1. Hillary Hadley   Is a corresponding author
  2. Lisa Scott   Is a corresponding author
  1. University of Massachusetts Amherst, United States

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.

References

    1. Kanwisher N
    2. McDermott J
    3. Chun MM
    (1997)
    The fusiform face area: a module in human extrastriate cortex specialized for face perception
    Journal of Neuroscience 17:4302–4311.
    1. Scott LS
    2. Nelson CA
    (2004)
    Review of Psychiatry Series
    Review of Psychiatry Series, Volume 23, American Psychiatric Publishing.

Article and author information

Author details

  1. Hillary Hadley

    Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, United States
    For correspondence
    hhadley@psych.umass.edu
    Competing interests
    The authors declare that no competing interests exist.
  2. Lisa Scott

    Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, United States
    For correspondence
    lscott@psych.umass.edu
    Competing interests
    The authors declare that no competing interests exist.

Publication history

  1. Version of Record published: June 2, 2015 (version 1)

Copyright

© 2015, Hadley and Scott

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Hillary Hadley
  2. Lisa Scott
(2015)
Face Recognition: Babies get it right
eLife 4:e08232.
https://doi.org/10.7554/eLife.08232

Further reading

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    Research Article Updated

    Neural circuit formation and function require that diverse neurons are specified in appropriate numbers. Known strategies for controlling neuronal numbers involve regulating either cell proliferation or survival. We used the Drosophila visual system to probe how neuronal numbers are set. Photoreceptors from the eye-disc induce their target field, the lamina, such that for every unit eye there is a corresponding lamina unit (column). Although each column initially contains ~6 post-mitotic lamina precursors, only 5 differentiate into neurons, called L1-L5; the ‘extra’ precursor, which is invariantly positioned above the L5 neuron in each column, undergoes apoptosis. Here, we showed that a glial population called the outer chiasm giant glia (xgO), which resides below the lamina, secretes multiple ligands to induce L5 differentiation in response to epidermal growth factor (EGF) from photoreceptors. By forcing neuronal differentiation in the lamina, we uncovered that though fated to die, the ‘extra’ precursor is specified as an L5. Therefore, two precursors are specified as L5s but only one differentiates during normal development. We found that the row of precursors nearest to xgO differentiate into L5s and, in turn, antagonise differentiation signalling to prevent the ‘extra’ precursors from differentiating, resulting in their death. Thus, an intricate interplay of glial signals and feedback from differentiating neurons defines an invariant and stereotyped pattern of neuronal differentiation and programmed cell death to ensure that lamina columns each contain exactly one L5 neuron.