Faster model updating in autism during early sensory processing

Background: Recent theories of autism propose that a core deficit in autism would be a less context-sensitive weighting of prediction errors. There is also first support for this hypothesis on an early sensory level. However, an open question is whether this decreased context-sensitivity is caused by faster updating of one9s model of the world (i.e. higher weighting of new information), proposed by predictive coding theories, or slower model updating. Here, we differentiated between these two hypotheses by investigating how first impressions shape the mismatch negativity (MMN), reflecting early sensory prediction error processing. Methods: An autism and matched control group (both n=27) were compared on the multi-timescale MMN paradigm, in which tones were presented that were either standard (frequently occurring) or deviant (rare), and these roles reversed every block. A well-replicated observation is that the initial model (i.e. the standard and deviant sound in the first block) influences MMN amplitudes in later blocks. If autism is characterized by faster model updating, we hypothesized that their MMN amplitudes would be less influenced by the initial context. Results: We found that MMN responses in the autism group did not differ between the initial deviant and initial standard sounds as they did in the control group. Conclusions: These results show that individuals with autism are less influenced by initial contexts, confirming that autism is characterized by faster updating of sensory models, as proposed by predictive coding accounts of autism.