Rapid reshaping of human motor generalization

Abstract

People routinely learn how to manipulate new tools or make new movements. This learning requires the transformation of sensed movement error into updates of predictive neural control. Here, we demonstrate that the richness of motor training determines not only what we learn but how we learn. Human subjects made reaching movements while holding a robotic arm whose perturbing forces changed directions at the same rate, twice as fast, or four times as fast as the direction of movement, therefore exposing subjects to environments of increasing complexity across movement space. Subjects learned all three environments and learned the low- and medium-complexity environments equally well. We found that subjects lessened their movement-by-movement adaptation and narrowed the spatial extent of generalization to match the environmental complexity. This result demonstrated that people can rapidly reshape the transformation of sense into motor prediction to best learn a new movement task. We then modeled this adaptation using a neural network and found that, to mimic human behavior, the modeled neuronal tuning of movement space needed to narrow and reduce gain with increased environmental complexity. Prominent theories of neural computation have hypothesized that neuronal tuning of space, which determines generalization, should remained fixed during learning so that a combination of neuronal outputs can underlie adaptation simply and flexibly. Here, we challenge those theories with evidence that the neuronal tuning of movement space changed within minutes of training.

Figure 1.

Mean subject performance (D) in fields 1 (A; blue), 2 (B; green), and 4 (C; red). The numbers of the fields identify their spatial frequency (Eq. 1). The magnitude and direction of the applied force (arrows) depend on the x- and y-components of hand velocity, represented on the x- and y-axes, respectively. Insets show the dependence of x- and y-components of force on velocity direction. D, Correlation coefficients plotted against movement number as subjects train in fields 1 (blue), 2 (green), and 4 (red). Correlation coefficients were smoothed using a 20-movement moving average.

Figure 2.

Mean subject hand trajectories during fielded movements (A–C) and catch trials (D–F) in fields 1 (A, D), 2 (B, E), and 4 (C, F). Axes represent x and y hand positions. A–C, The gold lines represent the first movement in the force field, and the blue lines represent a movement immediately before a catch trial. D–F, Catch-trial trajectories are plotted in magenta with the replotted, blue precatch-trial trajectories. The vertices of the polygons connect positions at peak velocity. Magenta vertices outside blue vertices indicate that subjects anticipated a resistive force; blue outside magenta indicates anticipation of an assistive force.

Figure 3.

The sensitivity function (B in Eq. 2) plotted against the angular difference (θ) between sensed and adapted movement directions. The sensitivity function is estimated in fields 1 (thick black diamonds), 2 (thick gray circles), and 4 (thin black squares), based on movements averaged across the subjects.

Figure 4.

Neural network model (A, B) and model neuron tuning (C, E) that mimicked (D, F) movement-by-movement generalization in fields 1 and 2. A, Upper-level neurons, encoding desired movement space, projected to lower-level neurons, encoding predicted force (Eq. 3). The radii of the circles within the upper-level neurons represent activity in one movement. B, After this movement, the error-induced weight change (Eq. 4) is represented by the circles on the interlayer connections. A second movement then activated different portions of the upper neuronal tuning curves, as represented by new circles. C, Individual neuronal tuning functions, encoding Cartesian velocity, that best fit subject movement-by-movement sensitivity in fields 1 (black) and 2 (gray). The tuning was symmetrically dependent on the x- and y-components of hand velocity (only one dimension is shown). D, Simulations of sensitivity functions, as determined by the neuronal tunings in C and calculated using Equation 6. The dashed lines replot the human sensitivities from Figure 3. E, F, Best-fitting individual tuning functions (E) and simulated sensitivity functions (F) with neurons that encode velocity direction and scale activity with movement speed.