Psychology/Brain and Mind Institute, The University of Western Ontario, London, ON, Canada.
Department of Psychology, Stanford University, Stanford, CA, USA.
Wiley Interdiscip Rev Cogn Sci. 2015 May-Jun;6(3):235-47. doi: 10.1002/wcs.1340. Epub 2015 Jan 30.
The field of formal linguistics was founded on the premise that language is mentally represented as a deterministic symbolic grammar. While this approach has captured many important characteristics of the world's languages, it has also led to a tendency to focus theoretical questions on the correct formalization of grammatical rules while also de-emphasizing the role of learning and statistics in language development and processing. In this review we present a different approach to language research that has emerged from the parallel distributed processing or 'connectionist' enterprise. In the connectionist framework, mental operations are studied by simulating learning and processing within networks of artificial neurons. With that in mind, we discuss recent progress in connectionist models of auditory word recognition, reading, morphology, and syntactic processing. We argue that connectionist models can capture many important characteristics of how language is learned, represented, and processed, as well as providing new insights about the source of these behavioral patterns. Just as importantly, the networks naturally capture irregular (non-rule-like) patterns that are common within languages, something that has been difficult to reconcile with rule-based accounts of language without positing separate mechanisms for rules and exceptions.
形式语言学领域的建立基于这样一个前提，即语言在心理上被表示为一种确定性的符号语法。虽然这种方法捕捉到了世界上许多语言的重要特征，但它也导致了一种倾向，即把理论问题的重点放在正确的语法规则形式化上，同时也淡化了学习和统计在语言发展和处理中的作用。在这篇综述中，我们提出了一种不同的语言研究方法，这种方法源自并行分布式处理或“连接主义”的研究。在连接主义框架中，通过模拟神经网络中的学习和处理来研究心理运算。有鉴于此，我们讨论了连接主义模型在听觉词识别、阅读、形态和句法处理方面的最新进展。我们认为，连接主义模型可以捕捉到语言学习、表示和处理的许多重要特征，并为这些行为模式的来源提供新的见解。同样重要的是，网络自然地捕捉到了语言中常见的不规则（非规则样）模式，这在不假定规则和例外有单独机制的情况下，很难用基于规则的语言解释来调和。
