Infants use a region on the right side of their brain to distinguish between human faces and objects.
Human faces are an important part of social interactions. We use them to recognize a friend, to gauge someone's mood, or to figure out where to direct our attention. But before engaging in any of these activities, we must first identify a face as a face.
The fundamental question of how a perceptual system, such as the one underlying face recognition, becomes organized in the brain is important for understanding how changes in the brain lead to changes in behavior. Studying face perception in developing infants could help us to understand the parts of the brain that contribute to adult face perception. It might also reveal how face-processing abilities can be impaired in some populations, such as people with Autism.
In adults, the right hemisphere of the brain is critical for recognizing faces. Damage to the right hemisphere, but not the left hemisphere, can impair face recognition. Moreover, the right hemisphere produces larger brain responses than the left hemisphere when a face is seen. This has been witnessed using two different neuroimaging methods. The first, called functional magnetic resonance imaging (fMRI), measures the flow of blood around the brain and relates this to brain activity (e.g., Kanwisher et al., 1997). The second directly measures event-related potentials (ERPs)—the electrical response of a brain region to a stimulus (e.g., Rossion et al., 2003).
In children, there is also evidence that the right and left hemispheres of the brain respond differently to faces (Scherf et al., 2007). Recently it was reported that the response of the right hemisphere to faces is intricately linked to changes that occur in the left hemisphere when children learn to read (Dundas et al., 2014). To date, the majority of studies on infants (who are too young to read) have found no significant differences in how the two sides of the brain respond to faces (e.g., de Haan and Nelson, 1999; Gliga and Dehaene-Lambertz, 2007). However, one group did find response differences between hemispheres when comparing faces to markedly less complex stimuli (patterns of colored dots) (Tzourio-Mazoyer et al., 2002).
Now, in eLife, Adélaïde de Heering and Bruno Rossion from the University of Louvain have used a fast periodic visual stimulation (FPVS) approach to explore face perception in a group of infants aged between four and six months (de Heering and Rossion, 2015). This approach involves presenting images at a rapid, fixed rate in order to induce brain responses that occur at the same rate (often defined as ‘steady-state visual evoked potentials’, SSVEPs; Regan, 1966; for a review, see Norcia et al., 2015).
de Heering and Rossion report that, in the brains of these infants, faces are represented as a distinct category of objects, separate from other categories of objects such as plants or man-made objects. This distinction can be seen most prominently in the response recorded over the right occipito-temporal brain region, which is near the back of the brain. Importantly, faces that vary in size, viewpoint and features (such as the expression and the gender of the faces) are all categorized as faces. This is even the case when the images include naturalistic backgrounds.
These findings provide evidence that by the time they are six months old, infants possess a relatively robust ability to identify that faces are different from objects, and can do so in a realistic context. Moreover, the larger face-related brain responses recorded over the right hemisphere suggest that the right hemisphere of the brain has begun to preferentially respond to faces by six months of age. These findings also complement previous behavioral and ERP work suggesting that infants can distinguish between faces and objects in the first year of life (for a review, see Scott and Nelson, 2004).
Although this technique has been successfully used in infant studies of low-level vision (e.g., Braddick et al., 1986), de Heering and Rossion are among the first researchers to demonstrate the effectiveness of the FPVS technique using complex images in infant research. This is an important addition to the developmental scientist's toolbox and will greatly expand our ability to characterize brain development in infants even before they begin to talk. This technique has been successfully used in a variety of adult investigations but to our knowledge only one other published study reports results from this method with infants (Farzin et al., 2012).
The fact that the FPVS technique can be applied to infant populations has a number of benefits for researchers. Infants can be exposed to hundreds of trials and several conditions within minutes, and no verbal or motor response is required. This large amount of data, collected in a short period of time, results in a higher of proportion data suitable for analysis than in studies using behavior or standard ERP approaches. The increased number of trials also allows researchers to use a variety of visual stimuli that vary in shape, size and orientation, leading to conclusions that are more generalizeable and relevant to real-world situations. Relative to other methods, the FPVS method measures infant brain responses objectively, allowing for precise testing of predictions and easy comparisons across investigations. Finally, the FPVS method measures how the brain tells the difference between various stimulus conditions and provides a direct link between this response and the behavioral tasks commonly used to study infant perception, learning and memory.
Brain activity differentiates face and object processing in 6-month-old infantsDevelopmental Psychology 35:1113.https://doi.org/10.1037/0012-16126.96.36.1993
The fusiform face area: a module in human extrastriate cortex specialized for face perceptionJournal of Neuroscience 17:4302–4311.
Review of Psychiatry SeriesReview of Psychiatry Series, Volume 23, American Psychiatric Publishing.
Downloads (link to download the article as PDF)
Download citations (links to download the citations from this article in formats compatible with various reference manager tools)
Open citations (links to open the citations from this article in various online reference manager services)
Early life adversity (ELA) is associated with increased risk for stress-related disorders later in life. The link between ELA and risk for psychopathology is well established but the developmental mechanisms remain unclear. Using a mouse model of resource insecurity, limited bedding (LB), we tested the effects of LB on the development of fear learning and neuronal structures involved in emotional regulation, the medial prefrontal cortex (mPFC) and basolateral amygdala (BLA). LB delayed the ability of peri-weanling (21 days old) mice to express, but not form, an auditory conditioned fear memory. LB accelerated the developmental emergence of parvalbumin (PV)-positive cells in the BLA and increased anatomical connections between PL and BLA. Fear expression in LB mice was rescued through optogenetic inactivation of PV-positive cells in the BLA. The current results provide a model of transiently blunted emotional reactivity in early development, with latent fear-associated memories emerging later in adolescence.
Hippocampal firing is organized in theta sequences controlled by internal memory processes and by external sensory cues, but how these computations are coordinated is not fully understood. Although theta activity is commonly studied as a unique coherent oscillation, it is the result of complex interactions between different rhythm generators. Here, by separating hippocampal theta activity in three different current generators, we found epochs with variable theta frequency and phase coupling, suggesting flexible interactions between theta generators. We found that epochs of highly synchronized theta rhythmicity preferentially occurred during behavioral tasks requiring coordination between internal memory representations and incoming sensory information. In addition, we found that gamma oscillations were associated with specific theta generators and the strength of theta-gamma coupling predicted the synchronization between theta generators. We propose a mechanism for segregating or integrating hippocampal computations based on the flexible coordination of different theta frameworks to accommodate the cognitive needs.