Guest Lecture by Rufin Van Rullen

Global Workspace Theory (GWT) is a leading account of human cognition and consciousness. In this view, a number of independent specialized modules connect to a shared central representation space; when a module is selected by attention, its contents are mobilized into the Global Workspace, and broadcast across the entire brain, resulting in a unified and integrated experience. Inspired by this framework, we have developed a deep learning architecture that captures key features of GWT: the Global Latent Workspace (GLW). I will present our GLW and its initial implementations, with promising applications in various AI domains. The model shows improvements in sample efficiency for multimodal representation learning. It can be leveraged for downstream classification and retrieval tasks. When an action module is connected to the GLW, the whole system exhibits affordance-like properties. The GLW is also beneficial as an input space for RL policy training: the policy is learned with fewer environment steps, and displays zero-shot cross-modal transfer abilities. Finally, augmenting the GLW with “operation” modules and an attention-controlled routing mechanism could open the way toward System-2 reasoning and sequential problem-solving. As GWT is also widely held as a neuroscientific theory of consciousness, I will finish by discussing the possible implications of these systems for AI consciousness.

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