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Computer Science
Control Systems
Motor Learning and Control
1. Introduction to Motor Behavior
2. Neural Foundations of Motor Control
3. Principles of Motor Control
4. Sensory Contributions to Motor Control
5. Motor Learning Principles
6. Theories and Models of Motor Learning
7. Computational Approaches to Motor Control
8. Applications and Special Populations
Theories and Models of Motor Learning
Schema Theory
Motor Response Schema
Initial Conditions
Response Specifications
Sensory Consequences
Response Outcomes
Recognition Schema
Expected Sensory Consequences
Actual Sensory Consequences
Error Detection
Variability Predictions
Novel Response Production
Parameter Scaling
Supporting Evidence
Transfer Studies
Variability Effects
Criticisms and Limitations
Storage Problems
Novelty Issues
Dynamical Systems Models
Attractor Landscapes
Skill States as Attractors
Learning as Landscape Changes
Perturbation and Stability
Skill Robustness
Adaptive Flexibility
Self-Organization in Learning
Emergent Coordination
Practice-Induced Changes
Computational Models
Optimal Control Theory
Cost Function Minimization
Movement Optimization
Minimum Jerk Models
Minimum Torque Change
Reinforcement Learning
Reward-Based Learning
Policy Improvement
Actor-Critic Models
Temporal Difference Learning
Error-Based Learning
Sensory Prediction Errors
Forward Model Adaptation
Cerebellar Learning Models
Memory Consolidation
Offline Learning
Between-Session Improvements
Sleep-Dependent Consolidation
Memory Systems
Declarative Memory
Procedural Memory
Working Memory
Consolidation Processes
Synaptic Consolidation
Systems Consolidation
Reconsolidation
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5. Motor Learning Principles
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7. Computational Approaches to Motor Control