Machine learning algorithms have achieved superhuman performance in specific complex domains. However, learning online from few examples and compositional learning for efficient generalization across domains remain elusive. In humans, such learning includes specific declarative memory formation and is closely associated with consciousness. Predictive processing has been advanced as a principled Bayesian framework for understanding the cortex as implementing deep generative models for both sensory perception and action control. However, predictive processing offers little direct insight into fast compositional learning or of the separation between conscious and unconscious contents. Here, propose that access consciousness arises as a consequence of a particular learning mechanism operating within a predictive processing system. We extend predictive processing by adding online, single-example new structure learning via hierarchical binding of unpredicted inferences. This system learns new causes by quickly connecting together novel combinations of perceptions, which manifests as working memories that can become short- and long-term declarative memories retrievable by associative recall. The contents of such bound representations are unified yet differentiated, can be maintained by selective attention and are globally available. The proposed learning process explains contrast and masking manipulations, postdictive perceptual integration, and other paradigm cases of consciousness research. 'Phenomenal conscious experience' is how the learning system transparently models its own functioning, giving rise to perceptual illusions underlying the meta-problem of consciousness. Our proposal naturally unifies the feature binding, recurrent processing, predictive processing, and global workspace theories of consciousness.

翻译：机器学习算法已在特定复杂领域实现超人类性能。然而，在线少样本学习与跨领域高效泛化的组合式学习仍是未解难题。在人类认知中，此类学习涉及特定的陈述性记忆形成，并与意识紧密关联。预测处理理论作为理解皮层实现深度生成模型以进行感觉感知与动作控制的原则性贝叶斯框架已被提出，但该框架对快速组合式学习或意识与无意识内容的分离机制未能提供直接解释。本文提出：访问意识产生于预测处理系统中特定学习机制的运作。我们通过层级绑定未预测推断实现单样本在线新结构学习，从而扩展了预测处理框架。该系统通过快速联结感知的新颖组合来学习新成因，其表现形式为可通过联想回忆提取的短时与长时陈述性工作记忆。此类绑定表征的内容既统一又分化，可通过选择性注意维持并实现全局可用性。所提出的学习过程可解释对比度与掩蔽操作、后效知觉整合以及意识研究的其他范式案例。"现象意识体验"是学习系统对其自身功能的透明建模，由此产生构成意识元问题的知觉错觉。本理论自然统一了意识研究的特征绑定理论、循环处理理论、预测处理理论与全局工作空间理论。