Motor Control Theory Chapter 5 – slide set 4

The crucial relationship is: From the last slide set… Between the order parameter(s), control parameter(s) and stability… For the baby stepping: Order parameter = step reflex; control parameters = fat weight, muscle strength, environment Energy efficiency is the overall guide to the circumstances under which the step is more or less stable

The crucial relationship is: So where does the step part of the movement come from? Remember the idea of interacting parts of a system (from the water example)? Here the interacting parts are muscles, and their connections When innervated as a whole, the babies muscles resolve themselves into a kick

The crucial relationship is: …and the dynamics of the CNS adapting to the most energy efficient solution to the problem (given enough time and experience) Leads to many “local solutions” Learning progresses by de-stabilizing these in favor of more effective solutions Think of developing bad habits in sport, and trying to get out of them – the longer they have persisted, the more stable they are and the harder they are to break

Relating to humans – another example – running and walking (see link below) Order Parameter Control parameter Attractor state B A C http://perso.wanadoo.fr/l.d.v.dujardin/ct/cusp.html#applet2

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

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

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

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

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) Difference between jt. Angles Coordination Stability 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 Difference between jt. Angles Coordination Stability Variation in jt. Angles (arbitrary units)

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

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.

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 become one structure for the purposes of coordination Kelso et al. (1984) – jaw movements Arytunyen et al. (1980) – pistol shooting

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)

Perception-action coupling - examples

Perception-action coupling - examples

“Embodied cognition” in artificial intelligence Other thoughts... “Embodied cognition” in artificial intelligence

Other thoughts... Pictures from Thelen’s (and Ulrich’s) early research – stepping and kicking

Other thoughts... Pictures from Thelen’s (and Ulrich’s) early research – “walking” on a treadmill

Other thoughts... Data from Thelen et al. 1993: two children showing very different approaches to reaching for an object. However...

by the end of the first year, the movements are very similar ...by the end of the first year, the movements are very similar. What does that say about the search for coordination?

Other thoughts... Schematic from Thelen et al. 2001: the “A not B” problem. A truly complex and adaptive case of modeling order and control parameters...see next slide

Other thoughts... Excerpt from Spencer et al (2006): the “A not B” problem. A list of control parameters in the task. And another comment on embodied cognition. DST doesn't only deal with movement.