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Mechanization of Cognition 79

established by the previous sentence. The architecture of Figure 3.3 generalizes to using larger
bodies of context for a variety of cognition processes.
Even more abstract levels of representation of language meaning are possible. For example,
after years of exposure to language and co-occurring sensory and action representations, lexicons
can form that represent sets of commonly encountered lower-abstraction-level symbols. Via the
SRE mechanism (a type of thought process), such symbols take on a high level of abstraction, as
they become linked (directly, or via equivalent symbols) to a wide variety of similar-meaning
symbol sets. Such symbol sets need not be complete to be able to (via confabulation) trigger
activation of such high-abstraction representations. In language, these highest-abstraction-level
symbols often represent words! For example, when you activate the symbol for the word joy, this
can mean joy as a word, or joy as a highly abstract concept. This is why in human thought the most
exalted abstract concepts are made specific by identifying them with words or phrases. It is also
common for these most abstract symbols to belong to a foreign language. For example, in English
speaking lands, the most sublime abstract concepts in language are often assigned to French, or
sometimes German, words or phrases. In Japanese, English or French words or phrases typically
serve in this capacity.
High-abstraction lexicons are used to represent the meaning content of objects of the
mental world of many types (language, sound, vision, tactile, etc.). However, outside of the
language faculty, such symbols do not typically have names (although they are often strongly
linked with language symbols). For example, there is probably a lexicon in your head with a
symbol that abstractly encodes the combined taste, smell, surface texture, and masticational feel
of a macaroon cookie. This symbol has no name, but you will surely know when it is being
expressed!

3.3.5 Discussion

A key observation is that confabulation architectures automatically learn and apply grammar, and
honor syntax; without any in-built linguistic structures, rules, or algorithms. This strongly suggests
that grammar and syntax are fictions dreamed up by linguists to explain an orderly structure that is
actually a requirement of the mechanism of cognition. Otherwise put, for cognition to be able, given
the limitations of its native machinery, to efficiently deal with language, that language must have a
structure which is compatible with the mathematics of confabulation and consensus building. In this
view, every functionally usable human language must be structured this way. Ergo, universal
appearance of some sort of grammar and syntactic structure in all human languages.
Thus, Chomsky’s (1980) famous long search for a universal grammar (which must now be
declared over) was both correct and incorrect. Correct, because if you are going to have a language
that cognition can deal with at a speed suitable for survival, grammar and syntactic structure are
absolute requirements (i.e., languages that don’t meet these requirements will either adapt to do so,
or will be extincted with their speakers). Thus, grammar is indeed universal. Incorrect, because
grammar itself is a fiction. It does not exist. It is merely the visible spoor of the hidden native
machinery of cognition: confabulation, antecedent support knowledge, and the conclusion–action
principle.

3.4 SOUND COGNITION

Unlike language, which is the centerpiece and masterpiece of human cognition, all the other
functions of cognition (e.g., sensation and action) must interact directly with the outside world.
Sensation requires conversion of externally supplied sensory representations into symbolic repre-
sentations and vice versa for actions. This section, and the next (discussing vision), must therefore
discuss not only the confabulation architectures used, but also cover the implementation of this