Automatic and feature-specific (anticipatory) prediction-related neural activity in the human auditory system

Abstract

Prior experience shapes sensory perception by enabling the formation of expectations with regards to the occurrence of upcoming sensory events. Especially in the visual modality, an increasing number of studies show that prediction-related neural signals carry feature-specific information about the stimulus. This is less established in the auditory modality, in particular without bottom-up signals driving neural activity. We studied whether auditory predictions are sharply tuned to even carry tonotopic specific information. For this purpose, we conducted a Magnetoencephalography (MEG) experiment in which participants passively listened to sound sequences that varied in their regularity (i.e. entropy). Sound presentations were temporally predictable (3 Hz rate), but were occasionally omitted. Training classifiers on the random (high entropy) sound sequence and applying them to all conditions in a time-generalized manner, allowed us to assess whether and how carrier frequency specific information in the MEG signal is modulated according to the entropy level. We show that especially in an ordered (most predictable) sensory context neural activity during the anticipatory and omission periods contains carrier-frequency specific information. Overall our results illustrate in the human auditory system that prediction-related neural activity can be tuned in a tonotopically specific manner.