Creating a conscious machine remains controversial and challenging. This work describes a humanoid cognitive robot that learns tasks by imitating human demonstrations, using cause-effect reasoning to infer a demonstrator's intentions rather than merely copying actions. Its cognitive components center on top-down control of working memory, which retains explanatory interpretations constructed during learning. Ongoing work aims to convert this imitation learning system into purely neurocomputational form, including low-level neuromotor components, working memory, and causal reasoning. Based on initial results, top-down cognitive control of working memory—especially its gating mechanisms—is argued to be an important potential computational correlate of consciousness in humanoid robots. Developing such neurocognitive control systems provides a credible route to ultimately developing a phenomenally conscious machine.
Conscious experience may arise from electromagnetic waves propagating through time, not just space. Standard Maxwell's equations are likely incomplete; extending them with complex-valued field components implies that electromagnetic fields extend temporally. Applied to the brain's self-generated fields, this hypothesis accounts for the extended duration of conscious moments and the subjective flow of time—phenomena unexplained by current physics. It also reframes episodic memory: recalling past experiences becomes partly a perceptual re-experiencing process rather than pure retrieval from storage. Complex-valued fields substantially increase the explanatory power of electromagnetic field theories of consciousness.