Pupillometric decoding of high-level musical imagery.

Abstract

Humans report imagining sound where no physical sound is present: we replay conversations, practice speeches, and "hear" music all within the confines of our minds. Research has identified neural substrates underlying auditory imagery; yet deciphering its explicit contents has been elusive. Here we present a novel pupillometric method for decoding what individuals hear "inside their heads". Independent of light, pupils dilate and constrict in response to noradrenergic activity. Hence, stimuli evoking unique and reliable patterns of attention and arousal even when imagined should concurrently produce identifiable patterns of pupil-size dynamics (PSDs). Participants listened to and then silently imagined music while eye-tracked. Using machine learning algorithms, we decoded the imagined songs within- and across-participants following classifier-training on PSDs collected during both imagination and perception. Echoing findings in vision, cross-domain decoding accuracy increased with imagery strength. These data suggest that light-independent PSDs are a neural signature sensitive enough to decode imagination.