The impact of spatial variance on precision estimates in an auditory oddball paradigm

Abstract

Predictive processing theories suggest that a principal function of the brain is to reduce the surprise of incoming sensory information by creating accurate and precise models of the environment. These models are commonly explored by looking at the prediction errors elicited when experience departs from predictions. One such prediction error is the mismatch negativity (MMN). Using this component, it is possible to examine the effect of external noise on the precision of the developed model. Recent studies have shown that the brain may not update its model every time there is a change in the environment, rather it will only update it when doing so will increase precision and or accuracy of the model. The current study examined this process using oddball sound sequences with high and low spatial variability and examining how this affected the elicited MMN to a duration deviant sound. The results showed a strong null effect of spatial variance both at a local and sequence levels. These results indicate that variability in the sound sequence will not invariably affect model precision estimates and thus the amplitude of the MMN component.

Keywords: Duration deviants; MMN; Spatial variability; Statistical learning.