David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Behavioral and Brain Sciences 29 (1):37-70 (2006)
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.
|Keywords||binding blackboard architectures combinatorial structure compositionality language dynamic system neurocognition sentence complexity sentence structure working memory variables vision|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
Eric Crawford, Matthew Gingerich & Chris Eliasmith (2015). Biologically Plausible, Human‐Scale Knowledge Representation. Cognitive Science 39 (7).
Ray Jackendoff (2006). The Simpler Syntax Hypothesis. Trends in Cognitive Sciences 10 (9):413-418.
M. Kukleta, P. Bob, M. Brázdil, R. Roman & I. Rektor (2010). The Level of Frontal-Temporal Beta-2 Band EEG Synchronization Distinguishes Anterior Cingulate Cortex From Other Frontal Regions. Consciousness and Cognition 19 (4):879-886.
Pierre Bonzon (2011). Towards Machine Consciousness: Grounding Abstract Models as Π-Processes. International Journal of Machine Consciousness 3 (01):1-17.
Pavel Prudkov (2010). A View on Human Goal-Directed Activity and the Construction of Artificial Intelligence. Minds and Machines 20 (3):363-383.
Similar books and articles
Michael A. Arbib (2003). Brain, Meaning, Grammar, Evolution. Behavioral and Brain Sciences 26 (6):668-669.
Michael G. Dyer (2006). Will the Neural Blackboard Architecture Scale Up to Semantics? Behavioral and Brain Sciences 29 (1):77-78.
Birgitta Dresp & Jean Charles Barthaud (2006). Has the Brain Evolved to Answer “Binding Questions” or to Generate Likely Hypotheses About Complex and Continuously Changing Environments? Behavioral and Brain Sciences 29 (1):75-76.
Lokendra Shastri (2006). Comparing the Neural Blackboard and the Temporal Synchrony-Based SHRUTI Architectures. Behavioral and Brain Sciences 29 (1):84-86.
Friedrich T. Sommer & Pentti Kanerva (2006). Can Neural Models of Cognition Benefit From the Advantages of Connectionism? Behavioral and Brain Sciences 29 (1):86-87.
Jerry A. Fodor & Zenon W. Pylyshyn (1988). Connectionism and Cognitive Architecture. Cognition 28 (1-2):3-71.
Frank van der Velde & Marc de Kamps (2002). Involvement of a Visual Blackboard Architecture in Imagery. Behavioral and Brain Sciences 25 (2):213-214.
Yoonsuck Choe (2006). How Neural is the Neural Blackboard Architecture? Behavioral and Brain Sciences 29 (1):72-73.
Bernard J. Baars (2006). Conscious Cognition and Blackboard Architectures. Behavioral and Brain Sciences 29 (1):70-71.
Frank van der Velde & Marc de Kamps (2006). From Neural Dynamics to True Combinatorial Structures. Behavioral and Brain Sciences 29 (1):88-104.
Added to index2009-01-28
Total downloads18 ( #150,602 of 1,725,168 )
Recent downloads (6 months)4 ( #167,174 of 1,725,168 )
How can I increase my downloads?