1-4 of 4 Results  for:

  • Keywords: language processing x
  • Computational Linguistics x
Clear all


Psycholinguistics and Aging  

Michael Ramscar

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 Morphological Learning  

Jordan Kodner

A computational learner needs three things: Data to learn from, a class of representations to acquire, and a way to get from one to the other. Language acquisition is a very particular learning setting that can be defined in terms of the input (the child’s early linguistic experience) and the output (a grammar capable of generating a language very similar to the input). The input is infamously impoverished. As it relates to morphology, the vast majority of potential forms are never attested in the input, and those that are attested follow an extremely skewed frequency distribution. Learners nevertheless manage to acquire most details of their native morphologies after only a few years of input. That said, acquisition is not instantaneous nor is it error-free. Children do make mistakes, and they do so in predictable ways which provide insights into their grammars and learning processes. The most elucidating computational model of morphology learning from the perspective of a linguist is one that learns morphology like a child does, that is, on child-like input and along a child-like developmental path. This article focuses on clarifying those aspects of morphology acquisition that should go into such an elucidating a computational model. Section 1 describes the input with a focus on child-directed speech corpora and input sparsity. Section 2 discusses representations with focuses on productivity, developmental paths, and formal learnability. Section 3 surveys the range of learning tasks that guide research in computational linguistics and NLP with special focus on how they relate to the acquisition setting. The conclusion in Section 4 presents a summary of morphology acquisition as a learning problem with Table 4 highlighting the key takeaways of this article.


Generative Grammar  

Knut Tarald Taraldsen

This article presents different types of generative grammar that can be used as models of natural languages focusing on a small subset of all the systems that have been devised. The central idea behind generative grammar may be rendered in the words of Richard Montague: “I reject the contention that an important theoretical difference exists between formal and natural languages” (“Universal Grammar,” Theoria, 36 [1970], 373–398).


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.