cognition and biology

[James L. McClelland]

Hideyuki Cateau <cateau at tkyux.phys.s.u-tokyo.ac.jp> writes:

>   I believe that it is really meaningful as a study of the brain that we 
> examine whether the existing neural network models can explain the many 
> other psychological experiments.

I hope by now nearly everyone agrees that it is very valuable to try
to understand which robust phenomena of human cognition depend on
which properties of the underlying mechanisms.  Sometimes very
abstract and general features that connectionist systems share with
other systems are doing the work; other times it is going to turn out
to be specific features not shared by a wide range of abstract models.
The power law appears to be a case of the former, since it has
probably been accounted for by more psychological models than any
other phenomenon.

There are clear cases in which listening to the brain has made a
difference to our understanding at the abstract level.  The phrase
listening to the brain actually comes from a paper of Terry
Sejnowski's in which he pointed out the stochastic character of neural
activity.  This observation contributed importantly to the development
of the Boltzmann machine.  More recently I have found that another
robust regularity of psychological performance, called the
independence law, arises from neural network models with
bi-directional (symmetric) connections.  This does not occur if the
network uses the deterministic activation function of McClelland and
Rumelhart's interactive activation model but it does occur if the
network uses any of a wide range of stochastic activation functions,
including the Boltzmann machine activation function and various
continuous diffusion functions.  These kinds of discoveries make it
clear that abstraction is of the essence of understanding, but they
also make it clear that it is important to abstract the right things.
To me this argues forcefully for an interactive style of research, in
which both details and abstractions matter.

-- Jay McClelland