Okay, below are three passages that I think give a good sense of what
I mean by "information" when I say that "consciousness is
information".  The first is from David Chalmers' "Facing up to the
Problem of Consciousness."  The second is from the SEP article on
"Semantic Conceptions of Information", and the third is from "Symbol
Grounding and Meaning:  A comparison of High-Dimensional and Embodied
Theories of Meaning", by Arthur Glenberg and David Robertson.

So I'm looking at these largely from a static, timeless, platonic
view.  In my view, there are ungrounded abstract symbols that acquire
meaning via constraints placed on them by their relationships to other
symbols.  The only "grounding" comes from the conscious experience
that is intrinsic to a particular set of relationships.  To repeat my
earlier Chalmers quote, "Experience is information from the inside;
physics is information from the outside."  It is this subjective
experience of information that provides meaning to the otherwise
completely abstract "platonic" symbols.

So I think that something like David Lewis' "modal realism" is true by
virtue of the fact that all possible sets of relationships are
realized in Platonia.

Note that I don't have Bruno's fear of white rabbits.  Assuming that
we are typical observers is fine as a starting point, and is a good
way to choose between otherwise equivalent explanations, but I don't
think it should hold a unilateral veto over our final conclusions.  If
the most reasonable explanation says that our observations aren't
especially typical, then so be it.  Not everyone can be typical.

I think the final passage from Glenberg and Robertson (from a paper
that actually argues against what's being described) gives the best
sense of what I have in mind, though obviously I'm extrapolating out
quite abit from the ideas presented.

Okay, so the passages of interest:

--

David Chalmers:

The basic principle that I suggest centrally involves the notion of
information. I understand information in more or less the sense of
Shannon (1948). Where there is information, there are information
states embedded in an information space. An information space has a
basic structure of difference relations between its elements,
characterizing the ways in which different elements in a space are
similar or different, possibly in complex ways. An information space
is an abstract object, but following Shannon we can see information as
physically embodied when there is a space of distinct physical states,
the differences between which can be transmitted down some causal
pathway. The states that are transmitted can be seen as themselves
constituting an information space. To borrow a phrase from Bateson
(1972), physical information is a difference that makes a difference.

The double-aspect principle stems from the observation that there is a
direct isomorphism between certain physically embodied information
spaces and certain phenomenal (or experiential) information spaces.
>From the same sort of observations that went into the principle of
structural coherence, we can note that the differences between
phenomenal states have a structure that corresponds directly to the
differences embedded in physical processes; in particular, to those
differences that make a difference down certain causal pathways
implicated in global availability and control. That is, we can find
the same abstract information space embedded in physical processing
and in conscious experience.

--

SEP:

Information cannot be dataless but, in the simplest case, it can
consist of a single datum.  A datum is reducible to just a lack of
uniformity (diaphora is the Greek word for “difference”), so a general
definition of a datum is:

The Diaphoric Definition of Data (DDD):

A datum is a putative fact regarding some difference or lack of
uniformity within some context.  [In particular data as diaphora de
dicto, that is, lack of uniformity between two symbols, for example
the letters A and B in the Latin alphabet.]

--

Glenberg and Robertson:

Meaning arises from the syntactic combination of abstract, amodal
symbols that are arbitrarily related to what they signify.  A new form
of the abstract symbol approach to meaning affords the opportunity to
examine its adequacy as a psychological theory of meaning.  This form
is represented by two theories of linguistic meaning (that is, the
meaning of words, sentences, and discourses), both of which take
advantage of the mathematics of high-dimensional spaces. The
Hyperspace Analogue to Language (HAL; Burgess & Lund, 1997) posits
that the meaning of a word is its vector representation in a space
based on 140,000 word–word co-occurrences. Latent Semantic Analysis
(LSA; Landauer & Dumais, 1997) posits that the meaning of a word is
its vector representation in a space with approximately 300 dimensions
derived from a space with many more dimensions. The vector elements
found in both theories are just the sort of abstract features that are
prototypical in the cognitive psychology of meaning.

Landauer and Dumais also apply LSA to sentence and discourse
understanding. A sentence is represented as the average of the vectors
of the words it contains, and the coherence between sentences is
predicted by the cosine of the angle (in multidimensional space)
between the vectors corresponding to successive sentences.  They claim
that LSA averaged vectors capture “the central meaning” of passages
(p. 231).

Consider a thought experiment (adapted from Harnad, 1990, and related
to the Chinese Room Argument) that suggests that something critical is
missing from HAL and LSA. Imagine that you just landed at an airport
in a foreign country and that you do not speak the local language. As
you disembark, you notice a sign printed in the foreign language
(whose words are arbitrary abstract symbols to you). Your only
resource is a dictionary printed in that language; that is, the
dictionary consists of other arbitrary abstract symbols. You use the
dictionary to look up the first word in the sign, but you don’t know
the meaning of any of the words in the definition.  So, you look up
the first word in the definition, but you don’t know the meaning of
the words in that definition, and so on. Obviously, no matter how many
words you look up, that is, no matter how many structural relations
you determine among the arbitrary abstract symbols, you will never
figure out the meaning of any of the words. This is the symbol
grounding problem (Harnad, 1990): To know the meaning of an abstract
symbol such as an LSA vector or an English word, the symbol has to be
grounded in something other than more abstract symbols.

Landauer and Dumais summarize the symbol grounding problem by noting,
“But still, to be more than an abstract system like mathematics words
must touch reality at least occasionally” (p. 227). Their proposed
solution is to encode, along with the word stream, the streams from
other sensory modalities. “Because, purely at the word–word level,
rabbit has been indirectly preestablished to be something like dog,
animal, object, furry, cute, fast, ears, etc., it is much less
mysterious that a few contiguous pairings of the word with scenes
including the thing itself can teach the proper correspondences.
Indeed, if one judiciously added numerous pictures of scenes with and
without rabbits to the context columns in the encyclopedia corpus
matrix, and filled in a handful of appropriate cells in the rabbit and
hare word rows, LSA could easily learn that the words rabbit and hare
go with pictures containing rabbits and not to ones without, and so
forth” (p. 227). Burgess and Lund (1997) offer a similar solution, “We
do think a HAL-like model that was sensitive to the same
co-occurrences in the natural environment as a human language learner
(not just the language stream) would be able to capitalize on this
additional information and construct more meaningful representations”
(p. 29).

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