Motor Learning PHYL 4518 - Wk 3 - Sep 18 PDF

PHYL 4518 Motor Learning F2024 – Wk 3 Zoe Chan, PhD [email protected]  Reaction Time (RT) Reaction time (RT) = premotor time + motor time Premotor time: pure information processing Motor time (electromechanical delay): excitation-contracti...

PHYL 4518 Motor Learning F2024 – Wk 3 Zoe Chan, PhD [email protected]  Reaction Time (RT) Reaction time (RT) = premotor time + motor time Premotor time: pure information processing Motor time (electromechanical delay): excitation-contraction coupling Premotor TimeMotor Time Movement time Reaction Time Total Response time  Understand and differentiate the different types of error measurement Be able to define a natural athlete and giftedness Today’s Understand the challenges and issues with talent learning identification objectives Measuring error Among the most common performance measures Determine accuracy of response outcomes as well as response production measures Spatial (in space) or temporal (in time) Measuring error Gain understanding of what caused the error How to instruct performers to better their performance How would you measure performance to provide feedback? Measuring error Who did better? What sort of feedback would you give each? A B C c c c c c c cc c c c c  Measuring error An error score is compiled and provided after several trials The majority of feedback given to learners to improve performance is based on error information Specifically, the deviation from a standard or idealized Measuring error Response outcome are easiest to quantify Constant Error Absolute Error Variable Error  Constant error Constant error (CE) is the average error over a given number of trials. – Reflects both magnitude and direction of error – Provides a bias or tendency in the performance CE = ∑ (xi - T) / n Take the average!  Absolute error Absolute error (AE) is the average over a given number of trials of the error absolute values. – Provides a measure of error magnitude only AE = ∑ (|xi – T|) / n Take the average, but make all values positive!  Variable error Variable error (VE) is the standard deviation of the group of error scores. – Not a measure of accuracy – Assessment of the participant’s inconsistency VE = √[Σ(xi – M)2 / n] Find the average (M, also the CE!) Square each difference from average (xi – M)2 Add these values up Divide by n Square root 2.7  Variable error VE = √[Σ(xi – M)2 / B n] Find the average(M) Square each c c difference from average (xi – M)2 Add these values c up A c C Divide by n Square root c c c c cc c c -ve +ve Natural athlete All-round athlete – Excel at multiple sports Many different abilities that are relevant to many motor skills or A few extremely good abilities that are relevant to many motor skills E.g., power, speed, visual search Make good use and be able to transfer Giftedness Untrained abilities that place someone in top 10% of age group Often refer to genetic (static) abilities Talent: combination of skills and abilities contributing to overall success in particular sport or activity  Talent identification Talent identification: Measuring specific subcomponent motor skills and abilities that contribute to overall performance Identifying areas of giftedness Talent identification Measuring abilities is important – Practice and training can be more deliberate in enhancing strength and overcoming weakness Successful motor skill performance nearly always requires multiple abilities – Measuring these abilities can be an important part of athletic development – Prediction of motor skill proficiency Talent identification Talent identification in athletes – Prediction of performance: looking for ‘right’ characteristics because they have the potential to reach the highest level Effectiveness is unclear and may lead to elimination of potentially talented players – Domain selection: broad or general form of talent ID that identifies particular fields or areas in which one’s particular talent or giftedness may be more useful  Talent identification Challenges: – Measuring those skills and abilities is difficult (muscle strength vs. emotional control) – Matching of abilities with performance in a particular to motor skill is difficult  Talent identification Issues with current talent ID program: – Success is limited, especially in young children Growth and maturation Adaptability to training is hard to predict – In high-level performers Baseline level of some abilities that are necessary Each performer then learns to use other abilities uniquely  Talent identification What we do know: Example of relative age effect – Relative age effect Born early in the year > selected over peers > more resources > more success How it may work: – Domain selection – Training guidelines Provide direction for training and practise Jakobsson et al. (2021)