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Machine learning algorithms have achieved superhuman performance in specificcomplex domains. However, learning online from few examples and compositionallearning for efficient generalization across domains remain elusive. In humans,such learning includes specific declarative memory formation and is closelyassociated with consciousness. Predictive processing has been advanced as aprincipled Bayesian framework for understanding the cortex as implementing deepgenerative models for both sensory perception and action control. However,predictive processing offers little direct insight into fast compositionallearning or of the separation between conscious and unconscious contents. Here,propose that access consciousness arises as a consequence of a particularlearning mechanism operating within a predictive processing system. We extendpredictive processing by adding online, single-example new structure learningvia hierarchical binding of unpredicted inferences. This system learns newcauses by quickly connecting together novel combinations of perceptions, whichmanifests as working memories that can become short- and long-term declarativememories retrievable by associative recall. The contents of such boundrepresentations are unified yet differentiated, can be maintained by selectiveattention and are globally available. The proposed learning process explainscontrast and masking manipulations, postdictive perceptual integration, andother paradigm cases of consciousness research. 'Phenomenal consciousexperience' is how the learning system transparently models its ownfunctioning, giving rise to perceptual illusions underlying the meta-problem ofconsciousness. Our proposal naturally unifies the feature binding, recurrentprocessing, predictive processing, and global workspace theories ofconsciousness.

Consciousness is entailed by compositional learning of new causal structures in deep predictive processing systems