Parallel distributed processing (PDP) models in psychology are the precursors of deep networks used in computer science. However, only PDP models are associated with two core psychological claims, namely that all knowledge is coded in a distributed format and cognition is mediated by non-symbolic computations. These claims have long been debated in cognitive science, and recent work with deep networks speaks to this debate. Specifically, single-unit recordings show that deep networks learn units that respond selectively to meaningful categories, and researchers are finding that deep networks need to be supplemented with symbolic systems to perform some tasks. Given the close links between PDP and deep networks, it is surprising that research with deep networks is challenging PDP theory.

中文翻译：

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深度网络时代的并行分布式处理理论

心理学中的并行分布式处理 (PDP) 模型是计算机科学中使用的深度网络的先驱。然而，只有 PDP 模型与两个核心心理学主张相关，即所有知识都以分布式格式编码，认知由非符号计算介导。这些说法长期以来一直在认知科学中争论不休，而最近对深度网络的研究也说明了这一争论。具体来说，单单元记录表明深度网络学习对有意义的类别有选择性响应的单元，研究人员发现深度网络需要补充符号系统来执行某些任务。鉴于 PDP 和深度网络之间的密切联系，令人惊讶的是，对深度网络的研究正在挑战 PDP 理论。