The advantage of flexible neuronal tunings in neural network models for motor learning

Human motor adaptation to novel environments is often modeled by a basis function network that transforms desired movement properties into estimated forces. This network employs a layer of nodes that have fixed broad tunings that generalize across the input domain. Learning is achieved by updating the weights of these nodes in response to training experience. This conventional model is unable to account for rapid flexibility observed in human spatial generalization during motor adaptation. However, added plasticity in the widths of the basis function tunings can achieve this flexibility, and several neurophysiological experiments have revealed flexibility in tunings of sensorimotor neurons. We found a model, Locally Weighted Projection Regression (LWPR), which uniquely possesses the structure of a basis function network in which both the weights and tuning widths of the nodes are updated incrementally during adaptation. We presented this LWPR model with training functions of different spatial complexities and monitored incremental updates to receptive field widths. An inverse pattern of dependence of receptive field adaptation on experienced error became evident, underlying both a relationship between generalization and complexity, and a unique behavior in which generalization always narrows after a sudden switch in environmental complexity. These results implicate a model that is flexible in both basis function widths and weights, like LWPR, as a viable alternative model for human motor adaptation that can account for previously observed plasticity in spatial generalization. This theory can be tested by using the behaviors observed in our experiments as novel hypotheses in human studies

Motor Adaptation Scaled by the Difficulty of a Secondary Cognitive Task

Background: Motor learning requires evaluating performance in previous movements and modifying future movements. The executive system, generally involved in planning and decision-making, could monitor and modify behavior in response to changes in task difficulty or performance. Here we aim to identify the quantitative cognitive contribution to responsive and adaptive control to identify possible overlap between cognitive and motor processes. Methodology/Principal Findings: We developed a dual-task experiment that varied the trial-by-trial difficulty of a secondary cognitive task while participants performed a motor adaptation task. Subjects performed a difficulty-graded semantic categorization task while making reaching movements that were occasionally subjected to force perturbations. We find that motor adaptation was specifically impaired on the most difficult to categorize trials. Conclusions/Significance: We suggest that the degree of decision-level difficulty of a particular categorization differentially burdens the executive system and subsequently results in a proportional degradation of adaptation. Our results suggest

Possible model of systems involved in within-movement and across-movement motor control.

<p>During the pulsed movement, movement errors engage the feedback control system to correct the movement online (solid route) and the errors engage cognitive control systems (dashed route). The word categorization task also engages cognitive control differentially depending on the category ambiguity of the word (gray route). Feedback error learning systems update the predictive feedforward control on the next movement (solid route). Cognitive control also influences predictive control on the next movement (dashed route), but is degraded depending on the category ambiguity of the presented word.</p