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10.18.2009 - Moving in an uncertain world - Daniel Wolpert

  • Movement is the only we have of interacting with the world
    • "The only reason we have a brain is to be able to move"
  • Level of dexterity is much different between "robots" and humans.
  • There is noise and variability in:
    • Sensors
    • Motor output
    • Task
  • Decoding motor uncertainty
    • Bayesian learning
      • Beliefs are updated based on combing "data" with stored information
      • Bayes rule
        • P(A|B) = P(B|A) * P(A) / P(B)
      • P(state) = probability based on prior knowledge
      • P(sensory input| state) = likelihood of the sensory state matching a prior state
      • P(state|sensory input) = motivation or future prediction of knowledge
    • Predicting the consequences of action
      • Required to account for:
        • Control for delays
        • Mental simulation
        • Likelihood estimation
          • Do many predictions in parallel
        • Sensory filtering
          • Two types of information
            • Changes in the outside world
            • Changes that we cause - we can get that from the efference copy
          • We ignore changes that we cause
            • Showed throw a "tickle" experiment
              • The coupling between spatial and temporal sensory information is tightly coupled to whether the information is considered "self-produced"
            • Tit-for-tat experiment
              • Trying to match forces
              • The sensed force appears stronger than the force applied, which suggests that we subtract off our own actions.
            • Schizophrenia subjects may have difficulty in predicting what their own level of determining if forces are generated by themselves or from external sources.
    • Loss functions in movement
      • Possible loss functions
        • Only a hit matters
        • Error to some power
      • Turns out that the loss function appears to be mostly quadratic for small errors but linear for large errors
    • Optimal movements
      • Stereotypical motions are recognizable
      • How do we figure out which movements are "best"?
        • Signal dependent noise leads to a distribution of possible movements
        • The optimal movement is then the one that has the least variability in the end-point.
    • Decisions and changes of mind
      • Why do you change your mind once you have begun a particular motion?
        • This can be explained by estimating the integration of information as a random-walk
          • The extension that can explain for the change in movement by changing the boundary of decision during the delay of the motor command.
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