Separate and shared low-dimensional neural architectures for error-based and reinforcement motor learning
Areshenkoff, C. N.; de Brouwer, A. J.; Gale, D. J.; Nashed, J. Y.; Gallivan, J.
Show abstract
AO_SCPLOWBSTRACTC_SCPLOWMotor learning is supported by multiple systems adapted to processing different forms of sensory information (e.g., reward versus error feedback), and by higher-order systems supporting strategic processes. Yet, the extent to which these systems recruit shared versus separate neural pathways is poorly understood. To elucidate these pathways, we separately studied error-based (EL) and reinforcement-based (RL) motor learning in two functional MRI experiments in the same human subjects. We find that EL and RL occupy opposite ends of neural axis broadly separating cerebellar and striatal connectivity, respectively, with somatomotor cortex, and that alignment of this axis to each task is related to performance. Further, we identify a separate neural axis that is associated with strategy use during EL, and show that the expression of this same axis during RL predicts better performance. Together, these results offer a macroscale view of the common versus distinct neural architectures supporting different learning systems.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Cortical changes during the learning of sequences of simultaneous finger presses 98%
- Encoding neural representations of time-continuous stimulus-response transformations in the human brain with advanced deep neural networks 97%
- Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control 97%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.