Lecture 3 - Schema theory 2023
Page 1:
Topic: Schema theory and scheduling variability of practice
Speaker: Dr. Dominic Orth
Page 2:
Theory: Schmidt's schema theory
Applications and evidence of schema theory
Variability and transfer in motor learning
Individual differences
Challenge point hypothesis
Page 3:
Learning outcomes:
Define key concepts of Schema theory
Initial conditions, Parameters, Recognition schema, Recall schema, Generalised motor program, invariant features
Define key concepts required in applying Schema theory
Variability in practice conditions, contextual interference, transfer test
Describe research evidence supporting Schema theory
Summarize best practice advice for practitioners
Variability of practice, individual differences in skill level
Page 4:
Questions to think about during the lecture:
How does variation in conditions help people learn?
How can you increase or decrease variability during practice?
Should variation be based on the learner's skill level?
Page 5:
Schmidt's schema theory:
Learning theory
Importance and questions answered by the theory
Relates to variability in practice conditions and transfer of skill
Key concepts: Initial conditions, Desired outcome, Feedback, Parameters, Recall schema, Recognition schema, Generalised motor program, Limbs, Environment, Measured outcome, Augmented Proprioception, Exteroception
Page 6:
Schmidt's schema theory:
Characterization of learning
Detection of initial conditions and desired outcome
Selection of generalised motor program
Use of recall schema to assign parameters
Feedback evaluation by recognition schema
Formation of relationships and adaptation of motor programs
Page 7:
Key concepts of Schmidt's schema theory:
Open-loop control systems
Role of sensory feedback in rapid actions
Generalised motor program
Learning in schema theory
Closed-loop control systems (ref. week 2)
Page 8:
Open-loop control systems:
Not feedback dependent
Examples: old traffic light systems
Executive, Effector, Input, Instructions, Output
Page 9:
Open-loop control systems:
Control without error detection and adjustment
Explanation for high-speed discrete actions
Input as trigger for movement
No feedback about error during the action
Effector, Trigger, Instructions, Output, Executive, Stimulus Identification, Response Selection, Response Programming
Page 10:
Evidence for open-loop control in continuous actions
Central Pattern Generators in the spinal cord
Afferent information as a trigger for action
Example of passive dynamic walking
Page 11:
Role of feedback in rapid actions:
Sensory feedback available before and after the action
Use of feedback to determine initial conditions and desired outcome
Recall schema prepares the movement for current conditions
Page 12:
Role of feedback in rapid actions:
Evaluation of action after it is carried out
Extensive feedback delivered to the central nervous system
Information used to determine the quality of the movement
Adjustment of movement on subsequent trial
Recognition schema evaluates feedback information
Page 13:
Generalised motor program:
Controls a class of action for different initial conditions and desired outcome
Invariant features of the action
Parameters supplied to adapt the program to specific situations