2018
Coordination
Gebruiker
[Geef de naam van het bedrijf op]
1-3-2018
, Coordination is a quite abstract concept. Senses play an important role to maintain coordination. Senses gives information.
Afferent and efferent information is called the sensorimotor loop (or perception action coupling).
The CNS seems able to control coordination very accurate, but it is not very well understood by researchers. A few concepts
of ways how the CNS controls coordination have been stated. The 5 concepts described below are the leading concepts on
how coordination is controlled±
- Bayesian theory
- Ecological approach
- Computational approach
- Event coding (TEC)
- Synergies
Bayesian Approach:
The Bayesian approach is a computational approach (top-down; sensory information needs to get meaning from the CNS).
Determining the appropriate motor command (like hitting a ball in tennis) is fundamentally a decision process. Two
components define the decision problem: knowledge of the state of the world (including our own body) and knowledge of
our objectives. The sensory inputs of humans are plagued by noise, which means that we will always have uncertainty
about our hand’s true location. This uncertainty is depended of the sensory input (when you use proprioception instead of
sight to locate your hand, the uncertainty might be bigger.). This uncertainty places the problem of estimating the state of
the world and the control of our motor system within a statistical framework. The Bayesian statistics provides a systematic
way of solving problems in the presence of uncertainty. Bayesian statistics defines how new information should be
combined with prior beliefs and how information from several modalities should be integrated. It defines how our beliefs
should be combined with our objectives to make optimal decisions. The cost of each movement (such as energy consumed)
must be weighed against the potential rewards that can be obtained by moving. The selection of a movement can be
described as the rational choice of the movement that maximizes utility according to the decision theory. The decision
theory quantifies how people should choose in the context of a given utility function and some partial knowledge of the
world.
We need to estimate the variables that are relevant for our choice of movement. For example, when playing tennis we may
want to estimate where the ball will bounce. Because vision does not provide perfect information about the ball’s velocity
there is uncertainty as to the bounce location. However, if we know about the noise in our sensory system then the sensory
input can be used to compute the likelihood. We can combine this with information that is available over repeated
experience of tennis: the position where the ball hits the ground is not uniformly distributed over the court. For example,
the bounce locations are likely to be concentrated within the confines of the court and the distribution might by highly
peaked near the boundary lines where it is most difficulty to return the ball. This distribution of positions is called the prior
(sketched in green). Prior depends on experience/knowledge
The Bayes rule defines how to combine prior and likelihood to make an optimal estimate of the bounce location.
Coordination
Gebruiker
[Geef de naam van het bedrijf op]
1-3-2018
, Coordination is a quite abstract concept. Senses play an important role to maintain coordination. Senses gives information.
Afferent and efferent information is called the sensorimotor loop (or perception action coupling).
The CNS seems able to control coordination very accurate, but it is not very well understood by researchers. A few concepts
of ways how the CNS controls coordination have been stated. The 5 concepts described below are the leading concepts on
how coordination is controlled±
- Bayesian theory
- Ecological approach
- Computational approach
- Event coding (TEC)
- Synergies
Bayesian Approach:
The Bayesian approach is a computational approach (top-down; sensory information needs to get meaning from the CNS).
Determining the appropriate motor command (like hitting a ball in tennis) is fundamentally a decision process. Two
components define the decision problem: knowledge of the state of the world (including our own body) and knowledge of
our objectives. The sensory inputs of humans are plagued by noise, which means that we will always have uncertainty
about our hand’s true location. This uncertainty is depended of the sensory input (when you use proprioception instead of
sight to locate your hand, the uncertainty might be bigger.). This uncertainty places the problem of estimating the state of
the world and the control of our motor system within a statistical framework. The Bayesian statistics provides a systematic
way of solving problems in the presence of uncertainty. Bayesian statistics defines how new information should be
combined with prior beliefs and how information from several modalities should be integrated. It defines how our beliefs
should be combined with our objectives to make optimal decisions. The cost of each movement (such as energy consumed)
must be weighed against the potential rewards that can be obtained by moving. The selection of a movement can be
described as the rational choice of the movement that maximizes utility according to the decision theory. The decision
theory quantifies how people should choose in the context of a given utility function and some partial knowledge of the
world.
We need to estimate the variables that are relevant for our choice of movement. For example, when playing tennis we may
want to estimate where the ball will bounce. Because vision does not provide perfect information about the ball’s velocity
there is uncertainty as to the bounce location. However, if we know about the noise in our sensory system then the sensory
input can be used to compute the likelihood. We can combine this with information that is available over repeated
experience of tennis: the position where the ball hits the ground is not uniformly distributed over the court. For example,
the bounce locations are likely to be concentrated within the confines of the court and the distribution might by highly
peaked near the boundary lines where it is most difficulty to return the ball. This distribution of positions is called the prior
(sketched in green). Prior depends on experience/knowledge
The Bayes rule defines how to combine prior and likelihood to make an optimal estimate of the bounce location.
