Neural prediction of higher-order auditory sequence statistics

During auditory perception, we are required to abstract information from complex temporal sequences suchas those in music and speech. Here, we investigated how higher-order statistics modulate the neuralresponses to sound sequences, hypothesizing that these modulations are associated with higher levels of theperi-Sylvian auditory hierarchy. We devised second-order Markov sequences of pure tones with uniformfirstordertransition probabilities. Participants learned to discriminate these sequences from random ones.Magnetoencephalography was used to identify evoked fields in which second-order transition probabilitieswere encoded. We show that improbable tones evoked heightened neural responses after 200 ms post-toneonset during exposure at the learning stage or around 150 ms during the subsequent test stage, originatingnear the right temporoparietal junction. These signal changes reflected higher-order statistical learning,which can contribute to the perception of natural sounds with hierarchical structures. We propose that ourresults reflect hierarchical predictive representations, which can contribute to the experiences of speech andmusic.