Empirical studies on the computational and cognitive mechanisms of human learning and movement

The topic of human movement, and the question of how humans learn new behaviors, has puzzled philosophers and scientists since classical times. A commonly held assumption is that there are two qualitatively distinct learning systems, one responsible for remembering knowledge of facts and events, and the other responsible for forming associations and learning new skills, including motor learning. The evidence in support of this dissociation has been independently reproduced through many different experiments and methods of analysis. One line of evidence that has recently been investigated is the dual-component nature of adaptation learning. When humans and animals are challenged with a change in their environment or the physiology of their bodies, such as what might happen through growth and development or because of injury, the nervous system adjusts its control mechanisms to maintain accurate movements. Learning of this form is known as adaptation, and had originally been theorized to be achieved through an implicit learning mechanism. Furthermore, it was often thought that this same learning mechanism was responsible for more general forms of learning, such as learning the use of new tools. This model has recently come under scrutiny as evidence has emerged demonstrating a role for memory of facts in adaptation. If there are at least two mechanisms responsible for adaptation learning, which one of them, if either, is actually responsible for more general skill learning? If one, but not the other, of these mechanisms is responsible for skill learning, what is adaptation really a model of? And how might the conclusions of other studies that used adaptation as a general model for learning need to be reconsidered? For instance, the results from neurophysiological studies of adaptation may find neural correlates that are uniquely related to adaptation but not to other types of motor learning. Having a better behavioral- and computational-level understanding of the mechanisms involved in adaptation learning is necessary to address these and potentially many other questions. Given the challenges present in the study of adaptation, there is a need for other models of learning and movement that give different perspectives and emphasize other aspects of learning that might be missing from adaptation. For instance, adaptation involves correction of movements around an existing ability, such as reaching. How is reaching itself learned? Acquiring or building new behavioral abilities might involve qualitatively different mechanisms compared to adaptation. Furthermore, new methods for analyzing the kinematics of movements are necessary, as adaptation paradigms typically limit their analysis to the choice of reaching direction only. In this dissertation, I will present several original, empirical studies on the role of cognition and explicit knowledge in motor learning. I will investigate the computational mechanisms that underlie learning new behaviors. I will introduce a new model for human motor skills and skill learning, and show how this model fills gaps that exist in the repertoire of models, methods, and concepts currently popular in the science of learning. I will show evidence that adaptation learning is made up of at least two qualitatively distinct learning components. One component appears to be deliberate, driven by explicit knowledge, and is computationally expensive. The other is implicit, driven by sensory-prediction errors, and is automatic and readily expressed. I will demonstrate that the deliberate component becomes automatic following practice, and will argue that this process is a plausible mechanism for how more general motor skills are learned. Implicit recalibration does not change with practice and therefore appears unlikely to be responsible for skill learning. I will show that learning a new continuous-movement behavior, like skiing or riding a bike, is done through the creation of a flexible feedback control policy. I will discuss the inconsistency of sequence learning and chunking hypotheses, and contrast them with the control policy theory. The studies, results, and conclusions presented here demonstrate that motor learning intrinsically involves cognition and explicit representations of knowledge. The classical concept of motor learning being a subset of implicit memory is inconsistent with the present findings and other recent work. Instead, a view of motor learning as being a phenomenon emergent from the interaction of multiple forms of memory and algorithms of learning is emerging

Proprioceptive loss and the perception, control and learning of arm movements in humans: evidence from sensory neuronopathy

