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.

翻译：机器学习算法在特定复杂领域已实现超越人类的表现，然而，从少量样本进行在线学习并高效跨领域泛化仍是难题。在人类中，此类学习通过陈述性记忆形成过程实现，且与意识紧密相关。预测处理理论作为原则性的贝叶斯推断框架，被提出用于理解大脑皮层如何实现深层生成感知模型以处理感觉数据与动作控制。但该理论对快速组合学习或意识之谜的直接阐释甚少。本文提出：通过基于不可预测推论的层级绑定实现在线学习，预测处理系统可在新异情境中灵活泛化——通过从单个样例形成感知与动作的工作记忆，这些记忆可转化为可被联想回忆提取的短时与长时陈述性记忆。我们论证此类工作记忆的内容具有统一性兼具区分性，可通过选择性注意维持，并与掩蔽、后验感知整合等意识研究经典范式观察结果一致。我们描述了大脑如何可能进化出利用感知价值预测进行强化学习，从而同时实现多种生存与繁殖策略的复杂动作策略。"意识体验"正是这种学习系统对自身功能的感知表征，这为意识的元问题提供了回答线索。本方案自然统一了特征绑定、递归处理、预测处理与全局工作空间理论，并在较小程度上兼容高阶意识理论。