Enduring Debates on Psychology and Language in the 20th Century

Research in the psychology of language has been dogged by some enduring controversies, many of which continue to divide researchers. Furthermore, language research has been riven by too many dichotomies and too many people taking too extreme a position, and progress is only likely to be made when researchers recognize that language is a complex system where simple dichotomies may not be relevant. The enduring controversies cover the width of psycholinguistics, including the work of Chomsky and the nature of language, to what extent language is innately determined and the origin of language and how it evolved. Chomsky’s work has also influenced our conceptions of the modularity of the structure of the mind and the nature of psychological processing. Advances in the sophistication of brain imaging techniques have led to debate about exactly what these techniques can tell us about the psychological processing of language. There has also been much debate about whether psychological processing occurs through explicit rules or statistical mapping, a debate driven by connectionist modeling, deep learning, and techniques for the analysis of “big data.” Another debate concerns the role of prediction in language and cognition and the related issues of the relationship between language comprehension and language production. To what extent is language processing embodied, and how does it relate to controversies about “embedded cognition”? Finally, there has been debate about the purpose and use of language.

Connectionist Principles in Theories of Speech Production

This chapter focuses on connectionist modeling in language production, highlighting how core principles of connectionism provide coverage for empirical observations about representation and selection at the phonological, lexical, and sentence levels. The first section focuses on the connectionist principles of localist representations and spreading activation. It discusses how these two principles have motivated classic models of speech production and shows how they cover results of the picture-word interference paradigm, the mixed error effect, and aphasic naming errors. The second section focuses on how newer connectionist models incorporate the principles of learning and distributed representations through discussion of syntactic priming, cumulative semantic interference, sequencing errors, phonological blends, and code-switching.

Modelling the structure and dynamics of semantic processing

The contents and structure of semantic memory have been the focus of much recent research, with major advances in the development of distributional models, which use word co-occurrence information as a window into the semantics of language. In parallel, connectionist modeling has extended our knowledge of the processes engaged in semantic activation. However, these two lines of investigation have rarely been brought together. Here, we describe a processing model based on distributional semantics in which activation spreads throughout a semantic network, as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can successfully account for response times in lexical and semantic decision tasks, as well as for subjective concreteness and imageability ratings. We also show that the dynamics of the network is predictive of performance in relational semantic tasks, such as similarity/relatedness rating. Our results indicate that bringing together distributional semantic networks and spreading of activation provides a good fit to both automatic lexical processing (as indexed by lexical and semantic decisions) as well as more deliberate processing (as indexed by ratings), above and beyond what has been reported for previous models that take into account only similarity resulting from network structure.