Getting Off to a Shaky Start: Specificity in Planning and Feedforward Control During Sensorimotor Learning in Autism Spectrum Disorder.
Autistic adults need explicit practice on how much force to use before they can master new hand skills.
01Research in Context
What this study did
Green et al. (2020) asked 24 autistic adults and 24 non-autistic adults to learn a new motor task. The task was a video game where you push a robotic handle to move a cursor through a hidden maze.
Everyone practiced 150 times while the researchers tracked hand speed, force, and accuracy. The team wanted to see if autistic learners plan and adjust their movements differently.
What they found
Autistic adults did learn the maze, but their movements stayed shaky. They pushed too hard or too soft and hit the walls more often.
Even by the end, their force control was twice as variable as the non-autistic group. The data say the problem is in the planning stage, not just slow reaction time.
How this fits with other research
Gowen et al. (2022) found the same adults were also late at predicting when an action would restart after a brief pause. Both studies point to a timing issue in the feedforward system, so the results reinforce each other.
Chetcuti et al. (2019) showed that kids with autism imitate worse when the action has tricky force shifts. C et al. now show the weakness persists into adulthood, extending the timeline.
Pan (2014) saw large motor deficits in autistic teens on standard gym tests. The new lab data explain why: poor force planning, not simply low muscle strength.
Why it matters
When you teach a client to zip a coat, cut food, or use a stylus, add extra trials that focus only on how hard to push. Give clear verbal cues like “gentle” or “firm” and show a visual force meter if you have one. Reducing force variability first may unlock faster acquisition of the whole skill.
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02At a glance
03Original abstract
Whilst autistic individuals develop new internal action models during sensorimotor learning, the acquired movements are executed less accurately and with greater variability. Such movement profiles are related to differences in sensorimotor integration and/or altered feedforward/feedback sensorimotor control. We investigated the processes underlying sensorimotor learning in autism by quantifying accuracy and variability, relative timing, and feedforward and feedback control. Although autistic individuals demonstrated significant sensorimotor learning across trials, which was facilitated by processing knowledge-of-results feedback, motor execution was less accurate than non-autistic individuals. Kinematic analysis indicated that autistic individuals showed significantly greater spatial variability at peak acceleration, but comparable spatial variability at peak velocity. These kinematic markers suggest that autistic movement profiles are driven by specific differences in sensorimotor control processes (i.e., internal action models) associated with planning and regulating the forces required to execute the movement. The reduction of variability at peak velocity indicates intact early feedback-based sensorimotor control in autism. Understanding how feedforward and feedback-based control processes operate provides an opportunity to explore how these control processes influence the acquisition of socio-motor actions in autism. Autism Res 2020, 13: 423-435. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Autistic adults successfully learned a new movement skill by physically practising it, and using feedback about how well they had done to become more accurate. When looking at the movements in detail, autistic adults were more variable than non-autistic adults when planning (e.g., how much force to use), and performing, the movement. These differences impact how autistic individuals learn different types of movement skills, which might influence how other behaviours (e.g., imitation) are acquired that support social interaction.
Autism research : official journal of the International Society for Autism Research, 2020 · doi:10.1002/aur.2214