Abstract
In everyday life, humans interact with a dynamic environment often requiring rapid adaptation of visual perception and motor control. In particular, new visuo–motor mappings must be learned while old skills have to be kept, such that after adaptation, subjects may be able to quickly change between two different modes of generating movements (‘dual–adaptation’). A fundamental question is how the adaptation schedule determines the acquisition speed of new skills. Given a fixed number of movements in two different environments, will dual–adaptation be faster if switches (‘phase changes’) between the environments occur more frequently? We investigated the dynamics of dual–adaptation under different training schedules in a virtual pointing experiment. Surprisingly, we found that acquisition speed of dual visuo–motor mappings in a pointing task is largely independent of the number of phase changes. Next, we studied the neuronal mechanisms underlying this result and other key phenomena of dual–adaptation by relating model simulations to experimental data. We propose a simple and yet biologically plausible neural model consisting of a spatial mapping from an input layer to a pointing angle which is subjected to a global gain modulation. Adaptation is performed by reinforcement learning on the model parameters. Despite its simplicity, the model provides a unifying account for a broad range of experimental data: It quantitatively reproduced the learning rates in dual–adaptation experiments for both direct effect, i.e. adaptation to prisms, and aftereffect, i.e. behavior after removal of prisms, and their independence on the number of phase changes. Several other phenomena, e.g. initial pointing errors that are far smaller than the induced optical shift, were also captured. Moreover, the underlying mechanisms, a local adaptation of a spatial mapping and a global adaptation of a gain factor, explained asymmetric spatial transfer and generalization of prism adaptation, as observed in other experiments.
Highlights
Visuo–motor mappings are efficiently learned, adapted and re– adapted throughout life
With a plausible learning mechanism inspired by reinforcement learning [22], our model finds a configuration of its internal parameters that allows for acquisition and storage of dual sensory– motor mappings
In order to quantify learning in our dual–adaptation experiment, we focus on the direct effect, i.e. the error of the first movements made in every block b indexed by b~1,2
Summary
Visuo–motor mappings are efficiently learned, adapted and re– adapted throughout life. When first confronted with such a visual shift, observers have the strong tendency for ballistic movements to deviate leftwards or rightwards (depending on the type of prisms) of the correct reaching trajectory towards a target This behavior results in an error between final hand and target positions, the direct effect. This error is subsequently corrected by observers during further pointing movements, until they acquire a new mapping between proprioceptive and visual space. N The alternative hypothesis is that the acquisition of multiple mappings is mainly regulated by the number of trials with an effective feedback, e.g. with a pointing error This would mean that dual–adaptation should mainly depend on the total number of executed movements which provide feedback about the error, but not on the number of phase changes. The appendix S1 holds additional information for the model setup and generalization of results
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