Behavioral and Brain Sciences



Main Article

Neural blackboard architectures of combinatorial structures in cognition


Frank van der Velde a1 and Marc de Kamps a2
a1 Cognitive Psychology, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands vdvelde@fsw.leidenuniv.nl
a2 Robotics and Embedded Systems, Institut für Informatik, Technische Universität München, Boltzmannstrasse 3, D-85748 Garching bei München, Germany kamps@in.tum.de

Article author query
van der velde f   [PubMed][Google Scholar] 
de kamps m   [PubMed][Google Scholar] 

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.


Key Words: binding; blackboard architectures; combinatorial structure; compositionality; language; dynamic system; neurocognition; sentence complexity; sentence structure; working memory; variables; vision.


Metrics