Researchers in the fields of cognitive neuroscience and Artificial Intelligence are fond describing the brain as a highly sophisticated computational device in which data is encoded and processed. Prior to the invention and development of computers many of the most prominent theories of the "workings" of the brain assumed either that it was minutely mechanical in its functioning (tiny cogs and pulleys, weights and levers) or else – or also - that the physical matter of the brain was simply some form of interface between the brain and the non-physical mind. This “Dualistic” theory, as it came to be known, is most closely associated with the 17th Century French philosopher Rene Descartes.
Since
the 17th Century, advances in scientific understanding have enabled
us to be a great deal more certain that the mind is entirely physical in origin,
yet vestiges of Cartesian Dualism continue to linger in many quarters despite the
overwhelming evidence that consciousness – the mind - is a product of
electrochemical processes occurring in – or largely in - the brain.
Throughout
history, many differing conceptions of the origins of thought and
intelligent agency have stocked our vocabulary with a rich variety of metaphors
borrowed from a range of objects, activities and occupations. We talk of the
mechanisms of the mind, of streams of consciousness and information flows, of
brain cells lighting up, sparking and firing, of cascades, filters, causal
chains and neural triggers.
Such
terms are extremely useful and often unavoidable in formulating theories and
characterising barely understood processes. But characterisations can also
present significant difficulties, especially if taken too literally as
conceptual models. When attempting to understand complex and currently unexplained
phenomena we are inevitably restricted both by what we know and what we don't
know. Our knowledge and the terminology we have to describe it frames our thinking in ways that do not always help to illuminate the issues or processes we seek to understand. The same was true for Descartes,
and is no doubt the reason he rejected the mechanical model of mind in favour of a non-physical account. It is perhaps
possible then, to see how the versatility and character of the analogies available to us can significantly
influence the concepts we are likely to formulate and the conclusions we are likely to draw during our exploration of unfamiliar processes and phenomena.
Since
the 1970's Deirdre Gentner’s research has focussed on the use of analogical
reasoning in the domains of cognitive science and artificial intelligence in particular.
Gentner’s findings show that “analogical access and analogical inference are governed by very different rules.” In other words, when we make comparisons we have a tendency to pick out
superficially accessible analogies instead of analogies that might offer
greater predictive potential. I think this is precisely what is happening when
the functioning of the brain is explained by way of computational analogies. Even
more worrying though is the assumption – frequently made by theorists and
scientists - that brains necessarily utilise encoded representations. I would
argue that the failure to recognise the incoherence of this analogy is having a
significantly detrimental influence on the development of both cognitive
neuroscience and artificial intelligence.
In their
1995 book “Foundational Issues in Artificial Intelligence and Cognitive Science” Mark Bickhard and Loren
Terveen argue that the fields of Cognitive Science and Artificial Intelligence
are “in the midst of a programmatic impasse.” They make the vital point that
this impasse cannot be overcome by making changes – no matter how insightful -
at the “project level.” That is to say; if the foundations aren’t sound, no
amount of work above ground is ever likely to resolve the initial errors - in
fact it is far more likely to compound them.
Bickhard and Terveen coin the term “Encodingism” to describe the assumption that cognition involves the use of representational encodings. They write:
“It is assumed that the
symbol represents a particular thing, and that it - the symbol - somehow
informs the system of what that symbol is supposed to represent. This is a
fatal assumption in spite of its seeming obviousness.”
The
point they are making is that it is by no means straightforward what any symbol
is actually intended to symbolise. As C. S.
Peirce theorised at the end of the 19th Century, symbols represent their
denoted objects as a result of rules or interpretive habits that are independent of any properties that may or may not be shared between symbols and the objects they represent. Symbols are things that require interpretation. They do not lead immediately and
without complication to the things they denote. The rules and habits of symbol use
have to be learned. If this is true, then it is extremely doubtful that an
intelligence could emerge capable of interpreting symbols without a prior
non-symbol-using evolution. And if intelligence is possible without symbol use, then no encoded symbols are required for cognition.
