Machine learning algorithms have achieved superhuman performance in specific
complex domains. Yet learning online from few examples and efficiently
generalizing across domains remains elusive. In humans such learning proceeds
via declarative memory formation and is closely associated with consciousness.
Predictive processing has been advanced as a principled Bayesian inference
framework for understanding the cortex as implementing deep generative
perceptual models for both sensory data and action control. However, predictive
processing offers little direct insight into fast compositional learning or the
mystery of consciousness. Here we propose that through implementing online
learning by hierarchical binding of unpredicted inferences, a predictive
processing system may flexibly generalize in novel situations by forming
working memories for perceptions and actions from single examples, which can
become short- and long-term declarative memories retrievable by associative
recall. We argue that the contents of such working memories are unified yet
differentiated, can be maintained by selective attention and are consistent
with observations of masking, postdictive perceptual integration, and other
paradigm cases of consciousness research. We describe how the brain could have
evolved to use perceptual value prediction for reinforcement learning of
complex action policies simultaneously implementing multiple survival and
reproduction strategies. 'Conscious experience' is how such a learning system
perceptually represents its own functioning, suggesting an answer to the meta
problem of consciousness. Our proposal naturally unifies feature binding,
recurrent processing, and predictive processing with global workspace, and, to
a lesser extent, the higher order theories of consciousness.

通过单例学习和分层绑定等方式，预测处理系统可灵活地在新情景中广泛推广，并使用感知价值预测来实施多个生存和复制策略，且这种系统所感知的 “有意识的体验” 是其自身功能的感知表示，从而解决了 “意识元问题”