RESEARCH ARTICLE


Neurally and Mathematically Motivated Architecture for Language and Thought



L.I Perlovsky1, *, R Ilin2
1 Harvard University, Cambridge, and the Air Force Research Laboratory, Sensors Directorate, Hanscom AFB, USA
2 The Air Force Research Laboratory, Sensors Directorate, Hanscom AFB, USA


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© Perlovsky and Ilin; Licensee Bentham Open

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Air Force Research Laboratory, Sensors Directorate, Hanscom AFB, USA; Tel.: 781-377-1728; Tel: 781-377-1728; Fax: 781-377-8984; E-mail: leonid@seas.harvard.edu


Abstract

Neural structures of interaction between thinking and language are unknown. This paper suggests a possible architecture motivated by neural and mathematical considerations. A mathematical requirement of computability imposes significant constraints on possible architectures consistent with brain neural structure and with a wealth of psychological knowledge. How language interacts with cognition. Do we think with words, or is thinking independent from language with words being just labels for decisions? Why is language learned by the age of 5 or 7, but acquisition of knowledge represented by learning to use this language knowledge takes a lifetime? This paper discusses hierarchical aspects of language and thought and argues that high level abstract thinking is impossible without language. We discuss a mathematical technique that can model the joint language-thought architecture, while overcoming previously encountered difficulties of computability. This architecture explains a contradiction between human ability for rational thoughtful decisions and irrationality of human thinking revealed by Tversky and Kahneman; a crucial role in this contradiction might be played by language. The proposed model resolves long-standing issues: how the brain learns correct words-object associations; why animals do not talk and think like people. We propose the role played by language emotionality in its interaction with thought. We relate the mathematical model to Humboldt’s “firmness” of languages; and discuss possible influence of language grammar on its emotionality. Psychological and brain imaging experiments related to the proposed model are discussed. Future theoretical and experimental research is outlined.

Keywords: Thinking and language interaction, language, high-level cognition, brain, mind, emotions, semantics, Emotional Sapir-Whorf Hypothesis, dynamic logic, knowledge instinct, dual model, learning context.