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.
- Showed throw a "tickle" experiment
- Two types of information
- Required to account for:
- 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
- Possible loss functions
- 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.
- This can be explained by estimating the integration of information as a random-walk
- Why do you change your mind once you have begun a particular motion?
- Bayesian learning