and Morton, 1991). At a few months of age, differences in event-related potentials (ERPs) have been found between face stimuli and meaningless patterns (Halit et al., 2004; Kouider et al., 2013) as well as between faces and exemplars of a single object category segmented from its natural background (e.g., toys, de Haan and Nelson, 1999; cars, Peykarjou and Hoehl, 2013; houses or cars, Gliga and Dehaene, 2007). However, there is no evidence on the effectiveness of infant vision in segmenting faces in natural images and representing them as a distinct, generalized category, or on the developing neural systems that may achieve this process. Clarifying this issue is also important for understanding the origin of hemispheric lateralization for face-selective processes in the human brain. In human adults, areas of the ventral and lateral occipito-temporal cortex are more active when viewing faces vs a variety of non-face objects (Sergent et al., 1992; Puce et al., 1995; Kanwisher et al., 1997; Haxby et al., 2000; Rossion et al., 2012; Weiner and Grill-Spector, 2013). This face-selective activation is typically larger in the right than the left hemisphere and, in right handed individuals at least, right unilateral brain lesions can lead to selective impairment in face recognition (prosopagnosia: e.g., Barton et al., 2002; Busigny et al., 2010; Hecaen and Angelergues, 1962; Sergent and Signoret, 1992). According to a recent hypothesis, this right hemispheric dominance for face perception, which seems specific to humans (e.g., Tsao et al., 2008), is driven by the left hemispheric lateralization for words emerging during reading acquisition (Dundas et al., 2013). Thus, according to this view, right hemispheric lateralization for faces should not be present in infancy. Up to now, infant ERP studies have not been able to provide evidence for right hemispheric lateralization of face-selective processes (de Haan and Nelson, 1999; Gliga et al., 2007; Peykarjou and Hoehl, 2013) and right hemispheric lateralization has only been observed when comparing faces to meaningless stimuli that differ in terms of low-level visual cues (Tzourio-Mazoyer et al., 2002; Kouider et al., 2013).

We addressed these outstanding issues by means of a simple ‘frequency tagging’ or ‘fast periodic visual stimulation’ (FPVS) approach, providing robust electroencephalographic (EEG) responses—steady state visual evoked potentials (SSVEPs, Regan, 1989; for a review see Norcia et al., 2015)—over the left and right hemispheres of 4- to 6-month-old infants. This approach is ideal to study the infant brain because it is relatively immune from artifacts and provides high signal-to-noise ratio (SNR) responses in a few minutes only. Moreover, compared to other approaches such as ERPs to transient stimulation, the FPVS approach is objective and predictive because the response appears exactly at the periodic frequency of stimulation defined by the experimenter. So far, infants have been tested with this approach only in response to low-level visual stimuli (i.e., acuity, contrast sensitivity, spatial phase, orientation, or motion; e.g., Braddick et al., 1986; Norcia et al., 1990). A recent EEG study tested infants with segmented faces and objects in different stimulation streams, but without testing face vs object discrimination or generalization across diverse face views, and without providing evidence of hemispheric lateralization (Farzin et al., 2012). Here, to achieve these goals, we isolated face-selective responses by means of a fast periodic oddball paradigm (Heinrich et al., 2009) recently adapted to characterize adults' individual face discrimination (Liu-Shuang et al., 2014) and face-selective responses in adults (Rossion et al., 2015).

Collectively, the findings of Experiments 1 and 2 demonstrate that the infant right hemisphere discriminates natural photographs of faces from non-face objects of multiple categories and generalizes across face photographs despite their high physical variability. In both experiments, faces are temporally embedded in a rapid stimulation sequence of non-face objects, so that there is an inherent comparison, or contrast, without the need to perform a subtraction between conditions recorded at different times. That is, there is an oddball response only because the face is discriminated from all other object categories, activating a (face-)specific population of neurons at a rate of 1.2 Hz. Although this is unlikely, we cannot formally exclude at this stage that another visual category than faces would be represented by a distinct population of neurons and would therefore also elicit an oddball response of this amplitude at 4–6 months of age. However, to our knowledge, there is no other visual category that elicits such a large specific response, with a right hemisphere advantage, in the human adult brain. Moreover, the face is arguably the most frequent and socially relevant stimulus in the human (infant) visual environment, making it the best candidate for the early development of category-selective responses.

