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Psycholinguistic Methods and Tasks in Morphology  

Daniel Schmidtke and Victor Kuperman

Lexical representations in an individual mind are not given to direct scrutiny. Thus, in their theorizing of mental representations, researchers must rely on observable and measurable outcomes of language processing, that is, perception, production, storage, access, and retrieval of lexical information. Morphological research pursues these questions utilizing the full arsenal of analytical tools and experimental techniques that are at the disposal of psycholinguistics. This article outlines the most popular approaches, and aims to provide, for each technique, a brief overview of its procedure in experimental practice. Additionally, the article describes the link between the processing effect(s) that the tool can elicit and the representational phenomena that it may shed light on. The article discusses methods of morphological research in the two major human linguistic faculties—production and comprehension—and provides a separate treatment of spoken, written and sign language.

Article

Neurolinguistic Approaches in Morphology  

Niels O. Schiller

Neurolinguistic approaches to morphology include the main theories of morphological representation and processing in the human mind, such as full-listing, full-parsing, and hybrid dual-route models, and how the experimental evidence that has been acquired to support these theories uses different neurolinguistic paradigms (visual and auditory priming, violation, long-lag priming, picture-word interference, etc.) and methods (electroencephalography [EEG]/event-related brain potential [ERP], functional magnetic resonance imaging [fMRI], neuropsychology, and so forth).

Article

Discriminative Learning and the Lexicon: NDL and LDL  

Yu-Ying Chuang and R. Harald Baayen

Naive discriminative learning (NDL) and linear discriminative learning (LDL) are simple computational algorithms for lexical learning and lexical processing. Both NDL and LDL assume that learning is discriminative, driven by prediction error, and that it is this error that calibrates the association strength between input and output representations. Both words’ forms and their meanings are represented by numeric vectors, and mappings between forms and meanings are set up. For comprehension, form vectors predict meaning vectors. For production, meaning vectors map onto form vectors. These mappings can be learned incrementally, approximating how children learn the words of their language. Alternatively, optimal mappings representing the end state of learning can be estimated. The NDL and LDL algorithms are incorporated in a computational theory of the mental lexicon, the ‘discriminative lexicon’. The model shows good performance both with respect to production and comprehension accuracy, and for predicting aspects of lexical processing, including morphological processing, across a wide range of experiments. Since, mathematically, NDL and LDL implement multivariate multiple regression, the ‘discriminative lexicon’ provides a cognitively motivated statistical modeling approach to lexical processing.