Learning to predict: Neuronal signatures of auditory expectancy in human event-related potentials.

ABSTRACT: Learning to anticipate future states of the world based on statistical regularities in the environment is a key component of perception and is vital for the survival of many organisms. Such statistical learning and prediction are crucial for acquiring language and music appreciation. Importantly, learned expectations can be implicitly derived from exposure to sensory input, without requiring explicit information regarding contingencies in the environment. Whereas many previous studies of statistical learning have demonstrated larger neuronal responses to unexpected versus expected stimuli, the neuronal bases of the expectations themselves remain poorly understood. Here we examined behavioral and neuronal signatures of learned expectancy via human scalp-recorded event-related brain potentia

SUBMITTER: Fishman YI 

PROVIDER: S-EPMC9215305 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature