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---
title: "Reading Notes: \"The Symbol Grounding Problem\", Stevan Harnad"
date: 2020-02-02
---
cite:harnad1990_symbol_groun_probl [[https://eprints.soton.ac.uk/250382/1/symgro.pdf][(PDF version)]] defined the /symbol
grounding problem/, which is one of the most influential issues in
natural language problems since the 1980s. The issue is to determine
how a formal language system, consisting in simple symbols, can be
imbued with any /meaning/.
From the abstract:
#+begin_quote
How can the semantic interpretation of a formal symbol system be made
/intrinsic/ to the system, rather than just parasitic on the meanings
in our heads? How can the meanings of the meaningless symbol tokens,
manipulated solely on the basis of their (arbitrary) shapes, be
grounded in anything but other meaningless symbols?
#+end_quote
In this landmark paper, Harnad makes the issue explicit, in its
context of cognitivism and competing theories of mind and
intelligence. He then proposes an original solution based on a
combination of symbolic and connectionist properties. The problem
itself is still highly relevant to today's NLP advances, where the
issue of extracting /meaning/ is still not solved.
* What is the symbol grounding problem?
** Context: cognitivism, symbolism, connectionism
/Behaviourism/ was the dominant framework of experimental psychology in
the first half of the 20th century. It grounded psychology firmly in
an empirical setting, arguing that mental events are not observable,
and that only external behaviour can be studied
citep:miller2003_cognit_revol.
In the 1950s, new theories, in particular Chomsky's theories in
linguistics, started to question this approach and highlighted its
limitations. /Cognitivism/ arose as a way to take into account
internal mental states. It allowed scientists to make hypotheses about
unobservable phenomenons, provided they made predictions testable in
an experimental setting.
Harnad defines a /symbol system/ as a set of arbitrary token with
explicit rules (also in the form of tokens or strings of tokens) to
combine them. Note that the set of rules should be explicit and not
defined as posteriori, because nearly every phenomenon can be
interpreted as following a set of rules.
An additional (and most relevant for us) property of symbol systems is
that they are /semantically interpretable/: we can associate a meaning
in a systematic fashion to every token or string of tokens.
This exposes /symbolism/, i.e. the view that cognition is a symbolic
system. The alternative view, /connectionism/, has its root in
biological models of the brain, and posits that the network of
connections in the brain is what defines cognition, without any formal
symbol system.
#+begin_quote
According to connectionism, cognition is not symbol manipulation but
dynamic patterns of activity in a multilayered network of nodes or
units with weighted positive and negative
interconnections. citep:harnad1990_symbol_groun_probl
#+end_quote
One common criticism of connectionism is that it does not meet the
compositionality criterion. Moreover, we cannot give a semantic
interpretation of connectionist patterns in a systematic way as we can
in symbolic systems. This issue was recently raised again by Gary
Marcus in his recent book /Rebooting AI/
citep:marcus2019_reboot_ai. Human cognition makes extensive use of
internal representations. Chomsky's theories on the existence of a
"universal grammar" is a good example of such internal structure for
linguistics. These cognitive representations seem to be highly
structured (as demonstrated by the work of Kahneman and Tversky
citep:kahneman2011_think_fast_slow), and compositional. (See also his
[[https://thegradient.pub/an-epidemic-of-ai-misinformation/][recent article]] in /The Gradient/.)
#+CAPTION: Connectionism versus symbol systems (Taken from cite:harnad1990_symbol_groun_probl.)
|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| *Strengths of connectionism:* |
| 1) Nonsymbolic Function: As long as it does not aspire to be a symbol system, a connectionist network has the advantage of not being subject to the symbol grounding problem. |
| 2) Generality: Connectionism applies the same small family of algorithms to many problems, whereas symbolism, being a methodology rather than an algorithm, relies on endless problem-specific symbolic rules. |
| 3) "Neurosimilitude": Connectionist architecture seems more brain-like than a Turing machine or a digital computer. |
| 4) Pattern Learning: Connectionist networks are especially suited to the learning of patterns from data. |
|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| *Weaknesses if connectionism:* |
| 1) Nonsymbolic Function: Connectionist networks, because they are not symbol systems, do not have the systematic semantic properties that many cognitive phenomena appear to have. |
| 2) Generality: Not every problem amounts to pattern learning. Some cognitive tasks may call for problem-specific rules, symbol manipulation, and standard computation. |
| 3) "Neurosimilitude": Connectionism's brain-likeness may be superficial and may (like toy models) camoflauge deeper performance limitations. |
|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| *Strengths of symbol systems:* |
| 1) Symbolic Function: Symbols have the computing power of Turing Machines and the systematic properties of a formal syntax that is semantically interpretable. |
| 2) Generality: All computable functions (including all cognitive functions) are equivalent to a computational state in a Turing Machine. |
| 3) Practical Successes: Symbol systems' ability to generate intelligent behavior is demonstrated by the successes of Artificial Intelligence. |
|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| *Weaknesses of symbol systems:* |
| 1) Symbolic Function: Symbol systems are subject to the symbol grounding problem. |
| 2) Generality: Turing power is too general. The solutions to AI's many toy problems do not give rise to common principles of cognition but to a vast variety of ad hoc symbolic strategies. |
|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
** Exposing the issue: thought experiments
* What the human mind does, and what AIs could do
* References