Revisiting the Bayesian Brain: predictive processes of facial emotion recognition in autism

Background: Autism spectrum disorder (ASD) affects social interaction, communication and behavioural flexibility. Recent theories grounded in the Bayesian Brain Framework propose that autistic individuals process environmental uncertainty differently, overweighting volatility when updating predictions about the world. However, the robustness of these predictive processing differences and their relevance to core symptoms remains unclear. Methods: We attempted to replicate findings from Lawson and colleagues (1), who showed a tendency to overestimate environmental volatility in autistic adults based on belief states extracted using a Hierarchical Gaussian Filter. We extended their paradigm to a domain central to the autistic experience: rather than distinguishing houses from faces, participants detected valence in emotional facial expressions. Preregistered hypotheses were evaluated using Bayesian linear mixed models.Results: Despite using an identical computational model to extract belief states, we did not find robust differences between autistic and non-autistic adults, though autistic participants showed a non-credible trend towards increased processing of phasic volatility. Largely comparable probabilistic associative learning was also reflected in response times and pupil sizes. Limitations: While participants reacted faster to expected than unexpected trials in the beginning, this effect decreased over the course of the experiment; thus, possibly indicating that the individual blocks of stable cue-outcome associations were too short.Conclusions:These results challenge the robustness and symptom-specificity of previous reported volatility processing differences in autism, suggesting that altered Bayesian inference may not generalize to socially relevant prediction tasks that involve core autistic difficulties.