Connectionists: AI at 50 videos...

[Danny Silver]

Barack ..  You are certainly correct that this is a theory based partially on introspection to fill in the gaps.  However, you can see in the paper that there is some supportive evidence.   And I most certainly agree with your last paragraph.
.. Danny

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________________________________
From: Barak A. Pearlmutter <barak at pearlmutter.net>
Sent: Thursday, 27 August 2026 07:38:08
To: Danny Silver <danny.silver at acadiau.ca>
Cc: Grossberg, Stephen <steve at bu.edu>; connectionists at mailman.srv.cs.cmu.edu <connectionists at mailman.srv.cs.cmu.edu>
Subject: Re: Connectionists: AI at 50 videos...

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Dear Danny,

As I say in the original email  -  what we learn and reason about is to some extent constrained/shaped by the “lexicon” of symbols (and their related concepts) that we have already created or learned from others.   Humans initially developed the ability to manipulate symbolic representations to communicate their thoughts to each other - it was necessary to survive.   But then a beautiful thing happened,  we started using the symbols and this language of thought to constrain/rationalize our thinking - or at least, at the best of times we do so {:]).

This is coming from what I'd call the "introspection" strategy for figuring out how cognition works. How do we know the above? Through introspection. We can just feel those symbolic gears turning in our own heads. Bilingual subjects report switching which language they're thinking in. Etc.

Despite its intuitive appeal, I'm not convinced that introspection is a reliable methodology for doing cognitive science.

To give an example, even within what we classify as "symbolic" things like speech, people push continuous quantities onto the channel: volume, speed, cadence, mimicry of accents, deliberately slurred utterances, facial expressions, etc. We do this even in written forms. Handwriting is more expressive than typed. Even in typeset text, people use \emph{...} and \textbf{...} and manipulate size and \raisebox{...}{...}. It's as if there are highly intricate and complex underlying continuous quantities being compressed into a mostly-quantized channel.

My working hypothesis would be that, even when symbolic things are happening in the brain, the discrete structures are engulfed in a cloud of associated probability distributions whose representation and manipulation dwarf, in computational terms, the tiny kernel of purely symbolic structure.

Cheers,

--Barak.

On Thu, 27 Aug 2026 at 02:53, Danny Silver <danny.silver at acadiau.ca<mailto:danny.silver at acadiau.ca>> wrote:
Dear Barak and Hello Stephen (great to speak with you again).
I have attached my original email below for Stephen, as the last word from the connectionist mail list was that my original email  was pending approval by the moderator.  So not sure how Stephen saw your response but perhaps not my original email.
Anyway .. the miracles of the web.

Barak .. I like your paraphrase - you definitely get the major idea of the paper.  Young children develop conceptual representations of things like mother, food they like, asking for more, or things that scare them - long before they have symbols for these concepts.  But once they have learned such symbols (hand gestures or words) they can use them to communicate about these concepts to others, in albeit limited ways.   Many lower-order animals seem to function in the world very well - learning about and using concepts - without ever having need for symbols.  So symbols are not required to think, but they make explaining our thinking to others possible (kind of).

But please note,  there is a more subtle second idea that you may have missed.  Animals (particularly humans) who use symbols to communicate externally with each other also gained the advantage of creating an additional constraint (a bias) for learning and reasoning about the world.  As I say in the original email  -  what we learn and reason about is to some extent constrained/shaped by the “lexicon” of symbols (and their related concepts) that we have already created or learned from others.   Humans initially developed the ability to manipulate symbolic representations to communicate their thoughts to each other - it was necessary to survive.   But then a beautiful thing happened,  we started using the symbols and this language of thought to constrain/rationalize our thinking - or at least, at the best of times we do so {:]).

Stephen, I can see how an ART network can develop conceptual feature representations, say for the concept <cat> from photos, before the network becomes aware of a symbol for <cat>, such as the spoken word “cat".   The associated recognition category may simply not have a label.   But how is this conceptual representation later bound to a symbolic representation for the evert symbol “cat”.  This is particularly messy when one considers there are all kinds of cats - ones you can pet, one that can eat you.
Perhaps through the simultaneity of both seeing a cat and also hear the word “cat”.  Hmmm … Very interesting.


… Danny

From: Grossberg, Stephen <steve at bu.edu<mailto:steve at bu.edu>>
Date: Wednesday, August 26, 2026 at 3:58 PM
To: Barak A. Pearlmutter <barak at pearlmutter.net<mailto:barak at pearlmutter.net>>, Danny Silver <danny.silver at acadiau.ca<mailto:danny.silver at acadiau.ca>>
Cc: connectionists at mailman.srv.cs.cmu.edu<mailto:connectionists at mailman.srv.cs.cmu.edu> <connectionists at mailman.srv.cs.cmu.edu<mailto:connectionists at mailman.srv.cs.cmu.edu>>
Subject: Re: Connectionists: AI at 50 videos...

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Evert recognition category provides a “ symbolic “ representation of the distributed feature pattern that it represents. A feature-category resonance links category and features into a bound state that enables conscious recognition of the features. My 2021 OUP book explains this in detail and provides lots of experimental support for it.

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From: Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu<mailto:connectionists-bounces at mailman.srv.cs.cmu.edu>> on behalf of Barak A. Pearlmutter <barak at pearlmutter.net<mailto:barak at pearlmutter.net>>
Sent: Wednesday, 26 August 2026 13:04:32
To: Danny Silver <danny.silver at acadiau.ca<mailto:danny.silver at acadiau.ca>>
Cc: connectionists at mailman.srv.cs.cmu.edu<mailto:connectionists at mailman.srv.cs.cmu.edu> <connectionists at mailman.srv.cs.cmu.edu<mailto:connectionists at mailman.srv.cs.cmu.edu>>
Subject: Re: Connectionists: AI at 50 videos...

Dear Danny,

If I might paraphrase that work, it seems to me the idea is that
"subsymbolic" signals are used for local processing, while symbolic
representations are used for longer distance communication, with
things becoming more symbolic the longer and narrower the channel:
from one brain region to another, from the brain to its future self,
from one brain to another.

That certainly seems consistent with the information bottleneck of
Tishby et al, where distributions fragment into clustered
representations as the bottleneck becomes more severe.

My only issue is that, if you measure things in a conventional
computer doing symbol processing, the individual bits on wires can be
seen as subsymbolic. What is bit 7 of register 4? What is bit 11 of
the address bus? It is not until you agglomerate things just right, at
just the right level of abstraction, that the crisp symbolic nature
of, say, a compiler running on a simple RISC, becomes apparent. This
subproblem seems to subsume the whole question, because it's basically
"figure out how it all works and then we can talk about whether there
are symbolic representations." This might not obviate the distinction
between symbolic and subsymbolic, but I'd argue that it does make it
uninteresting.

Cheers,

--Barak.
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