Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-25T03:24:29.105Z Has data issue: false hasContentIssue false
  • English
  • Français

Scaling up: How computational models can propel bilingualism research forward

Published online by Cambridge University Press:  19 June 2018

PING LI  and
ANGELA GRANT
PING LI*
Affiliation:
The Pennsylvania State University
ANGELA GRANT
Affiliation:
Concordia University
*
Address for correspondence: Ping Li, 452 Moore Building, Department of Psychology, The Pennsylvania State University, University Park, PA 16802[email protected]
Get access Rights & Permissions [Opens in a new window]

Extract

The Multilink model that Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, de Korte, and Rekké (2018) present is an excellent example that connects empirical patterns obtained from behavioral studies with mechanisms that can be implemented in computational models. We have previously argued that implementation of computational models is important because it forces the researchers to be explicit about assumptions and to specify parameters and variables that may be absent in verbal models. The Multilink model, along with BIA/BIA+ and many other models, provides concrete hypotheses regarding the role of variables such as word frequency, word length, orthographic similarity, and phonological neighborhood for researchers to test and verify against empirical data (see examples in the special issue on computational modeling published in this journal; Li, 2013).

Type
Peer Commentaries
Information
Bilingualism: Language and Cognition , Volume 22 , Issue 4 , August 2019 , pp. 682 - 684
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Balota, D. A., Yap, M. J., Cortese, M. J., Hutchison, K. A., Kessler, B., Loftis, B., Neely, J. H., Nelson, D. L., Simpson, G. B., & Treiman, R. (2007). The English lexicon project. Behavior Research Methods, 39, 445–459.Google Scholar
Brysbaert, M., Stevens, M., Mandera, P., & Keuleers, E. (2016). The impact of word prevalence on lexical decision times: Evidence from the Dutch Lexicon Project 2. Journal of Experimental Psychology: Human Perception and Performance, 42, 441–458.Google Scholar
Burgess, C., & Lund, K. (1997). Modelling parsing constraints with high-dimensional context space. Language & Cognitive Processes, 12, 177–210.Google Scholar
Caliskan, A., Bryson, J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356, 183–186.Google Scholar
Dijkstra, A., Wahl, A., Buytenhuijs, F., van Halem, N., Al-jibouri, Z., de Korte, M., & Rekké, S. (2018). Multilink: a computational model for bilingual word recognition and word translation. Bilingualism: Language and Cognition, doi:10.1017/S1366728918000287.Google Scholar
Fang, S., Zinszer, B., Malt, B., & Li, P. (2016). Bilingual object naming: A connectionist model. Frontiers in Psychology: Cognitive Science, 7, Article 644. doi: 10.3389/fpsyg.2016.00644Google Scholar
Gollan, T. H., Montoya, R. I., Cera, C., & Sandoval, T. C. (2008). More use almost always means a smaller frequency effect: Aging, bilingualism, and the weaker links hypothesis. Journal of Memory and Language, 58, 787–814. https://doi.org/10.1016/j.jml.2007.07.001Google Scholar
Grosjean, F. (1989). Neurolinguists, beware! The bilingual is not two monolinguals in one person. Brain and Language, 36 (1), 3–15.Google Scholar
Hopman, E. W. M., & Macdonald, M. C. (2018). Production Practice During Language Learning Improves Comprehension. Psychological Science. Advance online publication. https://doi.org/10.1177/0956797618754486Google Scholar
Kang, S. H. K., Gollan, T. H., & Pashler, H. (2013). Don't just repeat after me: retrieval practice is better than imitation for foreign vocabulary learning. Psychonomic Bulletin & Review, 20 (6), 1259–65. https://doi.org/10.3758/s13423-013-0450-zGoogle Scholar
Kroll, J. F., & Stewart, E. (1994). Category interference in translation and picture naming: Evidence for asymmetric connections between bilingual memory representations. Journal of Memory and Language, 33, 149–174.Google Scholar
Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato's problem: The Latent Semantic Analysis theory of the acquisition, induction, and representation of knowledge. Psychological Review, 104, 211–240.Google Scholar
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of Workshop at ICLR, 2013. (arXiv preprint arXiv:1301.3781).Google Scholar
Li, P. (2002). Bilingualism is in dire need of formal models. Bilingualism: Language and Cognition, 5, 213.Google Scholar
Li, P., & Farkas, I. (2002). A self-organizing connectionist model of bilingual processing. In Heredia, R. & Altarriba, J. (Eds.), Bilingual sentence processing (pp. 59–85). North-Holland: Elsevier Science Publisher.Google Scholar
Li, P. (2013). Computational modeling of bilingualism: How can models tell us more about the bilingual mind? Bilingualism: Language and Cognition, 16, 241–245.Google Scholar
Malt, B., Li, P., Pavlenko, A., Zhu, H., & Ameel, E. (2015). Bidirectional lexical interaction in late immersed Mandarin-English bilinguals. Journal of Memory and Language, 82, 86–104.Google Scholar
Thomas, M. (1997). Connectionist networks and knowledge representation: The case of bilingual lexical processing. Unpublished dissertation, Oxford University, UK.Google Scholar
Zinszer, B. D., Malt, B. C., Ameel, E., & Li, P. (2014). Native-likeness in second language lexical categorization reflects individual language history and linguistic community norms. Frontiers in Psychology: Language Sciences, 5, Article 1203. doi: 10.3389/fpsyg.2014.01203Google Scholar