Thanks to this original fast periodic visual stimulation (FPVS) approach, the infant's face categorization response identified here goes beyond previous observations of discrimination between segmented faces and non-face stimuli in ERPs (de Haan and Nelson, 1999; Halit et al., 2004; Gliga and Dehaene-Lambertz, 2007; Peykarjou and Hoehl, 2013), near infrared spectroscopy responses (NIRS; Kobayashi et al., 2011) or positron emission tomography (PET; Tzourio-Mazoyer et al., 2002) activations obtained with a standard presentation mode (i.e., transient, slow, and non-periodic stimulation). Despite the great interest of these studies, it is fair to say that it is difficult to define sensitive (i.e., high SNR) and objective face-selective responses in infants with a conventional stimulation mode as used in these studies, so that there is a lack of consistency across studies. Moreover, given time constraints, these studies used segmented stimuli rather than natural images, and could only compare faces to a limited number of categories. Hence, the face-selective responses obtained in previous studies could be due to systematic differences between categories in terms of a homogenous stimulus, such as contour for instance (e.g., all round faces vs rectangular pictures of cars). Finally, a significant contribution of low-level visual cues to faces vs objects responses could not be excluded from these studies, or precisely evaluated.

Here, in Experiment 2, removing shape information while preserving low-level visual differences in the power spectrum completely erased the 1.2 Hz face-selective response. In other words, the 1.2 Hz face-selective response identified here for natural face images cannot be attributed to low-level visual confounds, such as differences in power spectrum between faces and other object categories. Moreover, the face stimuli were always embedded within distinct natural backgrounds, suggesting that the infant's brain is able to perform complex figure-ground segregation. This is even more impressive considering the brief presentation duration of each face stimulus (i.e., 167 ms SOA, about 100 ms for stimulus duration above 20% contrast, see ‘Materials and methods’) and the rapid mode of stimulation where each stimulus interrupts the processing of the previous one. Considering the enormous amount of resources devoted to develop face segmentation algorithms in computer vision (Scheirer et al., 2014), this is not a trivial accomplishment.

Finding a dominant face-selective response over the right hemisphere in young infants has important implications for our understanding of hemispheric lateralization in humans. It demonstrates that the right hemispheric dominance for face-selective processes—typical of the adult brain (Hecaen and Angelergues, 1962; Hillger and Koenig, 1991; Sergent et al., 1992; Kanwisher et al., 1997; Busigny et al., 2010; Rossion, 2014; see Rossion et al., 2015 with the present approach) is already present in infancy, independently of low-level cues. This observation refutes the view that the right hemispheric lateralization for faces arises only after a few years of age, following and being driven by the left hemispheric lateralization for words that emerges during reading acquisition (Dundas et al., 2013, 2014). Rather, even if literacy can refine cortical organization for vision and language (Dehaene et al., 2010), the right hemispheric face-selective response identified here in young infants indicates that the right lateralization for face perception is present well before reading acquisition (see also Dehaene et al., 2010 for right hemisphere lateralization in illiterate adults, and Cantlon et al., 2011 for right lateralization in 4 years old children). Instead, our findings are in agreement with an early emergence of right lateralization for faces during development (de Schonen and Mathivet, 1990), a view so far based on evidence collected with face stimuli only (de Schonen and Mathivet, 1990; Tzourio-Mazoyer et al., 2002; Le Grand et al., 2003) or by comparing faces to meaningless stimuli that also differ in terms of low-level visual cues (Tzourio-Mazoyer et al., 2002; Kouider et al., 2013).

What is the origin of this early face-selective response? Some authors have suggested a face-processing module pre-specified in the genome (Farah et al., 2000), compatible with newborns' preferential looking behavior for face patterns at birth (Goren et al., 1975; Johnson and Morton, 1991; but see; Turati et al., 2002). However, infants are already extensively exposed to faces after a few months of life. Hence, face-selective responses observed here in 4–6 month-old infants may originate from a combination of initial biological constraints and of accumulation of visual experience with faces during early development. Neuroimaging studies in children show that the magnitude of face-selective neural responses is not adult-like at 7 years of age and keeps increasing until adolescence (Golarai et al., 2007, 2010; Scherf et al., 2007), suggesting that face-selectivity continues to increase during development. Given its advantages in terms of sensitivity, implicit recording and objectivity (i.e., measuring brain responses at a known, exact rate of periodic stimulation), the FPVS approach used here with electroencephalography is well positioned to test this hypothesis and characterize the full human developmental course of face processing and natural visual scene categorization.

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Author details

1. Adélaïde de Heering

   1. Psychological Sciences Research Institute, University of Louvain, Louvain-la-Neuve, Belgium
   2. Institute of Neuroscience, University of Louvain, Louvain-la-Neuve, Belgium

   Contribution

   AH, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

2. Bruno Rossion

   1. Psychological Sciences Research Institute, University of Louvain, Louvain-la-Neuve, Belgium
   2. Institute of Neuroscience, University of Louvain, Louvain-la-Neuve, Belgium

   Contribution

   BR, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   bruno.rossion@uclouvain.be

   Competing interests

   The authors declare that no competing interests exist.

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