Computational cognitive theories of Autism Spectrum Disorder have received renewed attention in recent years. Consistent with the predictive processing framework, ASD has been re-conceptualized as a disorder of aberrant prediction and learning-rate estimation involving multiple levels of a putative cognitive computational hierarchy. Specifically, behavioral symptoms of individuals with ASD might manifest due to an aberrant overestimation of the volatility of environmental contingencies (i.e. tendency of change in cue-outcome probabilities) which in turn might induce a dysfunctional setting of learning rates. In this work, we attempted to conceptually replicate computational modeling analyses of an impactful study of the recent ASD modeling literature in an independent sample of subjects. We were not able to replicate some prior reported effects likely due to differences in model architecture and cognitive task setup. We found statistical trends in similar directions.