A Neural Global Workspace Model for Conscious Attention.
Sung Bae Cho, Bernard J. Baars, James Newman
Neural networks : the official journal of the International Neural Network Society October 1, 1997 DOI: 10.1016/s0893-6080(97)00060-9 (opens in new tab) via PubMed
Summary
AI-generated from the abstractA neurocognitive model of consciousness is presented, defining it as a global integration and dissemination system nested within a distributed array of specialized bioprocessors. This system controls the allocation of central nervous system processing resources via cortical gating of a strategic thalamic nucleus. The model integrates experimental data from cognitive psychology, artificial intelligence, and neuroscience, building on developments of Baars' Global Workspace theory. The basic circuitry of this neural system is reasonably well understood and can be approximated using neural network principles.
Study at a glance
| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Key finding | Proposes that consciousness functions as a global integration and dissemination system, controlled via cortical gating of a thalamic nucleus, which can be modeled with neural network principles. |
Abstract
Considerable progress is being made in interdisciplinary efforts to develop a general theory of the neural correlates of consciousness. Developments of Baars' Global Workspace theory over the past decade are examples of this progress. Integrating experimental data and models from cognitive psychology, AI and neuroscience, we present a neurocognitive model in which consciousness is defined as a global integration and dissemination system - nested in a large-scale, distributed array of specialized bioprocessors - which controls the allocation of the processing resources of the central nervous system. It is posited that this global control is effected via cortical 'gating' of a strategic thalamic nucleus. The basic circuitry of this neural system is reasonably well understood, and can be modeled, to a first approximation, employing neural network principles.