The need for a universal computational model of bilingual word recognition and word translation

Heuven, Walter J. B. van; Wen, Yun

doi:10.1017/s1366728918000688

Cited by 9 publications

(12 citation statements)

References 7 publications

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“…First, this approach allows complete decoupling of the roles of phonology and orthography during learning so that any observed translation‐ambiguity effect can be unequivocally traced to the phonological level. Second, one may argue that the lexical system of different‐script multilinguals is organized differently from that of same‐script multilinguals because the nonoverlapping orthographies allow for functional separation between the languages (e.g., Goral, ; Jiang, ; van Heuven & Wen, ; but see Degani et al., ). Thus, results from same‐script multilinguals may not generalize to different‐script multilinguals.…”

Section: Background Literaturementioning

confidence: 99%

How Individual Differences Affect Learning of Translation‐Ambiguous Vocabulary

Degani

Goldberg

2019

Language Learning

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This study examined interactions of word and learner characteristics during foreign vocabulary learning, focusing on translation ambiguity and individual differences in cognitive resources and linguistic background (language proficiency, multilingual experience). Fifty-three native Hebrew speakers and Russian-Hebrew multilinguals learned the phonological form of target Arabic words along with their Hebrew translations and definitions. The mapping could be translation ambiguous, with a single Hebrew word translated into two Arabic words (one-to-many) or translation unambiguous (oneto-one mapping). Results from translation production and meaning recognition tests revealed that translation-ambiguous words were more difficult to learn than translationunambiguous words. This disadvantage did not dissipate with time, and learners' phonological short-term memory was associated with increased translation ambiguity costs. Learners' proficiency in the language through which learning took place (Hebrew), but not degree of multilingualism, modulated learning. Findings underscore the importance of item and learner interactions, clarifying the multilingualism effect in novel language learning. active competition between the different alternative translations may hinder performance, which is in line with assumptions of competitive inhibitory interactions between connected representations in interactive activation models (Doherty, 2004;McClelland & Rumelhart, 1981). Second, translation-ambiguous words may suffer from a decrease in associative strength, similar to the fan effect (e.g., Anderson, 1974). The fan effect assumes that as more facts are associated with a concept, each fact has decreased probability of occurrence and thus lower associative strength. As a result, the more facts that are connected to a concept, the longer individuals take to recall the concept. In the case of translation-ambiguous words, a learner would take longer to retrieve a translation if additional translations are linked to the same word.The majority of previous studies have examined the one-to-many direction of ambiguity, where a single L1 word was mapped onto two foreign language vocabulary items (for an exception focusing on many-to-one mapping, see

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Section: Background Literaturementioning

confidence: 99%

How Individual Differences Affect Learning of Translation‐Ambiguous Vocabulary

Degani

Goldberg

2019

Language Learning

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“…Nevertheless, we still obtained surprisingly high correlations between simulations and empirical data for just one parameter setting across different tasks. Van Heuven and Wen (2018) point out that different model variants should be compared against each other, and contrasting Multilink without and with lateral inhibition is one important possibility (see below).…”

Section: Multilink: Theoretical Issues and Desired Extensionsmentioning

confidence: 99%

“…In contrast to what Declerck et al (2018) suggest, the ‘cognate patch’ could not (yet) be replaced by lateral inhibition: identical cognates suffered disproportionally from lateral inhibition relative to non-identical cognates and control words. Only after suitable settings of the lateral inhibition parameter have been determined, can we start to simulate the masked translation priming studies mentioned by van Heuven and Wen (2018; e.g., Wen & van Heuven, 2017), as well as orthographic priming studies.…”

Section: Multilink: Theoretical Issues and Desired Extensionsmentioning

confidence: 99%

“…In addition, in our paper, we demonstrated that the similarity of the three localist connectionist models allows for close model-to-model comparisons relative to shared datasets (like DLP, ELP, and diverse empirical studies). Nevertheless, as van Heuven and Wen (2018) say, it is also important to contrast different variants of Multilink itself, covering multidimensional parameter variations, in order to better understand the consequences of architectural, processing, and parameter assumptions in the various models.…”

Section: Positioning Multilink In the Research Domain Relative To Biamentioning

confidence: 99%

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Modelling bilingual lexical processing: A research agenda and desiderabilia

et al. 2018

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Like the BIA model (Dijkstra & van Heuven, 1998; van Heuven, Dijkstra & Grainger, 1998) and the BIA+ model (Dijkstra & van Heuven, 2002), the Multilink model is a symbolic, localist-connectionist, interactive model for lexical processing in the visual domain. In our view, the symbolic nature of Multilink makes it attractive and easily interpretable, even in relation to brain activity (Page, 2000, p. 501; 2017). Symbolic localist-connectionist models have a long tradition and have been applied to many different areas of cognitive research (e.g., Grainger & Jacobs, 1998). As a consequence, a lot is known about their properties and limitations (e.g., Bowers, 2009). These models can also easily be organized hierarchically in a cognitive functional way, and they have a reasonable degree of flexibility while still being falsifiable. Thus, despite the availability of other sophisticated frameworks for modeling language processes, a lot can still be gained from localist models.

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“…Among the model's limitations, Mishra (2019) notes that Multilink overemphasizes lexical dimensions such as cognate status and orthographic similarity, which may be relevant for word processing in Dutch–English bilinguals, but possibly less so for speakers that use different types of orthographies and phonologies (see also Jiang, 2019). Van Heuven and Wen (2019) make similar suggestions: the need to evaluate Multilink with findings from studies involving different-script bilinguals, as the model focuses only on studies with stimuli from alphabetic languages. Tokowicz (2019) emphasizes that while Multilink does indeed address some shortcomings of previous models (BIA and BIA+), there are additional ways in which the model could be expanded, including a sharper focus on individual differences among speakers.…”

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confidence: 99%

Computational approaches to word retrieval in bilinguals

Abutalebi¹,

Clahsen²

2019

Bilingualism

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The cognitive architecture of human language processing has been studied for decades, but using computational modeling for such studies is a relatively recent topic. Indeed, computational approaches to language processing have become increasingly popular in our field, mainly due to advances in computational modeling techniques and the availability of large collections of experimental data. Language learning, particularly child language learning, has been the subject of many computational models. By simulating the process of child language learning, computational models may indeed teach us which linguistic representations are learnable from the input that children have access to (and which are not), as well as which mechanisms yield the same patterns of behavior that are found in children's language performance.

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The need for a universal computational model of bilingual word recognition and word translation

Cited by 9 publications

References 7 publications

How Individual Differences Affect Learning of Translation‐Ambiguous Vocabulary

How Individual Differences Affect Learning of Translation‐Ambiguous Vocabulary

Modelling bilingual lexical processing: A research agenda and desiderabilia

Computational approaches to word retrieval in bilinguals

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