A modular network treatment of Baars' Global Workspace consciousness model

Abstract

Adapting techniques from random and semirandom network theory, this work provides an alternative to the renormalization and phase transition methods used in Wallace's (2005a) treatment of Baars' Global Workspace model. The new formalism predicts dynamics that should be empirically distinguishable from those suggested by the earlier analysis. Nevertheless, like the earlier work, it produces the workspace itself, the tunable threshold of consciousness, and the essential role for embedding contexts, in an explicitly analytic 'necessary conditions' manner which suffers neither the mereological fallacy inherent to brain-only theories nor the sufficiency indeterminacy of neural network or agent-based simulations. This suggests that the new approach, and the earlier, represent different analytically solvable limits in a complicated continuum of possible models. Thus the development significantly extends the theoretical foundations for an empirical general cognitive model (GCM) based on the Shannon-McMillan Theorem. Patterned after the general linear model based on the Central Limit Theorem, the proposed technique could be particularly useful in the reduction of experimental data on consciousness.

Available Versions of this Item

• A modular network treatment of Baars' Global Workspace consciousness model. (deposited 08 Sep 2005) [Currently Displayed]
  • A modular network treatment of Baars' Global Workspace consciousness model. (deposited 20 Oct 2005)