One of the central claims associated with the parallel distributed processing approach popularized by D.E. Rumelhart, J.L. McClelland and the PDP Research Group is that knowledge is coded in a distributed fashion. Localist representations within this perspective are widely rejected. It is important to note, however, that connectionist networks can learn localist representations and many connectionist models depend on localist coding for their functioning. Accordingly, a commitment to distributed representations should be considered a specific theoretical claim regarding the structure of knowledge rather than a core principle, as often assumed. In this paper, it is argued that there are fundamental computational and empirical challenges that have not yet been addressed by distributed connectionist theories that are readily accommodated within localist approaches. This is highlighted in the context of modeling word and nonword naming, the domain in which some of the strongest claims have been made. It is shown that current PDP models provide a poor account of naming monosyllable items, and that distributed representations make it difficult for these models to scale up to more complex language phenomena. At the same time, models that learn localist representations are shown to hold promise in supporting many of the core reading and language functions on which PDP models fail. It is concluded that the common rejection of localist coding schemes within connectionist architectures is premature.

中文翻译：

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挑战联结主义和分布式表示相辅相成的普遍假设

与 DE Rumelhart、JL McClelland 和 PDP 研究小组推广的并行分布式处理方法相关的核心主张之一是知识是以分布式方式编码的。这种观点中的地方主义代表被广泛拒绝。然而，重要的是要注意，联结网络可以学习局部表征，而且许多联结模型的功能依赖于局部编码。因此，对分布式表示的承诺应被视为关于知识结构的特定理论主张，而不是通常假设的核心原则。在本文中，有人认为，分布式联结主义理论尚未解决一些基本的计算和经验挑战，而这些挑战很容易适应本地主义方法。这在建模词和非词命名的上下文中得到了强调，该领域已经提出了一些最强烈的主张。结果表明，当前的 PDP 模型对命名单音节项目提供了一个糟糕的解释，并且分布式表示使得这些模型难以扩展到更复杂的语言现象。同时，学习本地化表征的模型在支持 PDP 模型失败的许多核心阅读和语言功能方面很有希望。得出的结论是，在连接主义架构中普遍拒绝本地化编码方案为时过早。