School of Experimental Psychology, University of Bristol, 12a Priory Road, Bristol, BS8 1TU, UK.
Trends Cogn Sci. 2017 Dec;21(12):950-961. doi: 10.1016/j.tics.2017.09.013. Epub 2017 Oct 31.
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.
心理学中的并行分布处理 (PDP) 模型是计算机科学中使用的深度网络的前身。然而，只有 PDP 模型与两个核心的心理学主张相关联，即所有知识都是以分布式格式编码的，认知是由非符号计算介导的。这些主张在认知科学中一直存在争议，最近的深度网络研究也涉及到了这一争议。具体来说，单细胞记录表明，深度网络学习的单元对有意义的类别有选择性的反应，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 和深度网络之间的紧密联系，令人惊讶的是，深度网络的研究正在挑战 PDP 理论。