© 2018 The Author(s) It is uncertain how vision and proprioception contribute to adaptation of voluntary arm movements. In normal participants, adaptation to imposed forces is possible with or without vision, suggesting that proprioception is sufficient; in participants with proprioceptive loss (PL), adaptation is possible with visual feedback, suggesting that proprioception is unnecessary. In experiment 1 adaptation to, and retention of, perturbing forces were evaluated in three chronically deafferented participants. They made rapid reaching movements to move a cursor toward a visual target, and a planar robot arm applied orthogonal velocity-dependent forces. Trial-by-trial error correction was observed in all participants. Such adaptation has been characterized with a dual-rate model: a fast process that learns quickly, but retains poorly and a slow process that learns slowly and retains well. Experiment 2 showed that the PL participants had large individual differences in learning and retention rates compared to normal controls. Experiment 3 tested participants’ perception of applied forces. With visual feedback, the PL participants could report the perturbation’s direction as well as controls; without visual feedback, thresholds were elevated. Experiment 4 showed, in healthy participants, that force direction could be estimated from head motion, at levels close to the no-vision threshold for the PL participants. Our results show that proprioceptive loss influences perception, motor control and adaptation but that proprioception from the moving limb is not essential for adaptation to, or detection of, force fields. The differences in learning and retention seen between the three deafferented participants suggest that they achieve these tasks in idiosyncratic ways after proprioceptive loss, possibly integrating visual and vestibular information with individual cognitive strategies

Hedging your bets: intermediate movements as optimal behavior in the context of an incomplete decision.

Existing theories of movement planning suggest that it takes time to select and prepare the actions required to achieve a given goal. These theories often appeal to circumstances where planning apparently goes awry. For instance, if reaction times are forced to be very low, movement trajectories are often directed between two potential targets. These intermediate movements are generally interpreted as errors of movement planning, arising either from planning being incomplete or from parallel movement plans interfering with one another. Here we present an alternative view: that intermediate movements reflect uncertainty about movement goals. We show how intermediate movements are predicted by an optimal feedback control model that incorporates an ongoing decision about movement goals. According to this view, intermediate movements reflect an exploitation of compatibility between goals. Consequently, reducing the compatibility between goals should reduce the incidence of intermediate movements. In human subjects, we varied the compatibility between potential movement goals in two distinct ways: by varying the spatial separation between targets and by introducing a virtual barrier constraining trajectories to the target and penalizing intermediate movements. In both cases we found that decreasing goal compatibility led to a decreasing incidence of intermediate movements. Our results and theory suggest a more integrated view of decision-making and movement planning in which the primary bottleneck to generating a movement is deciding upon task goals. Determining how to move to achieve a given goal is rapid and automatic

Data from: Hedging your bets: intermediate movements as optimal behavior in the context of an incomplete decision

Existing theories of movement planning suggest that it takes time to select and prepare the actions required to achieve a given goal. These theories often appeal to circumstances where planning apparently goes awry. For instance, if reaction times are forced to be very low, movement trajectories are often directed between two potential targets. These intermediate movements are generally interpreted as errors of movement planning, arising either from planning being incomplete or from parallel movement plans interfering with one another. Here we present an alternative view: that intermediate movements reflect uncertainty about movement goals. We show how intermediate movements are predicted by an optimal feedback control model that incorporates an ongoing decision about movement goals. According to this view, intermediate movements reflect an exploitation of compatibility between goals. Consequently, reducing the compatibility between goals should reduce the incidence of intermediate movements. In human subjects, we varied the compatibility between potential movement goals in two distinct ways: by varying the spatial separation between targets and by introducing a virtual barrier constraining trajectories to the target and penalizing intermediate movements. In both cases we found that decreasing goal compatibility led to a decreasing incidence of intermediate movements. Our results and theory suggest a more integrated view of decision-making and movement planning in which the primary bottleneck to generating a movement is deciding upon task goals. Determining how to move to achieve a given goal is rapid and automatic

Expt1_Data

Matlab .mat file containing data from Experiment 1

Expt2_Data

Matlab .mat file containing data from Experiment 2

Expt3_Data

Matlab .mat file containing data from Experiment 3

Group results for Experiment 1.

<p>Estimated sigmoid parameters across all subjects for each target jump amplitude. A) Total time over which reach direction varied (<i>t</i>₉₅–<i>t</i>₀₅; proportional to slope parameter, <i>τ</i>). B) Center of sigmoid, <i>t</i>₅₀. C) Time required to fully compensate for the target jump, <i>t</i>₉₅. Error bars indicate s.e.m. D) Average sigmoidal fits to behavior across all subjects obtained by averaging parameters <i>τ</i> and <i>t</i>₅₀.</p