Bilingual word recognition beyond orthography: On meaning, linguistic context and individual differences

2002 ◽  
Vol 5 (3) ◽  
pp. 209-212 ◽  
Author(s):  
Janet G. van Hell
Keyword(s):  
Word Recognition ◽  
Context Effects ◽  
Word Identification ◽  
Empirical Studies ◽  
Recognition System ◽  
Task Demands ◽  
Identification System ◽  
Linguistic Context ◽  
Decision System ◽  
Language Competition

Central questions in psycholinguistic studies on bilingualism are how bilinguals access words in their two languages, and how they control their language systems and solve the problem of cross-language competition. In their excellent paper “The architecture of the bilingual word recognition system: From identification to decision”, Dijkstra and Van Heuven expound their BIA+ model on bilingual word recognition. BIA+ builds on its predecessor BIA, one of the first connectionist models on bilingual word recognition. BIA+ preserves one of BIA's crucial assumptions, namely that the bilingual lexicon is integrated across languages and is accessed in a language non-selective way, an assumption that is supported in many empirical studies and that is now widely accepted in the bilingual literature. Compared to the original BIA model, the BIA+ architecture is further developed (in fact, much more so than the subtle ‘plus’ denotes). BIA+ now includes orthographic, as well as phonological and semantic representations in the word identification system, and a distinction is made between a word identification system and a task/decision system. This latter extension resembles the language task schemas in Green's (1998) Inhibitory Control model. Dijkstra and Van Heuven also distinguish between effects of linguistic and non-linguistic context on performance: linguistic context effects, that arise from lexical, syntactic and semantic sources, are assumed to affect the activity in the word identification system, whereas non-linguistic effects, that can arise from instruction, task demands or participant expectancies, are assumed to affect the task/decision system.

scholarly journals The architecture of the bilingual word recognition system: From identification to decision

2002 ◽  
Vol 5 (3) ◽  
pp. 175-197 ◽  
Author(s):  
Ton Dijkstra ◽  
Walter J.B. van Heuven
Keyword(s):  
Word Recognition ◽  
Empirical Evidence ◽  
Word Identification ◽  
Recognition System ◽  
Identification System ◽  
Linguistic Context ◽  
Decision System ◽  
Stimulus List ◽  
New Model ◽  
Lexical Representations

The paper opens with an evaluation of the BIA model of bilingual word recognition in the light of recent empirical evidence. After pointing out problems and omissions, a new model, called the BIA+, is proposed. Structurally, this new model extends the old one by adding phonological and semantic lexical representations to the available orthographic ones, and assigns a different role to the so-called language nodes. Furthermore, it makes a distinction between the effects of non-linguistic context (such as instruction and stimulus list composition) and linguistic context (such as the semantic and syntactic effects of sentence context), based on a distinction between the word identification system itself and a task/decision system that regulates control. At the end of the paper, the generalizability of the BIA+ model to different tasks and modalities is discussed.

scholarly journals On the temporal delay assumption and the impact of non-linguistic context effects

2002 ◽  
Vol 5 (3) ◽  
pp. 199-201 ◽  
Author(s):  
Marc Brysbaert ◽  
Ilse van Wijnendaele ◽  
Wouter Duyck
Keyword(s):  
Word Recognition ◽  
Context Effects ◽  
Identification System ◽  
Temporal Delay ◽  
Linguistic Context ◽  
Cautionary Note ◽  
Research Areas ◽  
Target Article ◽  
The Impact ◽  
Selective Access

It is not easy to comment on Dijkstra and Van Heuven's model because there are many more aspects we agree with than aspects we feel uncomfortable about. Indeed, the BIA model has played an enormous role in showing us how bilingual visual word recognition can be achieved without recurrence to the intuitively appealing – but wrong – ideas of separate, language-specific lexicons and language-selective access. As in many other research areas, a working computational model has been much more influential in convincing critical readers (and researchers) than any series of empirical findings. The BIA+ model inherits this strength and, hopefully, in the coming years will be implemented in enough detail to exceed its predecessor. In the rest of this comment, we would like to put a cautionary note behind the temporal delay assumption introduced in the target article and provide some additional corroborating evidence for the lack of non-linguistic effects on early processes in the identification system.

scholarly journals Neural Evidence of Language Membership Control in Bilingual Word Recognition: An fMRI Study of Cognate Processing in Chinese–Japanese Bilinguals

2021 ◽  
Vol 12 ◽  
Author(s):  
Ming-Che Hsieh ◽  
Hyeonjeong Jeong ◽  
Motoaki Sugiura ◽  
Ryuta Kawashima
Keyword(s):  
Word Recognition ◽  
Cognitive Processing ◽  
Word Processing ◽  
Word Identification ◽  
Response Competition ◽  
Target Language ◽  
Identification System ◽  
Decision Task ◽  
Decision System ◽  
Language Membership

This study aims to examine the neural mechanisms of resolving response competition during bilingual word recognition in the context of language intermixing. During fMRI scanning, Chinese–Japanese unbalanced bilinguals were required to perform a second-language (L2) lexical decision task composed of cognates, interlingual homographs, matched control words from both Chinese (first language) and Japanese (L2), and pseudowords. Cognate word processing showed longer reaction times and greater activation in the supplementary motor area (SMA) than L2 control word processing. In light of the orthographic and semantic overlap of cognates, these results reflect the cognitive processing involved in resolving response conflicts enhanced by the language membership of non-target language during bilingual word recognition. A significant effect of L2 proficiency was also observed only in the SMA, which is associated with the task decision system. This finding supports the bottom-up process in the BIA+ model and the Multilink model. The task/decision system receives the information from the word identification system, making appropriate responses during bilingual word recognition.

