RADICAL explores the idea that consciousness is something that the brain learns to do rather than a static property of certain neural states vs. others. Here, considering that consciousness is extended both in space and in time, I adopt a resolutely dynamical perspective that mandates an experimental approach focused on change, at different time scales. I suggest that consciousness arises as a result of the brain's continuous attempts at predicting not only the consequences of its actions on the world and on other agents, but also the consequences of activity in one cerebral region on activity in other regions. By this account, the brain continuously and unconsciously learns to redescribe its own activity to itself, so developing systems of metarepresentations that characterise and qualify the target first order representations. Such learned redescriptions form the basis of conscious experience. Learning and plasticity are thus constitutive of consciousness. This is what I call the “Radical Plasticity Thesis”. In a sense, this is the enactive perspective, but turned both inwards and (further) outwards. Consciousness involves “signal detection on the mind”; the conscious mind is the brain's (non-conceptual, implicit) theory about itself. Theoretically, RADICAL offers the possibility of unifying Global Workspace Theory with higher-order Thought Theory by showing how the former can be built through mechanisms that flesh out the latter. Empirically, RADICAL aims at testing these ideas in three domains: (1) the perception action loop, (2) the self-other loop, and (3) the inner loop. 20 experiments leveraging behavioural experimentation, brain imaging, and computational modeling are proposed to test and further develop RADICAL. The overarching goal of the project is to characterize the computational principles that differentiate conscious from unconscious cognition, based on a bold, original, and innovative theory in which learning and plasticity play central roles.