Talk at the Models of Consciousness 5 Conference. University of Bamberg in Germany (2024)
Video originally uploaded @models-of-consciousness
Abstract:
In the wake of the success of attention-based transformer networks, the discussion about consciousness in artificial systems has intensified. The global neuronal workspace theory (GNWT) models consciousness computationally, suggesting the brain has specialized modules connected by long-distance neuronal links. Depending on context, inputs, and tasks, content from one module is broadcasted to others, forming the global neuronal workspace representing conscious awareness.
The global latent workspace (GLW) model introduces a central latent representation around which multiple modules are built. A semi-supervised training process ensures cycle consistency, enabling content translation between modules with minimal loss. The central representation integrates necessary information from each module, with access determined by transformer-like attention mechanisms.
We examine the dynamics of a virtual embodied reinforcement learning agent with a minimal GLW setup, involving deep visual sensory and motor modules. The augmented PPO agent exhibits complex goal-directed behavior in the Obstacle Tower Challenge 3D environment. Latent space representations cluster into sensorimotor affordance groups.
This study links GNWT with sensorimotor contingency theory, suggesting that changes in sensory input relative to motor output constitute the neuronal correlates of conscious experience. This convergence in a machine learning setup raises the question: Can such in silico representation suffice for phenomenal spatial perception?