Structural and contextual priors affect visual search in children with and without autism

Area of Science:

• Cognitive Science
• Developmental Psychology
• Neuroscience

Background:

• Bayesian predictive coding theories link autism spectrum disorder (ASD) to altered probability distribution learning.
• Understanding how children, especially those with autism, learn and encode probability distributions is crucial but limited.

Purpose of the Study:

• To investigate how children with and without autism learn probability distributions using a novel paradigm.
• To characterize the precision of probability distribution encoding in children with and without ASD.

Main Methods:

• A distribution learning paradigm involving visual search for an odd-one-out target among distractors.
• Participants (24 autistic, 25 neurotypical children) learned parameters (mean, variance) of a Gaussian distribution over trials.
• Target-distractor role-reversal assessed the width (variance) of the encoded distribution.

Main Results:

• Both autistic and neurotypical children performed similarly on the visual search task.
• Groups learned the distractor distribution to a comparable extent.
• Both groups exhibited a broader learned variance than the presented distribution, indicating less informative priors.

Conclusions:

• Children with and without autism learn probability distributions similarly.
• Children, irrespective of autism diagnosis, show broader variance (less precise priors) compared to adults.
• Findings inform Bayesian accounts of perception in development and autism.