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Motor Learning- October 1st.docx

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Human Biology
Luc Tremblay

Motor Learning- October 1 st Midterm 11:30-1:30 Content: - Chapters 1-4 (Readings and Lectures) - Labs 1 and 2 Format: - All multiple choice o Calculated multiple choice (calculations)  CE, VE, etc. o Identify missing items in model or figure  Newell’s model o Fill in the blank All motor classification schemes (Gross-Fine) are continuous (not universal, objective, arbitrary, or valid) All motor skills are acquired: FALSE. (reflexes) LABORATORY 2 Which of the following statistical variables is best associated with Variable Error? Standard Deviation By comparing the CE, AE, and ACE for both aces, what condition is necessary for all three measures to be the same? All shots directed in the same positive quadrant or side of the grid. Absolute error is the closest value to standard deviation. Considering the performance on each axis, do you think you need to work more on aiming direction or amplitude? Why? - Largest VE Error - Endpoint distribution more difficult to adjust Identify three (3) practical situations in which you could use these measures to quantify performance and three (3) situations where such measures are not practical. - Example: Swimming  Difficult to establish desired angle and real error - Establish discrete moments in trajectory where you want to assess variable error and constant error. - Easier for a discrete skill than a continuous skill to calculate Chapter 4 Movement Production and Motor Programs - Most applicable model is Motor Programs. Rationale - Separate motor plans? 1) Storage - Attempting to throw a ball at 5 m  prepare series of muscle contractions. o Adams (1971)  suggested that one must put together a motor plan o Do you need to store a motor program for every distance that you must throw? o All the motor programs would have to be stored in the hippocampus! How do we store it all? 2) Novelty - How do we generate new actions? Schmidt’s Theory (1975) - Generalized Motor Program (GMP) o Shared by each class of movement o Store a single GMP and use for all distance that you can throw, etc. o Set of parameters specified prior to each trial (ex. Force and Angle) - Schmidt published a theory (figure) o Initial conditions + desired outcome  will pick up desired GMP  put in right specifications (what angle? What force?)  This creates unique motor program. o Motor program limbs (giving proprioceptive feedback)  environment (exteroception; do I see the ball going where I want it to go?)  Were the proprioceptive and exteroceptive feedback the same as the desired outcome?  EXP PFB (proprioceptive feedback) and EXP EFB (exteroceptive feedback)  FEEDBACK = LEARNING (can use both PFB and EFB) o Pulling the right GMP for the initial conditions is easy. What is more difficult is parameterizing the GMP! Review of Schmidt’s Theory Initial conditions, desired outcome Selection of motor schema (GMP) Execution of motor program Limbs are moving (produce PFB) Environment reacts (EFB) We measure the outcome (** IT IS NOT ONLY OPEN LOOP. There are some closed-loop processes. BUT, the theory is not 100% established at this point in re
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