No subject

  some comments concerning your posting:

> 
> What are the real implications of this study? One of the most
> important facts is that although both groups had identical
>training  sessions, they had different levels of learning 
>of the motor task because of what they did subsequent to
>practice. From this fact  alone one can conclude with some
>degree of certainty that real-time, instantaneous learning
>is not used for learning motor skills.
....
> So real-time, instantaneous and permanent
>weight-adjustment
> (real-time learning) is contradictory to the results here.

I do not get your point. Assume both groups learned the same level
of performance. Now they subsequently do something else. One group
learns a new motor skill which interfers with the previously
learned motor skill in short term motor memory. The other
groups does unrelated tasks (clearly nothing comparable to
Reza's manipulandum task), and this group does not have
interference with the short term memory. Why does this
exclude real-time learning? The consolidation process later
to put STM into LTM is not relevant to this questions.

> Second, from a broader behavioral perspective, all types of
> "learning" by the brain involves collection and storage of
> information prior to actual learning. As is well known, the
> fundamental process of learning involves: (1) collection and
> storage of information about a problem, (2) examination of the
> information at hand to determine the complexity of the 
>problem, (3)development of trial solutions (nets) for the
>problem, (4)
>testing of trial solutions (nets), (5) discarding such 
>trial solutions (nets) if they are not good enough, and 
>(6) repetition of these processes until an acceptable
>solution is found.  Real-time learning is not compatible
>with these learning processes.

Why would you make this statement about the brain? Nobody really
understands how learning in the brain works, and just because
the neural network community has this procedure to deal with
the bias-variance dilemma, I would not believe that this is the
only way to achieve good learning results. We actually worked
on a learning algrithm for a while which can achieve incremental
learning without all these steps you enumerated. All what it
needed is a smoothness bias.
(ftp://ftp.cc.gatech.edu/pub/people/sschaal/schaal-NC97.ps.gz)

> One has to remember that the essence of learning is
>generalization. In order to generalize well, one has to 
>look at the whole body of information relevant to a
>problem, not just bits and pieces of the information at a
>time as in real-time learning. So the argument against
>real-time learning is simple: one cannot
>learn (generalize) unless one knows what is there to learn
>(generalize). One finds out what is there to learn  
>(generalize) by collecting and storing information about
>the problem. In other
>words, no system, biological or otherwise, can prepare 
>itself to learn (generalize) without having any 
>information about what is to be learnt (generalized).

You are right in saying that one needs prior information for
generalization. However, there are classes of problems where
general priors will be sufficient to generalize. Nobody can
do extrapolation without have strong domain knowledge. But
you might be able to do save interpolation with some 
generic biases, which nature may have developed. Again, the
above paper talks about related topics.

> Another fact from the study that is highly significant is 
>that the brain takes time to learn. Learning is not quick 
>and instantaneous.

But this may depend on the task. Other tasks can be 
acquired more quickly. I assume it is save to say that the
biological system is only able to learn certain tasks very
quickly, and others not. This is why playing good golf or
tennis is so hard. But learning to balance a pole happens
quite quickly in humans.

Interesting arguments, but I do not see how you can make 
any of your claims about real-time learning. What is your
counter-hypothesis? Pure memory-based learning?

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