Abstract
Human cognition is unique in the way in which it relies on combinatorial
(or compositional) structures. Language provides ample evidence for
the existence of combinatorial structures, but they can also be found
in visual cognition. To understand the neural basis of human cognition,
it is therefore essential to understand how combinatorial structures
can be instantiated in neural terms. In his recent book on the foundations
of language, Jackendoff described four fundamental problems for a
neural instantiation of combinatorial structures: the massiveness
of the binding problem, the problem of 2, the problem of variables,
and the transformation of combinatorial structures from working memory
to long-term memory. This paper aims to show that these problems
can be solved by means of neural blackboard architectures. For
this purpose, a neural blackboard architecture for sentence structure
is presented. In this architecture, neural structures that encode
for words are temporarily bound in a manner that preserves the structure
of the sentence. It is shown that the architecture solves the four
problems presented by Jackendoff. The ability of the architecture
to instantiate sentence structures is illustrated with examples of
sentence complexity observed in human language performance. Similarities
exist between the architecture for sentence structure and blackboard
architectures for combinatorial structures in visual cognition, derived
from the structure of the visual cortex. These architectures are
briefly discussed, together with an example of a combinatorial structure
in which the blackboard architectures for language and vision are
combined. In this way, the architecture for language is grounded
in perception. Perspectives and potential developments of the architectures
are discussed.
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