Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Toward a Computational Theory of Mind

Published

Author(s)

James S. Albus

Abstract

Scientific knowledge of the brain and technology of intelligent systems have developed to a point where a computational theory of mind is feasible.  This paper briefly describes the RCS (Real-time Control System) reference model architecture that has been used successfully over the past 30 years for designing intelligent systems for a wide variety of applications.   It then describes how RCS can be mapped onto the neuronal structure of the brain, and vice versa. Both RCS and the brain are goal-directed and sensory-interactive intelligent control systems. Both are hierarchical in structure and partitioned into behavior generating and sensory processing hierarchies.   Both rely heavily on an internal model of the external world for perception and behavior.  The world model is used in perception for focusing attention, segmentation, grouping, and classification.  It is used in behavior for decision-making, planning, and control.  Both RCS and the brain have value judgment processes that assign worth to perceived objects, events, situations, and scenarios; and estimate the cost, risk, and benefit of plans for future behavior.  The formal structure of RCS provides a framework for a computational theory of mind that is both quantitative and experimentally testable.
Citation
Journal of Behavioral and Brain Sciences

Keywords

mind, brain, artificial intelligence, robotics, neuroscience, cognitive science

Citation

Albus, J. (2006), Toward a Computational Theory of Mind, Journal of Behavioral and Brain Sciences (Accessed June 12, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created December 29, 2006, Updated February 17, 2017