Statistical Learning is Associated with Autism Symptoms and Verbal Abilities in Young Children with Autism.
Statistical learning ability on a five-minute tablet game predicts autism symptom level and tells you how fast a child can pick up new skills.
01Research in Context
What this study did
The team gave a short tablet game to two groups of kids. One group had autism, the other group was typically developing.
The game secretly repeated picture patterns. The kids had to pick the next picture. No one told them the patterns existed.
What they found
Most kids with autism picked up fewer patterns than their peers. Yet some learned just as well.
The kids who learned well also had milder autism symptoms and stronger spoken language.
How this fits with other research
Schuwerk et al. (2016) first showed this pattern gap on an action task. The new study proves the gap also shows up in simple picture games.
Lu et al. (2019) found kids with autism learn rules fine against a computer, but slow down when they think a person is watching. Together the papers say the learning engine is intact, yet social load or symptom severity can hide it.
McAuliffe et al. (2020) saw the same scatter of learning speeds when kids copied hand gestures. The three studies line up: learning curves vary widely in autism, and the scatter predicts symptom level across very different tasks.
Why it matters
You can spot who needs extra help by running a quick, game-like probe before therapy starts. If a child picks up the hidden pictures easily, you can move faster through new programs. If the child misses most patterns, break skills into smaller steps and use clear cues instead of hoping implicit learning will kick in.
Want CEUs on This Topic?
The ABA Clubhouse has 60+ free CEUs — live every Wednesday. Ethics, supervision & clinical topics.
Join Free →Open a free pattern-sequence app, run ten trials, note hits and misses, then adjust lesson size and prompting level based on the score.
02At a glance
03Original abstract
Statistical learning-extracting regularities in the environment-may underlie complex social behavior. 124 children, 56 with autism and 68 typically developing, ages 2-8 years, completed a novel visual statistical learning task on an iPad. Averaged together, children with autism demonstrated less learning on the task compared to typically developing children. However, multivariate classification analyses characterized individual behavior patterns, and demonstrated a subset of children with autism had similar learning patterns to typically developing children and that subset of children had less severe autism symptoms. Therefore, statistically averaging data resulted in missing critical heterogeneity. Variability in statistical learning may help to understand differences in autism symptoms across individuals and could be used to tailor and inform treatment decisions.
Journal of autism and developmental disorders, 2018 · doi:10.1007/s10803-018-3625-7