Motor Control Theory

1. Motor Control Theory Dynamic & Ecological Approaches (Large parts adapted from Wallace, 1993)

2. Asking the right question/questions • How do we move the way we do? • Seeks one solution • A causal influence • Tends to emphasize neural solutions • Tends to lead to “hypothetical constructs” to achieve solutions where neural solutions are not known • Tends to shape one’s thinking of control as a hierarchy

3. Asking the right question/questions • Why do we move the way we do? • A different focus • Tends to make us look at all possible influences on the shape of movement • Does not seek one solution, but accepts many simultaneously • Tends to shape one’s thinking of control as a heterarchy • This is the way we’ll be approaching the problem

4. A heterarchical theory of control • Or theories... • Complex systems theory • Dynamic pattern perspective & synergetics • Ecological psychology • All have in common the tendency to ask why rather than how • The study of relationships among things, rather than the things themselves • Leads to the study of...

5. A heterarchical theory of control • Constraints... • Things which limit our range of movements – thus “shaping” them • ...and affordances • Things which permit (or even suggest) certain methods of movement or interaction with an object

6. Individual Capabilities Environmental Constraints Task Demands A heterarchical theory of control From Newell (1986) Flexibility Cognition Strength/power Motor abilities Cardiovascular Sensory loss Speed/accuracy requirements Surface type moving seated Lighting standing Visual flow Environmental stability Number of tasks 3 categories of constraint (+ examples)

7. A heterarchical theory of control From Schmidt & Fitzpatrick (1993) Coordination dynamics Dynamics of CNS(neural level) Dynamics of action system (effector level) Dynamics of environment (environmental level) Connectionism Action system theory Laws of perceiving and acting (ecological psychology) Processes in coordination dynamics

8. An example (physical world) • Systems far from equilibrium • There are no instructions determining the pattern of behavior – just elements of the system interacting • But this isn’t very complex Equilibrium Far from equilibrium Punch hole in container Closed system Open system

9. A more complex example • The Benard instability (Prirogine & Stengers, 1984) • Again, the system self-organizes to these patterns • Heat is a controlparameter More heat A pan of c. 1cm. of water, on a stove heat hexagonal turbulent random

10. A more complex example • The Benard instability (Prirogine & Stengers, 1984) • Note that if the heat were reduced, the hexagonal pattern would not reappear • hysteresis More heat A pan of c. 1cm. of water, on a stove heat hexagonal turbulent random

11. Um, what about humans? • We’re far from equilibrium all the time • We metabolize food, produce energy, eliminate waste, exchange O2 and CO2, pass on strategies for survival, learn language, etc... • This encourages self-organization (according to this theoretical view) • Learning in this sense involves the de-stabilization of one pattern so that a new pattern of stability may be found

12. Systems far from equilibrium • Implications of being far from equilibrium • 2nd Law of non-equilibrium thermodynamics • Entropy – any closed system is always attracted to maximum entropy (and when here it is stable) • The system goes from a state where there is maximum energy exchange (low entropy), to one where there is none (high entropy) - equilibrium

13. Modeling progress from low to high entropy high entropy is an attractor for closed systems When systems are open, and far from equilibrium, they can exchange energy (dissipate it), allowing a range of behaviors from the system’s parts Systems far from equilibrium V Current state of the system Potential energy minimal value – maximum entropy A potential energy (V) function

14. Pattern formation in systems far from equilibrium conditions • When a system is pushed far from equilibrium, in seeking stability, it adopts certain patterns which are “locally stable” • The water in the pan, when heated, adopted a different pattern – hexagons, then turbulence • Heat was a control parameter • An outside variable that can push the complex system into different behaviors • There is also an order parameter • The macroscopic description of the emergent behavior pattern (e.g. hexagons, turbulence)

