Special Treatment of Prediction Errors in Autism Spectrum Disorder, 2018-2021

Todorova, Greta K. and Pollick, Frank E. and Muckli, Lars (2021). Special Treatment of Prediction Errors in Autism Spectrum Disorder, 2018-2021. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-854905

Autism Spectrum Disorder (ASD) is a developmental condition with recognised impact on social functioning. Dysfunctional processing of sensory information has recently been acknowledged as a key diagnostic criterion (APA, 2013). Hence, understanding how sensory information is processed in autism is a crucial part of understanding and improving the way we treat it. The larger project associated with this dataset aims to test an important new approach for modelling sensory and cognitive processing in autism (Van de Cruys et al., 2014). This approach uses a predictive coding framework to examine how mismatches between expectations and internally generated models of the world can lead to an abundance of error signals in the brains of individuals with ASD. These error signals lead to differences in processing that have the potential for creating anxiety and difficulty in perceiving social signals. To test this framework in ASD we use behavioural and brain imaging experiments that involve observing object and human (biological) motion. This enabled us to investigate the processing of sensory information with and without a social component.

Data description (abstract)

According to HIPPEA (High, Inflexible Precision of Prediction Errors in Autism), in autism, neural processes are putting inflexibly high precision on prediction errors, irrespective of context. We used an apparent motion paradigm to test this prediction. This dataset contains the anonymised data from 11 autistic and 9 non-autistic participants, who took part in an apparent motion paradigm similar to the one presented by Sanders et al., 2012) to participants with Schizophrenia. The participants had to detect a flashing stimulus, that appeared either in-time (predictable) or out-of-time (unpredictable) with the apparent motion. We observed that 66% (6/9) neurotypical and (64%) 7/11 autistic participants were better at detecting predictable targets. Thus, autistic participants were likely able to use the apparent motion to establish a predictive model of the stimuli, benefiting their ability to detect the predictable over the unpredictable target. Additionally, 55% (6/11) of autistic participants had faster responses for unpredictable targets, whereas only 22% (2/9) neurotypicals had faster responses to unpredictable compared to predictable targets. Thus, it appears that for autistic participants unpredictable events are given special treatment in the brain, even if those targets are not detected more often. This data does not fulfill the power calculations for a robust effect, thus all of the data is being shared including experiment and data analysis pre-processing and final analysis scripts. Moreover, a pre-registered analysis is available for researchers to finish off the results of this study - https://osf.io/729cr

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