Autistic children showed earlier and less age-related refinement in the brain signals involved in processing faces.
A child looks at a face, and within milliseconds the brain begins sorting its features, expression, and identity. Researchers have now traced that process across the entire scalp in one of the most comprehensive studies of face perception in autism, recently published in Nature Mental Health.
Autistic people often make less eye contact or avoid looking directly at other people’s faces. Many also find it more difficult than neurotypical people to remember faces or identify facial emotions. Scientists have examined these differences for decades. In the early 2000s, for example, Yale-led research showed that brain regions involved in processing faces were less active in autistic people.
James McPartland, PhD, Harris Professor in the Child Study Center and director of the Center for Brain and Mind Health, studies these responses with electroencephalography (EEG), which records electrical activity in the brain. Most previous EEG research, however, has examined only selected regions rather than activity across the entire scalp.
In the new study, researchers led by McPartland collected measurements from approximately 400 autistic children to build a broader picture of face processing. The face-related signals were less distinct in autistic participants. In neurotypical children, those signals also became increasingly refined with age, but the same developmental pattern did not appear in autistic children.
“This is the first time we’ve leveraged the full volume of very rich information provided by an EEG recording to understand face perception and autism,” McPartland says.
Whole scalp signals reveal broader differences
McPartland compares earlier research to estimating traffic across an entire city with a camera positioned on only one downtown road. “That’s the way most research on face processing so far has worked,” McPartland says.
The new approach was more like viewing traffic across the whole city from a satellite. Using data from the Autism Biomarkers Consortium for Clinical Trials (ABC-CT), the researchers examined autistic and neurotypical children across a range of ages. EEG recordings captured their brain activity as they viewed pictures of faces and objects.
Instead of concentrating on a small set of locations, the researchers analyzed electrical signals from all 128 electrodes placed around the scalp. They then used machine learning to determine whether patterns in the brain activity could predict what each participant was viewing.
The system could identify when a neurotypical child was looking at a face more easily than when an autistic child was. “The neural activity that we measure at the scalp is just not as distinct in autistic children,” says first author Jason Griffin, PhD, assistant professor of psychology at University of Houston. Griffin was formerly a postdoctoral researcher in McPartland’s lab.
Among neurotypical children, the researchers became better able to predict what participants were viewing as age increased. They found no comparable age-related change in the brain activity of autistic children. “Their face-specific processing is not following the same trajectory,” Griffin says.
Face processing differences begin earlier
The results also change scientists’ understanding of when differences in autistic face processing first emerge. The process begins when the eyes receive visual information and continues until the brain recognizes whose face it is seeing.
Much of the previous research has centered on a biomarker called the N170, a change in brain activity that occurs about 170 milliseconds after someone sees a face. This established marker of face perception is delayed in autistic people.
“But when you look at face processing through an approach that’s more inclusive of the full temporal range of information, we’re seeing that differences in autism occur even earlier,” McPartland says.
Earlier support may matter most
Clinicians once tried to address face-processing differences by insisting that autistic patients make eye contact, an approach researchers now understand does not work. Identifying the underlying developmental patterns in the brain could provide more useful guidance for supporting autistic people.
A persistent challenge in autism research is separating biological features of the condition from changes shaped by the experience of living with autism. The new findings suggest that the observed differences in face processing reflect autism itself rather than those later experiences.
“When we’re seeing this expected refinement and specialization in non-autistic people, we’re seeing a reduced developmental change in autistic people,” McPartland explains.
That pattern suggests that support intended to help autistic children interpret facial emotions might be more useful earlier in childhood, when these developmental differences first appear.
Many neurodivergent people are not limited by autism and may neither need nor want intervention. For others, however, difficulty interpreting faces can affect everyday life.
“Imagine navigating life when it’s hard for you to perceive the information in another person’s face, from navigating a playground to navigating a job interview,” McPartland says. “This is one of the critical areas that causes difficulty for many autistic people.”
A potential biomarker for autism
Clinicians currently diagnose autism through behavior because no biologically based assessment tools are available. Researchers are evaluating several biomarkers that could contribute to improved diagnostic methods or help define different subgroups of autistic people.
A clearer understanding of face-processing differences might support the development of new diagnostic tools. It could also help researchers identify who may be more likely to benefit from interventions such as social skills therapy.
“This is a first step in introducing a new biomarker,” McPartland says.
Reference: “Decoding the temporal dynamics of face-specific neural representations in autism” by Jason W. Griffin, Alan H. Gerber, Kaylee Litson, Susan Faja, Shafali Jeste, Natalia Kleinhans, Geraldine Dawson, Adam Naples, April R. Levin, Sara Jane Webb, Frederick Shic, Catherine Sugar, James Dziura, James C. McPartland and for the Autism Biomarkers Consortium for Clinical Trials, 29 June 2026, Nature Mental Health.
DOI: 10.1038/s44220-026-00672-y
Supported by the National Institutes of Health (awards 40MH134303, U19MH108206, and K01MH137401) and Yale University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Additional funding was provided by the Hilibrand Foundation.
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