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The earliest stages of sensorimotor learning involve learning the correspondence between movements and sensory results-a sensorimotor map. The present exploratory study investigated the neurochemical underpinnings of map acquisition by monitoring 25 participants as they acquired a new association between movements and sounds. Functional magnetic resonance spectroscopy was used to measure neurochemical concentrations in the left primary motor cortex during learning. Resting-state functional magnetic resonance imaging data were also collected before and after training to assess learning-related changes in functional connectivity. There were monotonic increases in γ-aminobutyric acid (GABA) and decreases in glucose during training, which extended into the subsequent rest period and, importantly, in the case of GABA correlated with the amount of learning: participants who showed greater behavioral learning showed greater GABA increase. The GABA change was furthermore correlated with changes in functional connectivity between the primary motor cortex and a cluster of voxels in the right intraparietal sulcus: greater increases in GABA were associated with greater strengthening of connectivity. Transiently, there were increases in lactate and reductions in aspartate, which returned to baseline at the end of training, but only lactate showed a statistical trend to correlate with the amount of learning. In summary, during the earliest stages of sensorimotor learning, GABA levels are linked on a subject-level basis to both behavioral learning and a strengthening of functional connections that persists beyond the training period. The findings are consistent with the idea that GABA-mediated inhibition is linked to maintenance of newly learned information. NEW & NOTEWORTHY Learning the mapping between movements and their sensory effects is a necessary step in the early stages of sensorimotor learning. There is evidence showing which brain areas are involved in early motor learning, but their role remains uncertain. Here, we show that GABA, a neurotransmitter linked to inhibitory processing, rises during and after learning and is involved in ongoing changes in resting-state networks.

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.

Recent studies using visuomotor adaptation and sequence learning tasks have assessed the involvement of working memory in the visuospatial domain. The capacity to maintain previously performed movements in working memory is perhaps even more important in reinforcement-based learning to repeat accurate movements and avoid mistakes. Using this kind of task in the present work, we tested the relationship between somatosensory working memory and motor learning. The first experiment involved separate memory and motor learning tasks. In the memory task, the participant's arm was displaced in different directions by a robotic arm, and the participant was asked to judge whether a subsequent test direction was one of the previously presented directions. In the motor learning task, participants made reaching movements to a hidden visual target and were provided with positive feedback as reinforcement when the movement ended in the target zone. It was found that participants that had better somatosensory working memory showed greater motor learning. In a second experiment, we designed a new task in which learning and working memory trials were interleaved, allowing us to study participants' memory for movements they performed as part of learning. As in the first experiment, we found that participants with better somatosensory working memory also learned more. Moreover, memory performance for successful movements was better than for movements that failed to reach the target. These results suggest that somatosensory working memory is involved in reinforcement motor learning and that this memory preferentially keeps track of reinforced movements. NEW & NOTEWORTHY The present work examined somatosensory working memory in reinforcement-based motor learning. Working memory performance was reliably correlated with the extent of learning. With the use of a paradigm in which learning and memory trials were interleaved, memory was assessed for movements performed during learning. Movements that received positive feedback were better remembered than movements that did not. Thus working memory does not track all movements equally but is biased to retain movements that were rewarded.