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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