The BIA++: Extending the BIA+ to a dynamical distributed connectionist framework

Jacquet, Maud; French, Robert M.

doi:10.1017/s1366728902223019

Cited by 32 publications

(21 citation statements)

References 17 publications

(6 reference statements)

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“…Both initially focused on questions of memory organization, gradually converging on a relatively distributed, monolithic view of this organization, and then moved progressively towards the processes acting on this underlying organization: memory processes, such as recall and recognition processes, language-production processes, such as lexical selection, or even task-specific processes. Perhaps one of the most glaring shortcomings of many current models, in particular, the BIA, BIAþ and IC, is that they do not learn and cannot handle sequences of words [55,71]. They cannot, therefore, model the gradual emergence of bilingual memory organization (see also Box 2).…”

Section: Discussionmentioning

confidence: 99%

“…The same level-of-observation issues mentioned earlier apply to the BIA with its underlying assumption of a single, integrated bilingual lexicon and non-selective access. So, although the BIA might be 'integrated' at the level of lexical items, its use of language nodes (essentially equivalent to language tags) to selectively inhibit all words in one or the other language (hence producing lexical selection), means that, above the lexical level, its integrated nature disappears [55]. In short, the lack of a clear definition of language tags, their status and how they might have originally come into being, gives them somewhat of an ad hoc status in the BIA [7].…”

Section: Lexical Selectionmentioning

confidence: 99%

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Understanding bilingual memory: models and data

French

Jacquet

2004

Trends in Cognitive Sciences

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125

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Bilingual memory research in the past decade and, particularly, in the past five years, has developed a range of sophisticated experimental, neuropsychological and computational techniques that have allowed researchers to begin to answer some of the major long-standing questions of the field. We explore bilingual memory along the lines of the conceptual division of language knowledge and organization, on the one hand, and the mechanisms that operate on that knowledge and organization, on the other. Various interactive-activation and connectionist models of bilingual memory that attempt to incorporate both organizational and operational considerations will serve to bridge these two divisions. Much progress has been made in recent years in bilingual memory research, which also serves to illuminate general (language-independent) memory processes.

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Section: Discussionmentioning

confidence: 99%

Section: Lexical Selectionmentioning

confidence: 99%

Understanding bilingual memory: models and data

French

Jacquet

2004

Trends in Cognitive Sciences

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125

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“…With a one-to-one mapping between concepts and units, the localist representation clearly has simplicity and efficiency and has brought great computational success for simulating language processing. However, the one-node-one-concept representation is subject to the criticism that it lacks linguistic and psychological reality (Jacquet & French, 2002). For example, one cannot simulate similarity-based semantic priming effects with the localist representation, given that similarities among concepts are not encoded in the representation when concepts have been assigned random values in the model.…”

Section: Comparing Different Approachesmentioning

confidence: 99%

Contextual self-organizing map: software for constructing semantic representations

Zhao

Kohonen

2010

Behav Res

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In this article, we introduce a software package that applies a corpus-based algorithm to derive semantic representations of words. The algorithm relies on analyses of contextual information extracted from a text corpusspecifically, analyses of word co-occurrences in a largescale electronic database of text. Here, a target word is represented as the combination of the average of all words preceding the target and all words following it in a text corpus. The semantic representation of the target words can be further processed by a self-organizing map (SOM; Kohonen, Self-organizing maps, 2001), an unsupervised neural network model that provides efficient data extraction and representation. Due to its topography-preserving features, the SOM projects the statistical structure of the context onto a 2-D space, such that words with similar meanings cluster together, forming groups that correspond to lexically meaningful categories. Such a representation system has its applications in a variety of contexts, including computational modeling of language acquisition and processing. In this report, we present specific examples from two languages (English and Chinese) to demonstrate how the method is applied to extract the semantic representations of words.

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“…This, again, requires a definition of acquisition (if this term is insisted on) that takes into account the possibility of an item being present in memory but absent from performance, in which case sensitive psycholinguistic tests like semantic priming will be required to establish what a learner has or has not acquired in this minimalist sense of the word. Psycholinguistic research conducted within theoretical perspectives that are very different from MOGUL can nonetheless provide evidence for the effect of co-activation and competition in ways that are compatible with this particular theoretical framework (see, for example, Dijkstra & van Heuven 1998Jacquet & French 2002).…”

Section: Lexical Access In Mogul: Words As Rules Chains and Networkmentioning

confidence: 99%

Language acquisition as a side effect of language processing

Smith

2010

LIA

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Researchers in the field of language acquisition may adopt various particular theoretical approaches to the description and explanation of linguistic phenomena. They need this theory to describe the nature of what is or is not undergoing change over time. However linguistic theory normally makes no reference to the time dimension, a dimension crucial to any study of development. Psycholinguists, on the other hand, focus on temporal phenomena but these are normally measured in milliseconds. At the same time, they still need a coherent model of linguistic structure to carry out their investigations. The logic of this suggests that a framework is needed that integrates linguistic theories about the properties of language, i.e., linguistic knowledge, with theories about on-line processing. The need to integrate these two ways of looking at language is also crucial for researchers who study linguistic development in the individual. One such approach is Modular Growth and Use of Language (MOGUL), a theoretical framework based on a close examination of research in several disciplines and within which utterance processing plays a vital role in explaining development. In fact, in that part of MOGUL which is devoted to explaining acquisition, the claim is that development is a side-effect of making sense of language to which we are exposed. No special learning mechanisms are involved.

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The BIA++: Extending the BIA+ to a dynamical distributed connectionist framework

Cited by 32 publications

References 17 publications

Understanding bilingual memory: models and data

Understanding bilingual memory: models and data

Contextual self-organizing map: software for constructing semantic representations

Language acquisition as a side effect of language processing

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