15. Pattern formation in systems far from equilibrium conditions • The order parameter... • The macroscopic description of the emergent behavior pattern (e.g. hexagons, turbulence) • Behavior patterns are expressed by order parameters • It represents some relationship among the component parts of the system • The relationship changes when the control parameter alters so that the order parameter loses stability • The pattern always fluctuates due to oscillation of parts • When the fluctuation reaches a critical point, order is lost and a new pattern may emerge

16. Pattern formation in systems far from equilibrium conditions • Applying order and control parameters to human movement • Haken (1988) suggested that order and control parameters would change across types of complex system, but that research within each field should attempt to uncover parameters particular to the field • Kelso (e.g. 1997) and others have taken this charge on within the field of human movement

17. But really – how does it apply to humans??? • An example of emergent patterns from motor development • Stepping/Walking • 0-2 months • Stepping a stable behavior • 3-4 months • Stepping disappears • Why? Where might you look if you believed in constraints and affordances? • How might you examine this?

18. But really – how does it apply to humans??? • An example of emergent patterns from motor development 1 month 3 months How might you regain it? And how else might you lose it? What causes this loss of stability?

19. But really – how does it apply to humans??? • An example of emergent patterns from motor development • Zelazo’s (1972) explanation: • instrumentally conditioned • Thelen’s (1993) explanation: • Legs are strengthened through training

20. With practice...

21. But really – how does it apply to humans??? Another example: In-phase: Faster and faster… Kelso & Scholtz, 1985

22. But really – how does it apply to humans??? Another example: In-phase: Faster and faster… Kelso & Scholtz, 1985 Kelso & Scholtz, 1985

23. But really – how does it apply to humans??? Another example: Anti-phase: Faster and faster… Kelso & Scholtz, 1985 Kelso & Scholtz, 1985

24. But really – how does it apply to humans??? Another example: Anti-phase: Faster and faster… Kelso & Scholtz, 1985 Kelso & Scholtz, 1985

25. Coordination Stability Difference between jt. Angles Variation in jt. Angles (arbitrary units) But really – how does it apply to humans??? • Stability and attractors • The in-phase and out-of-phase states in the Kelso example are attractor states for the movement • Perturbing the movement when it is in a stable attractor region will result in a quick return to stability (in-phase)

26. Coordination Stability Difference between jt. Angles Variation in jt. Angles (arbitrary units) But really – how does it apply to humans??? • Stability and attractors • Perturbing the movement when it is close to a region of instability will result in either a longer period of instability followed by a resumption of the original state, or a new attractor state • When close to a period of transition, the movement will exhibit critical fluctuations • the movement will be more ‘wobbly,’ less stable

27. Relating to humans – last example – running and walking (see web) • Collective variable • Control parameter • Attractor state B A C

28. Exploring the coordination of movement using synergetics • Define system and joint motions used to perform task • Scale a parameter within the task and identify the slowest “moving” relationships of joint motions (order parameter) • Scan the dynamics of the slow moving relationships • Identify the preferred and non-preferred patterns • Choose a potential control parameter • Determine the relative stability of the patterns within a phase transition experiment • Mathematically model your system • Test the model

29. Properties of DST • Attractor states are characterized by • optimal energy efficiency • Stability • Self-organization • Motor development example: • The emergence of the shape of the step wasn’t dictated by the brain, but emerged as a consequence of the interplay amongst a whole range of variables.

30. Properties of DST • Coordinative structures • Partial solution to the degrees of freedom problem • joint or limb components ‘linked’ to each other during movement • become sensitive to the movements of other parts • they becomeone structure for the purposes of coordination • Kelso et al. (1984) – jaw movements • Arytunyen et al. (1980) – pistol shooting

31. Properties of DST • Perception-action coupling • Rather than the brain specifying behavior, the environment specifies behavior (such as timing) – that is it constrains the movement in an important way • There are many examples of this: • Long-jump (Lee, Lishman & Thomson, 1982) • Control of braking (Lee, 1976) • Catching (Savelsburgh, Whiting & Bootsma, 1992)