Multi-Theory Consciousness Architecture: Integrating GWT, AST, HOT, FEP, IIT, RPT, and BLT in a Unified Computational Framework

We present the Multi-Theory Consciousness (MTC) framework: an open-source Python implementation of seven major consciousness theories operating as interacting modules within a single architecture. The framework implements Global Workspace Theory (Baars, 1988), Attention Schema Theory (Graziano, 2013), Higher-Order Thought Theory (Rosenthal, 2005), the Free Energy Principle (Friston, 2010), Integrated Information Theory (Tononi, 2008), Recurrent Processing Theory (Lamme, 2006), and Beautiful Loop Theory (Laukkonen, Friston & Chandaria, 2025), with Damasio's three-layer model as an integrative embodied layer. Three neural substrates — a spiking neural network, a liquid state machine, and a hierarchical temporal memory — provide the computational medium. A 20-indicator assessment framework inspired by Butlin et al. (2023) measures architectural function across all theories, with noise normalization for honest scoring and ablation studies for measuring cross-theory dependencies. The central finding is that the theories interact in measurable ways: disabling one module changes assessment scores for other theories. Prediction error signals from active inference shape workspace competition; attention strength modulates precision weighting; workspace access gates meta-representation. We do not claim the framework is conscious. We claim it is a useful research instrument — a controlled testbed where consciousness theories can be implemented, measured, and compared. The complete codebase (~25,000 lines, 400 tests) is released under the Apache 2.0 license and runs on consumer hardware. Repository: https://github.com/WhiteLotusLA/multi-theory-consciousness