意识由深度预测处理系统中新因果结构的组合学习所蕴含
Consciousness is entailed by compositional learning of new causal structures in deep predictive processing systems
AI总结：
本文提出意识源于预测处理系统中通过分层绑定未预测推断实现的在线单示例组合学习，该机制统一了特征绑定、递归处理、预测处理和全局工作空间理论。
摘要：
机器学习算法已在特定复杂领域实现了超人类性能。然而，从少量示例中进行在线学习以及跨领域高效泛化的组合学习仍然难以实现。在人类中，此类学习包括特定的陈述性记忆形成，并与意识密切相关。预测处理已被提出作为一个原则性的贝叶斯框架，用于理解大脑皮层实现深度生成模型，以进行感觉感知和动作控制。然而，预测处理对快速组合学习或意识与无意识内容之间的分离提供的直接见解很少。在此，我们提出通达意识产生于预测处理系统内运行的特定学习机制。我们通过添加在线、单示例的新结构学习来扩展预测处理，该学习通过未预测推断的分层绑定实现。该系统通过快速连接新颖的感知组合来学习新的原因，这表现为工作记忆，可成为通过联想回忆可检索的短期和长期陈述性记忆。此类绑定表征的内容是统一但有区别的，可通过选择性注意维持，并全局可用。所提出的学习过程解释了对比度和掩蔽操作、事后知觉整合以及其他意识研究的范式案例。“现象意识体验”是学习系统透明地对其自身功能进行建模的方式，产生了构成意识元问题基础的知觉错觉。我们的提议自然统一了意识的特征绑定、递归处理、预测处理和全局工作空间理论。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