Words only go so far: Linguistic context affects bilingual word processing

2018 ◽  
Vol 22 (04) ◽  
pp. 689-690 ◽  
Author(s):  
JANET G. VAN HELL
Keyword(s):  
Computational Model ◽  
Word Processing ◽  
Word Identification ◽  
Empirical Studies ◽  
Model Development ◽  
Empirical Work ◽  
Identification System ◽  
Simulation Studies ◽  
Linguistic Context ◽  
Syntactic Information

In their keynote paper, Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, de Korte, and Rekké (2018) present a computational model of bilingual word recognition and translation, Multilink, that integrates and further refines the architecture and processing principles of two influential models of bilingual word processing: the Bilingual Activation Model (BIA/BIA+) and the Revised Hierarchical model (RHM). Unlike the earlier models, Multilink has been implemented as a computational model so its design principles and assumptions can be compared with human processing data in simulation studies – which is an important step forward in model development and refinement. But Multilink also leaves behind an important theoretical advancement that was touched upon in extending BIA to BIA+ (Dijkstra & Van Heuven, 2002): how linguistic context influences word processing. In their presentation of BIA+, Dijkstra and Van Heuven (2002) hypothesized that syntactic and semantic aspects of sentence context may affect the word identification system. Theoretically, this was an important step forward, as none of the bilingual word processing models (and few monolingual word processing models, for that matter) had incorporated linguistic context, and at that time only a handful of empirical studies had examined how linguistic context affects bilingual word processing. However, in the past 15 years a significant body of empirical work has been published that examines how semantic and syntactic information in sentences impacts word processing in bilinguals. These important insights are not incorporated in the Multilink model.

The bilingual as an adaptive system

2002 ◽  
Vol 5 (3) ◽  
pp. 206-208 ◽  
Author(s):  
David W. Green
Keyword(s):  
Adaptive System ◽  
Task Demands ◽  
Identification System ◽  
Important Research ◽  
Decision System ◽  
System A ◽  
Critical Separation

Dijkstra and van Heuven lucidly summarize the important research generated by the BIA model and provide an excellent case for the BIA+ model with its critical separation of the identification system from the task/decision system. A keynote article necessarily offers a selective exposition of the authors' thinking and so my remarks are an invitation to expand. My first question concerns the scope of the BIA+ model. My remaining questions broadly address a key feature of the BIA+ model – its ability to explain performance changes as a function of task demands

scholarly journals Theories that develop

2002 ◽  
Vol 5 (3) ◽  
pp. 216-217 ◽  
Author(s):  
Michael S. C. Thomas
Keyword(s):  
Word Recognition ◽  
Computational Models ◽  
Context Effects ◽  
Recognition System ◽  
Significant Advance ◽  
Target Article ◽  
Simplifying Assumptions ◽  
Recognition Systems ◽  
Decision Mechanisms ◽  
New Framework

The target article represents a significant advance in the level of sophistication applied to models of bilingual word recognition, and Dijkstra and van Heuven are to be congratulated on this endeavour. Bearing in mind the success of the (computational) BIA model in capturing detailed patterns of experimental data, I look forward to future simulation results from the BIA+ when the proposals of this new framework are implemented. It is an essential step to draw a distinction between recognition systems and the decision mechanisms that drive responses, and the authors have provided a novel way of apportioning empirical evidence of context effects in bilingual word recognition across this divide. Given the explanatory weight now being placed on decision mechanisms rather than the word recognition system itself, perhaps indeed it is now time to make some simplifying assumptions about the recognition system and start building detailed computational models of the decision component of the system. Implementation will provide the clarity of theorisation and evaluation of theory viability that have been the hallmark of the BIA model thus far.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

scholarly journals How do bilinguals control their use of languages?

2002 ◽  
Vol 5 (3) ◽  
pp. 214-215 ◽  
Author(s):  
Ardi Roelofs
Keyword(s):  
Word Recognition ◽  
Visual Word Recognition ◽  
Word Processing ◽  
Task Demands ◽  
Visual Word ◽  
Design Decisions ◽  
Processing Component ◽  
Visual Word Processing ◽  
New Processing

Dijkstra and Van Heuven sketch the BIA+ model for visual word processing in bilinguals. BIA+ differs in a number of respects from its predecessor, BIA, the leading implemented model of bilingual visual word recognition. Notably, BIA+ contains a new processing component that deals with task demands. BIA+ has not been computationally implemented yet and design decisions still need to be taken. In this commentary, I outline a proposal for modeling the control of tasks in BIA+.

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