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.
Daniel Schmidtke and Victor Kuperman
Healthy aging is associated with many cognitive, linguistic, and behavioral changes. For example, adults’ reaction times slow on many tasks as they grow older, while their memories, appear to fade, especially for apparently basic linguistic information such as other people’s names. These changes have traditionally been thought to reflect declines in the processing power of human minds and brains as they age. However, from the perspective of the information-processing paradigm that dominates the study of mind, the question of whether cognitive processing capacities actually decline across the life span can only be scientifically answered in relation to functional models of the information processes that are presumed to be involved in cognition. Consider, for example, the problem of recalling someone’s name. We are usually reminded of the names of friends on a regular basis, and this makes us good at remembering them. However, as we move through life, we inevitably learn more names. Sometimes we hear these new names only once. As we learn each new name, the average exposure we will have had to any individual name we know is likely to decline, while the number of different names we know is likely to increase. This in turn is likely to make the task of recalling a particular name more complex. One consequence of this is as follows: If Mary can only recall names with 95% accuracy at age 60—when she knows 900 names—does she necessarily have a worse memory than she did at age 16, when she could recall any of only 90 names with 98% accuracy? Answering the question of whether Mary’s memory for names has actually declined (or improved even) will require some form of quantification of Mary’s knowledge of names at any given point in her life and the definition of a quantitative model that predicts expected recall performance for a given amount of name knowledge, as well as an empirical measure of the accuracy of the model across a wide range of circumstances. Until the early 21st century, the study of cognition and aging was dominated by approaches that failed to meet these requirements. Researchers simply established that Mary’s name recall was less accurate at a later age than it was at an earlier one, and took this as evidence that Mary’s memory processes had declined in some significant way. However, as computational approaches to studying cognitive—and especially psycholinguistic—processes and processing became more widespread, a number of matters related to the development of processing across the life span began to become apparent: First, the complexity involved in establishing whether or not Mary’s name recall did indeed become less accurate with age began to be better understood. Second, when the impact of learning on processing was controlled for, it became apparent that at least some processes showed no signs of decline at all in healthy aging. Third, the degree to which the environment—both in terms of its structure, and its susceptibility to change—further complicates our understanding of life-span cognitive performance also began to be better comprehended. These new findings not only promise to change our understanding of healthy cognitive aging, but also seem likely to alter our conceptions of cognition and language themselves.
Computational models of human sentence comprehension help researchers reason about how grammar might actually be used in the understanding process. Taking a cognitivist approach, this article relates computational psycholinguistics to neighboring fields (such as linguistics), surveys important precedents, and catalogs open problems.
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).
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.