Mechanisms of Human Motor Learning Do Not Function Independently

Human motor learning is governed by a suite of interacting mechanisms each one of which modifies behavior in distinct ways and rely on different neural circuits. In recent years, much attention has been given to one type of motor learning, called motor adaptation. Here, the field has generally focused on the interactions of three mechanisms: sensory prediction error SPE-driven, explicit (strategy-based), and reinforcement learning. Studies of these mechanisms have largely treated them as modular, aiming to model how the outputs of each are combined in the production of overt behavior. However, when examined closely the results of some studies also suggest the existence of additional interactions between the sub-components of each learning mechanism. In this perspective, we propose that these sub-component interactions represent a critical means through which different motor learning mechanisms are combined to produce movement; understanding such interactions is critical to advancing our knowledge of how humans learn new behaviors. We review current literature studying interactions between SPE-driven, explicit, and reinforcement mechanisms of motor learning. We then present evidence of sub-component interactions between SPE-driven and reinforcement learning as well as between SPE-driven and explicit learning from studies of people with cerebellar degeneration. Finally, we discuss the implications of interactions between learning mechanism sub-components for future research in human motor learning.

A Ludicidade como Instrumento de Incentivo e Aprendizagem na Modalidade EJA - Ensino de Jovens e Adultos / Ludicity as an Incentive and Learning Instrument in the EJA Modality - Youth and Adults Education

Resumo: Este artigo tem como principal objetivo apresentar ao leitor uma nova perspectiva de ensino na modalidade EJA mediante as atribuições da ludicidade. O termo ludicidade remete muito ao ambiente infantil, que é mais proveniente de brincadeiras e jogos direcionados em seu contexto, mas na EJA vem abrindo espaço para o novo, com novas estratégias lúdicas para ampliação do conhecimento. O estudo dispõe como bases teóricas estudos bibliográficos de caráter qualitativo que foi o processo metodológico utilizado para a construção do artigo, assim como análise em obras e falas de autores renomados no tema abordado que tem opiniões formadas sobre o aspecto lúdico como mecanismo de aprendizagem. Foram apontadas algumas sugestões de procedimentos lúdicos a alunos de EJA, assim como uma perspectiva atendida na construção para educadores flexivos e inovadores.Abstract:This article has as main objective to present to the reader a new perspective of teaching in the EJA modality through the attributions of playfulness. The term playfulness refers a lot to the children's environment, which comes more from games and games directed in its context, but in EJA it has been opening space for the new, with new playful strategies to expand knowledge. The study has as theoretical bases bibliographic studies of a qualitative character, which was the methodological process used to build the article, as well as analysis in works and speeches by renowned authors on the topic discussed who have formed opinions on the playful aspect as a learning mechanism. Some suggestions of playful procedures for EJA students were pointed out, as well as a perspective considered in the construction for flexible and innovative educators. Keywords: Playfulness, EJA, Learning, Incentive.

A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning

In order to solve the problems of rapid path planning and effective obstacle avoidance for autonomous underwater vehicle (AUV) in 2D underwater environment, this paper proposes a path planning algorithm based on reinforcement learning mechanism and particle swarm optimization (RMPSO). A feedback mechanism of reinforcement learning is embedded into the particle swarm optimization (PSO) algorithm by using the proposed RMPSO to improve the convergence speed and adaptive ability of the PSO. Then, the RMPSO integrates the velocity synthesis method with the Bezier curve to eliminate the influence of ocean currents and save energy for AUV. Finally, the path is developed rapidly and obstacles are avoided effectively by using the RMPSO. Simulation and experiment results show the superiority of the proposed method compared with traditional methods.

A cost-efficient face learning mechanism: The impact of stability in appearance on the resolution of facial representations

The way faces become familiar and what information is represented as familiarity develops has puzzled researchers in the field of human face recognition for decades. In this paper, we propose a cost-efficient mechanism of face learning to describe how facial representations form over time and that explains why recognition errors occur. Encoding of diagnostic facial information would follow a coarse-to-fine trajectory, modulated by the intrinsic stability in individual faces’ appearance. In four experiments, we draw on a robust and ecological method using a proxy of exposure to famous faces in the real world to test hypotheses generated by the model and we manipulate test images to probe the nature of facial representations. We consistently show that stable facial appearances help create more reliable representation in early stages of familiarisation but that their resolution remains relatively low and therefore less discriminative over time. In contrast, variations in appearance hinder recognition at first but encourage refinement of representations with further exposure. Consistent with the cost-efficient face learning mechanism we propose, facial representations built on a foundation of large-scale coarse information. When coarse information loses its diagnostic value through the experience of variations across encounters, facial details and their spatial relationships receive additional representational weights.

Evolutionary learning in the brain by heterosynaptic plasticity

The way in which the brain modifies synapses to improve the performance of complicated networks remains one of the biggest mysteries in neuroscience. Existing proposals lack sufficient experimental support, and neglect inter-cellular signaling pathways ubiquitous in the brain. Here we show that the heterosynaptic plasticity between hippocampal or cortical pyramidal cells mediated by diffusive nitric oxide and astrocyte calcium wave, together with flexible dendritic gating of somatostatin interneurons, implies an evolutionary algorithm (EA). In simulation, this EA is able to train deep networks with biologically plausible binary weights in MNIST classification and Atari-game playing tasks up to performance comparable with continuous-weight networks trained by gradient-based methods. Our work leads paradigmatically fresh understanding of the brain learning mechanism.

Output-Feedback Position Tracking Servo System with Feedback Gain Learning Mechanism via Order-Reduction Speed-Error-Stabilization Approach

This paper presents a novel trajectory-tracking technique for servo systems treating only the position measurement as the output subject to practical concerns: system parameter and load uncertainties. There are two main contributions: (a) the use of observers without system parameter information for estimating the position reference derivative and speed and acceleration errors and (b) an order reduction exponential speed error stabilizer via active damping injection to enable the application of a feedback-gain-learning position-tracking action. A hardware configuration using a QUBE-servo2 and myRIO-1900 experimentally validates the closed-loop improvement under various scenarios.