Optimising training - constraints led approach - 6.2

Question 1

what is the constraints led approach?

Answer 1

A style of coaching where the coach takes a technique, skill or tactic and isolates it in a small sided game

Players are encouraged to find their own movement solution

An ecological approach which incorporates exploration

CLA involves principled practice design

Not given instructions on how to perform the movement - put on constraints - limit the amount of options they have

Centred around autonomy for players - come up with their own movement patterns

Came from deciding one part of brain doesn’t control everything - the way we perform skills is more decided by the environment than our brain

Controversial topic

Question 2

issues with traditional approach

Answer 2

Assuming that there is a ‘central controller’ just displaces the issue

Plenty of perceptual information in environment so why are detailed models needed?

If there is no central controller self-organisation becomes important

Self-organise our movements

Question 3

development of CLA

Answer 3

1. How can we account for organisation and behaviour without attributing it to an internal control structure?
2. How can an organism produce behaviours that are stable but also adaptive?

Question 4

CLA is designed to

Answer 4

Alter player behaviour

Introduce fun, competitive games

Help players find their own solutions

Tackle specific skills or aspects of the game

Only works if you give them enough time to come up with solutions

Younger (age) = takes longer

Constraints shouldn’t stop the skill looking like how it should

Question 5

what are constraints?

Answer 5

Newell (1986)

Motor development not skill acq

Eliminates possibilities for action

Actions are not caused by constraints

Does not necessarily increase complexity

Vary in degree of time dependence

Constraints = facilitating type of behaviour

Actions don’t come from central processor

Can only perform a skill in a certain way depending on your environment - place limits on the way you can play

try and limit the options to help player make their own solutions

Manipulate the environment

Doesn’t have to make the action more complex - can make it easier

Limits the range of options available and so helps them to make good decisions

Question 6

3 types of constraint

Answer 6

Organism

Environmental

Task

Question 7

organism constraints

Answer 7

things within you - height, range of motions, max speed,
basic structural traits, how quickly your brain produces new connections

some can be trained

whether it’s a constraint might depend on the task

Question 8

env constraints

Answer 8

level of light, anything that cannot be manipulated by

the coach

Question 9

task constraints

Answer 9

goals, rules and equipment

Question 10

interp is key

Answer 10

Original constraint = legs and arms had to move simultaneously and in synchrony - everyone assume they had to be under the water - imposed
own constraints based on a rule that didn’t actually limit them

Question 11

learning objective –> learning activity

Answer 11

Decide on the goal and then design the session around the goal

Criticism - don’t remove cognition - it is possible to set a goal without telling them exactly how to achieve it

Abilities already tailored to their own sports - refine what they already
know

Question 12

triangle diagram

Answer 12

Understand what info is available in the environment - what aspects
influence our behaviours

Represent circular causality - perception doesn’t cause movement and vice versa - interaction but not a causal relationship

Question 13

dynamics of action

Answer 13

Development of a movement action is dependent on the perception-action cycle

Understanding perceptual information and affordance not enough

There is no general purpose solution for all tasks!

Need to take into account of ability to perform the skill

Doesn’t tell us how to use the information

Limited by our own abilities, environment, rules

Question 14

behavioural dynamics (Warren, 2006)

Answer 14

Performer and environment – coupled dynamical systems

Interaction between performer and environment creates new dynamical system

Performer-environment interactions = self- organisation of movement

How learning occurs not really discussed

Information based control

Performers perception of capability not considered

Treat them as a pair

Used to predict what might happen in the future, why it happened in the past and understand some of the interactions

Question 15

ecological dynamics

Answer 15

involves affordance based control

Takes action capabilities into
account

Also talks about affordances - perceived capabilities and how they interact

Different skills performed using different movements - biological degeneracy

High level cognition - do team have more defensive or attacking approach? - not how they find a solution

Question 16

ecological dynamics key aspects

Answer 16

Education of attention

Education of intention

Skill performance arises from
environment/performer
interactions

Biological degeneracy

High level cognition

Question 17

issues with research into CLA

Answer 17

Different methods used to evaluate

CLA very recent

Which constraints?

General constraints manipulations

Can’t make group analyses - learn at different speeds

Qualitative approach

Have to be trained in specific way - need to establish the effect of different constraints

Question 18

Buszard et al.

Answer 18

Not unique to the constraints led approach

E.g. small child but full size basketball hoop - can’t learn effect movement pattern - scale to their size and age

Good evidence to support the use of scaling and more willing to perform the skills - keep them in the sport until they can use the proper equipment

Criticism - studies are not long term - studies also taken out of
context

Question 19

CLA and implicit learning

Answer 19

CLA approach involves players finding a solution to movement problems on their own

Promotes implicit learning?

Doesn’t meet all requirements of implicit learning

Is similar to errorless learning - constraining options to be more successful - limiting things that could go wrong

Question 20

Buszard et al. (2014)

Answer 20

They used dual task

Secondary task only disruptive when using full size equipment

Concluded using scaled equipment reduces load on memory and attentional demands required

Can we challenge performance in adult setting?

Question 21

Scott and Gray (2010)

Answer 21

Adaptation to heavier bat different across Ps

Compared to own individual constraints - better able to handle weight - adapted by swinging with more force - less able - changed timing

Question 22

Davids

Answer 22

Small sided games most commonly applied as a constraint - develop
technical and transferable skills - can be adapted in game situation

Removing opponents - simple passes - take away perceptual sources - only useful for creating movement patterns that can be used in situation

Don’t treat attacker and defender as separable aspects

Question 23

Timmerman et al. (2017)

Answer 23

Players adapted to situation well - changes in rally length - changes in shots used - changes in percentage of successful serves

Question 24

Barris et al. (2013)

Answer 24

Not able to explore variability of movements

Post - completed more dives and baulked less - greater consistency in diving scores and greater variability of movement in joints - ankles

Question 25

Tan et al. (2017)

Answer 25

Higher clustering in herding condition and

Manipulation of rules can encourage or discourage herding

Preliminary evidence - nothing rock solid

Question 26

Gray (2018)

Answer 26

Barrier set so ball had to be set with certain launch angle - progressive constraint

Used VR to create optimal feedback

CLA did better - more home runs, greater accuracy and angle - increased speed - low variability at start, high during training and low again at end -
exploring movement options

Ext focus next best and int worse

Question 27

Timmerman et al. (2017)

Answer 27

manipulated game constraints on match performance in hockey

all constraints influenced performance positively