Identifying musical pieces from fMRI data using encoding and decoding models.

ABSTRACT: Encoding models can reveal and decode neural representations in the visual and semantic domains. However, a thorough understanding of how distributed information in auditory cortices and temporal evolution of music contribute to model performance is still lacking in the musical domain. We measured fMRI responses during naturalistic music listening and constructed a two-stage approach that first mapped musical features in auditory cortices and then decoded novel musical pieces. We then probed the influence of stimuli duration (number of time points) and spatial extent (number of voxels) on decoding accuracy. Our approach revealed a linear increase in accuracy with duration and a point of optimal model performance for the spatial extent. We further showed that Shannon entropy is a driving fa

SUBMITTER: Hoefle S 

PROVIDER: S-EPMC5797093 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature