A new study out of 91亚色 shows autistic individuals more likely to use both hands

TORONTO, Jan. 19, 2026 鈥� Most people take for granted which hand they use to reach for a cup of coffee or a puzzle piece. However, a new study out of 91亚色 suggests that for autistic individuals, which hand they use for various tasks is highly variable, which points to profound differences in the brain.

The research, published today in the journal Autism Research, found that even autistic adults who are right-handed demonstrate a reduced specialization of hand use and more distinctive movement patterns when compared to non-autistic peers.

鈥淗andedness is one of the most visible markers of how our brain's left and right hemispheres specialize for different tasks,鈥� says 91亚色 Associate Professor , who conducted the study with lead author and Master鈥檚 student Emily Fewster. 鈥淚n the neurotypical population, about 90 per cent of people show a strong right-hand dominance, reflecting the left hemisphere鈥檚 specialization for fine motor skills. Our study shows that in autism, this specialization is less pronounced, leading to a unique and highly individualized motor signature.鈥�

The LEGO Building Task

To observe these behaviours in a real-world context, researchers asked 54 right-handed adults, half with an autism diagnosis, to recreate complex LEGO models. Unlike traditional questionnaires that ask which hand someone uses to write, this naturalistic task allowed researchers to track thousands of dynamic movements in 3D space.

By analyzing how people actually move during the LEGO building task, researchers found that the right-handedness of autistic participants鈥� function quite differently than that of the non-autistic participants. Despite both groups identifying as right-handed, the autistic participants used their right hand much less often for grasping and did not show the typical dominant preference for using their right hand when reaching across their body.

The autistic participants also tended to shrink their workspace by focusing on blocks placed closer to them, suggesting a more cautious or individualized strategy for managing the space around them. In addition, their movements followed highly unique, idiosyncratic paths. While non-autistic participants tended to follow a similar sequence of actions, each autistic participant moved in a distinct, more variable way.

Together, these findings suggest that the autistic brain organizes movement in a less specialized, more variable manner than previously understood.

Implications for Earlier Identification

While the study focuses on brain organization, these "motor signatures" have significant clinical potential. Because motor skills often emerge in infancy, long before the complex communication skills typically used to diagnose autism, identifying these subtle motor differences could open a window for much earlier support.

鈥淪tandard questionnaires often miss these nuances because they don鈥檛 capture the dynamic nature of real-life movement,鈥� says Freud. 鈥淏y looking at how people actually move in a natural setting, we can identify objective markers that might eventually help us provide more tailored support strategies much earlier in development.鈥�

The researchers suggest that this "noisy" or variable motor processing supports the theory that autism involves broader, less specialized neural representations across the brain.

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TORONTO, May 5, 2025 鈥� Getting a timely diagnosis of autism spectrum disorder is a major challenge, but new research out of 91亚色 shows that how young adults, and potentially children, grasp objects could offer a simpler way to diagnose someone on the autism spectrum.

The team, part of an international collaboration, used machine learning to analyze naturalistic hand movements 鈥� specifically, finger motions during grasping 鈥� in autistic and non-autistic individuals.

鈥淥ur models were able to classify autism with approximately 85 per cent accuracy, suggesting this approach could potentially offer simpler, scalable tools for diagnosis,鈥� says lead author, Associate Professor of 91亚色鈥檚 Department of Psychology and the Centre for Vision Research.

鈥淎utism currently affects about one in 50 Canadian children, and timely, accessible diagnosis remains a major challenge. Our findings add to the growing body of research suggesting that subtle motor patterns may provide valuable diagnostic signals 鈥� something not yet widely leveraged in clinical practice.鈥�

In addition to social and communication challenges, autism, a neurodevelopmental disorder, can include motor abnormalities which often show up in early childhood. The researchers say testing for these motor movements early could lead to faster diagnoses and intervention.

鈥淭he main behaviours markers for diagnosis are focused on those with relatively late onset and the motor markers that can be captured very early in childhood may thus lower age of diagnosis,鈥� says Professor of the University of Haifa, an expert in autism research and a key collaborator in this study.

Autistic and non-autistic young adult participants were asked to use their thumbs and index fingers, which had tracking markers attached, to grasp different blocks of varying size, lift each one and replace it in the same spot, and put their hand back in the starting position. The researchers used machine learning to analyze the participants鈥� finger movements as they made grasping motions.

Both groups of participants had normal IQ and were matched on age and intelligence. Young adults were used instead of children to rule out any differences in the findings due to delayed development.

The research found that subtle motor control differences can be captured effectively with more than 84 per cent accuracy. The study also showed there were distinct kinematic properties in the grasping movements between autistic and non-autistic participants.

Analysis of naturalistic precision grasping tasks has not typically been used in previous studies, says Freud. Machine learning, however, provides researchers with a powerful new tool to analyze motor patterns, opening new ways to use movement data in the assessment of autism spectrum disorder.

The findings, says Freud, could lead to the development of more accessible and reliable diagnostic tools as well as timely intervention and support that could improve outcomes for autistic individuals in the future.

The paper, , was published today in the journal Autism Research.

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