One of the puzzles of learning to talk or play a musical instrument is how we learn which movement produces a particular sound: an audiomotor map. The initial stages of map acquisition can be studied by having participants learn arm movements to auditory targets. The key question is what mechanism drives this early learning. Three learning processes from previous literature were tested: map learning may rely on active motor outflow (target), on error correction, and on the correspondence between sensory and motor distances (i.e., that similar movements map to similar sounds). Alternatively, we hypothesized that map learning can proceed without these. Participants made movements that were mapped to sounds in a number of different conditions that each precluded one of the potential learning processes. We tested whether map learning relies on assumptions about topological continuity by exposing participants to a permuted map that did not preserve distances in auditory and motor space. Further groups were tested who passively experienced the targets, kinematic trajectories produced by a robot arm, and auditory feedback as a yoked active participant (hence without active motor outflow). Another group made movements without receiving targets (thus without experiencing errors). In each case we observed substantial learning, therefore none of the three hypothesized processes is required for learning. Instead early map acquisition can occur with free exploration without target error correction, is based on sensory-to-sensory correspondences, and possible even for discontinuous maps. The findings are consistent with the idea that early sensorimotor map formation can involve instance-specific learning. NEW & NOTEWORTHY This study tested learning of novel sensorimotor maps in a variety of unusual circumstances, including learning a mapping that was permuted in such as way that it fragmented the sensorimotor workspace into discontinuous parts, thus not preserving sensory and motor topology. Participants could learn this mapping, and they could learn without motor outflow or targets. These results point to a robust learning mechanism building on individual instances, inspired from machine learning literature.
Unsupervised Temporospatial Neural Architecture for Sensorimotor Map Learning
