Biomedical subjects

Brian MacWhinney

Publications and source records attributed to Brian MacWhinney.

7 recordsLinked to original sources

PubMed2005

The emergence of competing modules in bilingualism.

How does the brain manage to store and process multiple languages without encountering massive interference and transfer? Unless we believe that bilinguals live in two totally unconnected cognitive worlds, we would expect far more transfer than actually occurs. However, imaging and lesion studies have not provided consistent evidence for the strict neuronal separation predicted by the theory of modularity. We suggest that emergentist theory offers a promising alternative. It emphasizes the competitive interplay between multiple languages during childhood and by focusing on the dual action of competition and entrenchment, avoids the need to invoke a critical period to account for age of acquisition effects in second-language learning. This view instantiates the motto formulated by Elizabeth Bates that 'modules are made, not born.'

Animals↗

PubMed2004

A multiple process solution to the logical problem of language acquisition.

Many researchers believe that there is a logical problem at the centre of language acquisition theory. According to this analysis, the input to the learner is too inconsistent and incomplete to determine the acquisition of grammar. Moreover, when corrective feedback is provided, children tend to ignore it. As a result, language learning must rely on additional constraints from universal grammar. To solve this logical problem, theorists have proposed a series of constraints and parameterizations on the form of universal grammar. Plausible alternatives to these constraints include: conservatism, item-based learning, indirect negative evidence, competition, cue construction, and monitoring. Careful analysis of child language corpora has cast doubt on claims regarding the absence of positive exemplars. Using demonstrably available positive data, simple learning procedures can be formulated for each of the syntactic structures that have traditionally motivated invocation of the logical problem. Within the perspective of emergentist theory (MacWhinney, 2001), the operation of a set of mutually supportive processes is viewed as providing multiple buffering for developmental outcomes. However, the fact that some syntactic structures are more difficult to learn than others can be used to highlight areas of intense grammatical competition and processing load.

Child Language↗

PubMed2004

Automatic parsing of parental verbal input.

To evaluate theoretical proposals regarding the course of child language acquisition, researchers often need to rely on the processing of large numbers of syntactically parsed utterances, both from children and from their parents. Because it is so difficult to do this by hand, there are currently no parsed corpora of child language input data. To automate this process, we developed a system that combined the MOR tagger, a rule-based parser, and statistical disambiguation techniques. The resultant system obtained nearly 80% correct parses for the sentences spoken to children. To achieve this level, we had to construct a particular processing sequence that minimizes problems caused by the coverage/ambiguity tradeoff in parser design. These procedures are particularly appropriate for use with the CHILDES database, an international corpus of transcripts. The data and programs are now freely available over the Internet.

Adult↗

PubMed2003

Phonological memory and vocabulary learning in children with focal lesions.

Eleven children with early focal lesions were compared with 70 age-matched controls to assess their performance in repeating non-words, in learning new words, and in immediate serial recall, a triad of abilities that are believed to share a dependence on serial ordering mechanisms (e.g.,; ). Results for the experimental group were also compared with other assessments previously reported for the same children by. The children with brain injury showed substantial impairment relative to controls in the experimental tasks, in contrast with relatively unimpaired performance on measures of vocabulary and non-verbal intelligence. The relationships between word learning, non-word repetition, and immediate serial recall were similar to those observed in several other populations. These results support previous reports that there are persistent processing impairments following early brain injury, despite developmental plasticity. They also suggest that word learning, non-word repetition, and immediate serial recall may be relatively demanding tasks, and that their relationship is a fundamental aspect of the cognitive system.

Adolescent↗

PubMed2002

Sentence processing in children with early unilateral brain injury.

This study examined how children use word order and animacy cues to determine the agent of the action in an on-line sentence-comprehension task. The subject group included 15 children, 5-12 years old, with brain injury incurred prior to the age of 2 months; 12 had left hemisphere (LH) damage and 3 had right hemisphere (RH) damage. The comparison group included 141 children, 5-10 years old, who were at the appropriate grade for age. The task required children to listen to sentences composed of two noun phrases (N) that varied in terms of animacy and a verb phrase (V) and then to indicate the agent of the action. Three word orders were presented: NVN, VNN, and NNV. Measures included the proportion of trials in which the first noun was selected (choice) and reaction time. Word order and animacy significantly influenced choice. The effect of subject group approached significance for choice. Word order and age influenced reaction time. The children with LH injury and two children with RH injury showed a developmental delay in choosing the appropriate N as agent; one child with RH injury had mature responses. The overlapping performance of children with LH and RH injury suggests that delays in the development of sentence comprehension strategies are more likely related to reliance on a smaller than usual neural network rather than to congenital specialization of the LH.

Brain↗

PubMed2002

PatPho: a phonological pattern generator for neural networks.

Much of the power of neural network modeling for language use and acquisition derives from a reliance on statistical regularities implicit in the phonological properties of words. Researchers have devised several methods for representing the phonology of words, but these methods are often either unable to represent realistically sized lexicons or inadequate in the ways they represent individual words. In this paper, we present a new phonological pattern generator (PatPho) that allows connectionist modelers to derive accurate phonological representations of the English lexicon. PatPho not only generates phonological patterns that can scale up to realistically sized lexicons, but also accurately and parsimoniously captures the similarity structures of the phonology of monosyllabic and multisyllabic words.

Humans↗

PubMedDate not supplied

Early lexical development in a self-organizing neural network.

In this paper we present a self-organizing neural network model of early lexical development called DevLex. The network consists of two self-organizing maps (a growing semantic map and a growing phonological map) that are connected via associative links trained by Hebbian learning. The model captures a number of important phenomena that occur in early lexical acquisition by children, as it allows for the representation of a dynamically changing linguistic environment in language learning. In our simulations, DevLex develops topographically organized representations for linguistic categories over time, models lexical confusion as a function of word density and semantic similarity, and shows age-of-acquisition effects in the course of learning a growing lexicon. These results match up with patterns from empirical research on lexical development, and have significant implications for models of language acquisition based on self-organizing neural networks.

Artificial Intelligence↗