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OVER 98% PASS RATE FOR THE NCS, OCS, AND PCS EXAMS forums NCS Advantage Error Based Learning vs Reinforcement learning

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    • #489505
      Karen C
      Participant

      I’d love to have a real-world, treatment examples for Error Based Learning and Reinforcement learning. I want to make sure my understanding is correct. (This was in the Cerebellum section, slide # 27 and 28)

    • #489506
      Karen C
      Participant

      Also an example of “closed loop reinforcement learning” would be great

    • #489507
      Karen C
      Participant

      And what is meant by motor noise? (Sorry for all the questions!!)

      • #489527

        Variability in movements from trial to trial can be attributed to motor noise and exploration variability. Exploration variability are conscious attempts to modify movements to be more successful. Individuals are generally unaware of their motor noise. Individuals with cerebellar dysfunction have more motor noise than normal controls (due to ataxia/incoordination). Performance tends to decline with increasing motor noise.

    • #489508
      Karen C
      Participant

      Also, I was wondering what type of “whole body movements” were used to “train trunk limb coordination”. It sounds like LSVT large amplitude type movements. Is this what they mean? I’d love more details on the treatments. (This is from the Ilg W study)
      Thank you for all your help and answering questions!

    • #489526

      Hi Karen,
      Error-based learning requires feedback about error data to allow the learner to make corrections on a trial by trial basis. If you recall our motor learning module, error-based learning requires knowledge of performance and often includes concurrent feedback. In the clinic, this might look like using biofeedback about weight shifting on a forceplate to control a figure on a computer monitor. It could also be throwing Velcro balls at a target where the learner can see how far their attempts land from the bullseye.

      Reinforcement learning involves only knowledge of results (success or failure) and terminal feedback that forces the learner to explore options to facilitate more successful outcomes. In the clinic, this might look like throwing a ball at a target and the learner only knowing whether they hit the target (success) or not (failure).

      Closed loop reinforcement learning means that task difficulty is controlled based on recent performance. In the ball-throwing example, this might mean moving the target to make it easier or harder to hit based on the patient’s recent attempts.

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