Predictive Processing in Autism Spectrum Disorder: The Atypical Iterative Prior Updating Account

The nature of predictive-processing differences between individuals with autism spectrum disorder (ASD) and typically developing (TD) individuals remains contested. Some studies have reported impaired predictive processing in ASD, while others have suggested intact but atypical learning dynamics.

We investigated duration reproduction tasks under high- and low-volatility settings to examine the updating dynamics of prior beliefs and sensory estimate updating in individuals with ASD (n = 32) and TD counterparts (n = 32). Using a two-state Bayesian model, we analyzed how the participants updated their prior beliefs and perceptual estimates and how these updates affected their behavior over time.

Individuals with ASD integrated prior knowledge similarly to TD control participants for perceptual estimates. However, they relied more heavily on sensory input for iteratively updating their prior beliefs, perceiving events as less interconnected. This heightened reliance on sensory inputs led to the initial underweighting of priors in perceptual estimates, resulting in a weaker central tendency early in sessions. Over time, ASD participants adapted, reaching integration weights comparable to those of TD control participants by the end of the session. These findings suggest that predictive processing in ASD is characterized by distinct updating dynamics, not an inability to form or use prior effectively.

Our study highlights a unique interplay between sensory inputs and prior beliefs in ASD, where greater reliance on sensory inputs during prior updating influences adaptation speed and intertrial dynamics. This process clarifies inconsistencies in the literature and underscores the role of interactive updating in predictive processing differences between individuals with ASD and TD individuals.