Early stages of sensorimotor map acquisition: learning with free exploration, without active movement or global structure

How the conception of control influences our understanding of actions

Wilful movement requires neural control. Commonly, neural computations are thought to generate motor commands that bring the musculoskeletal system - that is, the plant - from its current physical state into a desired physical state. The current state can be estimated from past motor commands and from sensory information. Modelling movement on the basis of this concept of plant control strives to explain behaviour by identifying the computational principles for control signals that can reproduce the observed features of movements. From an alternative perspective, movements emerge in a dynamically coupled agent-environment system from the pursuit of subjective perceptual goals. Modelling movement on the basis of this concept of perceptual control aims to identify the controlled percepts and their coupling rules that can give rise to the observed characteristics of behaviour. In this Perspective, we discuss a broad spectrum of approaches to modelling human motor control and their notions of control signals, internal models, handling of sensory feedback delays and learning. We focus on the influence that the plant control and the perceptual control perspective may have on decisions when modelling empirical data, which may in turn shape our understanding of actions.

A Framework for Optimizing Co-adaptation in Body-Machine Interfaces

The operation of a human-machine interface is increasingly often referred to as a two-learners problem, where both the human and the interface independently adapt their behavior based on shared information to improve joint performance over a specific task. Drawing inspiration from the field of body-machine interfaces, we take a different perspective and propose a framework for studying co-adaptation in scenarios where the evolution of the interface is dependent on the users' behavior and that do not require task goals to be explicitly defined. Our mathematical description of co-adaptation is built upon the assumption that the interface and the user agents co-adapt toward maximizing the interaction efficiency rather than optimizing task performance. This work describes a mathematical framework for body-machine interfaces where a naïve user interacts with an adaptive interface. The interface, modeled as a linear map from a space with high dimension (the user input) to a lower dimensional feedback, acts as an adaptive “tool” whose goal is to minimize transmission loss following an unsupervised learning procedure and has no knowledge of the task being performed by the user. The user is modeled as a non-stationary multivariate Gaussian generative process that produces a sequence of actions that is either statistically independent or correlated. Dependent data is used to model the output of an action selection module concerned with achieving some unknown goal dictated by the task. The framework assumes that in parallel to this explicit objective, the user is implicitly learning a suitable but not necessarily optimal way to interact with the interface. Implicit learning is modeled as use-dependent learning modulated by a reward-based mechanism acting on the generative distribution. Through simulation, the work quantifies how the system evolves as a function of the learning time scales when a user learns to operate a static vs. an adaptive interface. We show that this novel framework can be directly exploited to readily simulate a variety of interaction scenarios, to facilitate the exploration of the parameters that lead to optimal learning dynamics of the joint system, and to provide an empirical proof for the superiority of human-machine co-adaptation over user adaptation.

Early stages of sensorimotor map acquisition: neurochemical signature in primary motor cortex and its relation to functional connectivity

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.