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All,<br>
<br>
I have bit of a bone to pick, if you don't mind.<br>
<br>
Why is the question of how concepts relate to neurons so
scandalously incoherent, even after this field has been talking
about it for at least 35 years?<br>
<br>
In particular, why do so many papers - multiple thousands, by now
- in the neuroscience/connectionist nexus make the naive
assumption that a number of neurons (one neuron, a cluster, a
distributed cluster, a sparse group) will correspond to one
concept? So much so that when those neurons light up in an fMRI,
or are active electrically, people declare that the concept must
be 'there'?<br>
<br>
We have known for a long time that such a naive correspondence has
serious problems. (I say 'long time' because I remember
discussing the problems with John Taylor in 1981, and certainly
Donald Norman raised them in his chapter at the end of the two PDP
volumes) For example, what happens when I create a brand new
concept and name it? Let's say I decide to give the name
"skandulupper" to the concept of "watching all of Tuesday Weld's
movies in one week": does the human brain suddenly manage to find
a single neuron or a cluster that just happen to have all the
right connections to all the phoneme neurons for that word, and to
the concept neurons for "Tuesday Weld", and "movie" and "week"? <br>
<br>
(Notice that if the new concept is only hanging around in
transient (working memory) storage, with permanent storage in long
term memory being assigned during some kind of overnight
WM->LTM consolidation, that really only postpones the awkward
questions about how the right neurons, with the right connections,
are located.)<br>
<br>
And if the answer to that awkward question about new concepts is
"Duh! Distributed representations, of course!", then what do we
do about the next awkward question? You know the one: if there
is a distributed representation for a concept like, say "cook",
then how does the system represent a sentence like "Cook was a
good cook, as cooks go; but as good cooks go, Cook went."?<br>
<br>
The problem is, that there has never been a good answer to these
issues, so what the community seems to have done instead is to
write a thousand papers describing various neuroscience findings
AS IF the representations are pretty much localist, or sparse, or
even grandmotherly. Why is it acceptable to publish papers as if
a KNOWN FAILED MODEL is the one that is assumed to be valid? The
whole community seems to be suffering from a collective delusion.<br>
<br>
Let's try to be absolutely clear, here. Localist, semi-localist,
sparse, and distributed representations do not work as accounts of
actual cognition, unless they are used in "toy" models that do
nothing more than a fantasy version of what we know the real brain
does. Let's not pretend that just because a lot of people play
with toy models, and a lot of people pretend that the toy models
mean something, that somehow that changes the reality. This is
especially true of "reinforcement learning" models that claim to
show that the brain does RL -- a close examination of these claims
hinge on a sudden bait and switch from real neural wiring to a toy
model applied to trivial data, which could not possibly scale up.<br>
<br>
Now, it so happens that there really is a way to imagine a
solution to this problem, at the theoretical level. One simply
has to accept that the neural machinery is set up in such a way
that the structures corresponding to concepts are neither localist
nor distributed, but virtual. That means that concepts are to the
neural hardware what programs in a computer network system are to
the physical computers -- concepts are allowed to have two states
(active and dormant) and when active the concepts have most of the
properties of an old-school symbol processing system. Any version
of this virtual-concept idea would solve the problems inherent in
assuming that neurons correspond to concepts.<br>
<br>
Notice that we do not have to produce a fully-functional
simulation of such a virtual-concept system, for it to be a valid
competitor to localist, semi-localist, sparse, and distributed
representations. The latter are known failures, and "simulations"
of them are invariably toy models that cannot scale up. In that
context, it is enough to point out that the basic properties of a
virtual-concept type of model are already superior to the idea
that representations are localist, semi-localist, sparse, or
distributed. Calling something a "virtual-concept" system is
almost the same as saying that it is a variety of symbolic system
... and there were many dozens of symbolic systems in the 1970s,
80s and 90s that were fully implemented, and that clearly did not
suffer the problems shown by localist, semi-localist, sparse, and
distributed neural representations.<br>
<br>
--<br>
<br>
I have a personal reason for raising this question.<br>
<br>
Trevor Harley and I tried to raise this question in a paper we
wrote in 2010 [1]. Long story short, we said "Look, any kind of
virtual-concept system is the NEXT SIMPLEST theoretical construct
up from the two known failed constructs (localist and
distributed), so let's see what would happen if some version of
that virtual-concept idea turns out to be the way things are." In
particular, we asked two questions: (1) Would the arguments and
conclusions in a selection of popular brain-imaging/neuroscience
papers actually make any sense, if the brain really was using this
next-best type of system?, and (2) Would the virtual-concept idea
give a better account of any of these published neuroscience
results?<br>
<br>
We expanded a little on what the virtual-concept idea might
actually mean, so our readers would have a concrete feel for the
overall implications. Then we analysed the chosen set of papers.
Our conclusions were that most of the arguments in the papers fell
to pieces if virtual concepts were what the brain was doing, and
that the virtual-concept idea gave a better account of many of the
published results.<br>
<br>
And then this happened. Our paper was a book chapter, and in the
same book William Bechtel and Richard C. Richardson decided to go
on the offensive and trash every claim we tried to make. With
language like:<br>
<br>
"Loosemore and Harley [...] evidently do not think of this as a
serious model of cognition, but as a kind of toy structure [...]
The model is presented very sketchily, with no empirical backing
and no substantive constraints. Loosemore and Harley [...] offer
no detailed results to demonstrate that the model could
accommodate even the most accepted empirical results about
recognition or control. [...] This, however, is a cartoon of
serious science [...] If Loosemore and Harley were right, then
evolutionary biologists would need consistently to defend
themselves against Creationist contentions"<br>
<br>
Words cannot express my feelings, here. What kind of science is
this, when someone is compared to a Creationist for discussing a
model that, quite simply, is the only kind of model that has any
hope of getting out of the known problems of the toy models that
are currently accepted as the norm?<br>
<br>
<br>
Richard Loosemore.<br>
<br>
<br>
<br>
<br>
[1] Loosemore, R.P.W. & Harley, T.A. (2010). Brains and
Minds: On the Usefulness of Localization Data to Cognitive
Psychology. In M. Bunzl & S.J. Hanson (Eds.), Foundational
Issues in Human Brain Mapping. Cambridge, MA: MIT Press.<br>
<br>
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