Pre-attentive representation of prediction certainty in autism: A mismatch negativity (MMN) study

Abstract

According to predictive processing theories of perception, the brain generates predictions to prepare for sensory input, and calibrates certainty of predictions based on their likelihood. When an input doesn't match the prediction, an error signal leads to updating of the predictive model. Prior research suggests altered prediction certainty in autism, but predictive processing occurs across the cortical hierarchy, and the stage(s) of processing where prediction certainty breaks down is unknown. We therefore tested the integrity of prediction certainty in autism at pre-attentive and relatively automatic processing stages using the pre-attentive Mismatch Negativity (MMN) brain response. The MMN occurs in response to a "deviant" presented in a stream of "standards" and is measured while the participant performs an orthogonal task. Most critically, MMN amplitude typically varies with the level of certainty associated with the prediction. We recorded high-density EEG while presenting adolescents and young adults with and without autism with repetitive tones every half second (the standard) interspersed with infrequent pitch and inter-stimulus-interval (ISI) deviants. Pitch and ISI deviant probabilities were manipulated at 4, 8, or 16% within a block of trials to test whether MMN amplitude varied in a typical manner with respect to probability. For both groups, Pitch-MMN amplitude increased as the probability of deviance decreased. Unexpectedly, ISI-MMN amplitude did not reliably vary by probability in either group. Our Pitch-MMN findings suggest intact neural representation of pre-attentive prediction certainty in autism, addressing a critical knowledge gap in autism research. The implications of these findings are considered.

Predictive Coding: A more cognitive process than we thought?

“…To summarize, disrupted encoding of global and local oddballs speaks to the utility of this paradigm to uncover neural computations and interactions that are relevant for diseased states. The relationship between P300/MMN and cognitive functioning in disorders 17,50,[101][102][103][113][114][115][116] as well as imbalanced beta/gamma oscillatory states 107,113 in psychopathy, ASD, depression, and ScZ points to the importance of understanding these phenomena at a neuronal level and indicates the possibility of using neural data as biomarkers. With further development, it could be possible to design specific medications and treatments to specifically alter these biomarkers, helping to treat these disorders.…”