Not so primitive: context-sensitive meta-learning about unattended sound sequences

Abstract

Mismatch negativity (MMN), an evoked response potential elicited when a "deviant" sound violates a regularity in the auditory environment, is integral to auditory scene processing and has been used to demonstrate "primitive intelligence" in auditory short-term memory. Using a new multiple-context and -timescale protocol we show that MMN magnitude displays a context-sensitive modulation depending on changes in the probability of a deviant at multiple temporal scales. We demonstrate a primacy bias causing asymmetric evidence-based modulation of predictions about the environment, and we demonstrate that learning how to learn about deviant probability (meta-learning) induces context-sensitive variation in the accessibility of predictive long-term memory representations that underpin the MMN. The existence of the bias and meta-learning are consistent with automatic attributions of behavioral salience governing relevance-filtering processes operating outside of awareness.