The active inference framework (AIF) offers a unified, naturalistic account of life, mind, and consciousness by grounding them in the principle of free energy minimization. It bridges computational neuroscience, robotics, ecological psychology, and phenomenology, treating particles, organisms, and artificial agents under a common information-theoretic foundation. The paper introduces AIF, then examines its implications for evolutionary theory, ecological psychology, embodied phenomenology, and robotics, concluding with considerations for machine consciousness.
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.