Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-22T04:09:49.693Z Has data issue: false hasContentIssue false

Language membership as a gradient emergent feature

Published online by Cambridge University Press:  28 June 2018

MICHAEL A. JOHNS*
Affiliation:
Penn State University / Center for Language Science
MICHAEL T. PUTNAM
Affiliation:
Penn State University / Center for Language Science
*
Address for correspondence: Dr. Michael T. Putnam, Penn State University / Center for Language Science, 239 Burrowes Building, University Park, PA 16802[email protected]

Extract

In their keynote article, Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, de Korte, and Rekké (2018) introduce the Multilink model representing an integrated bi/multilingual lexicon. This proposal builds upon both previous and recent research on an integrated cognitive architecture underlying the language faculty (for a summary, see e.g., Putnam, Carlson & Reitter, 2018). In our view, the adjustments proposed by the authors are an improvement on previous instantiations of similar models such as those discussed in the present article. In our remarks we explicate how the Multilink model may be further enhanced, by making any appeal to language-specific nodes or representations epiphenomenal. To achieve this, we propose a novel approach to representing language membership as the result of gradient emergent principles that builds upon the integrated lexicon underlying the Multilink model.

Type
Peer Commentaries
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

Christiansen, M. H., & Chater, N. (2016). The Now-or-Never bottleneck: A fundamental constraint on language. Behavioral and Brain sciences, e62. doi: 10.1017/S0140525X1500031XGoogle 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
Fazly, A., Alishahi, A., & Stevenson, S. (2010). A Probabilistic Computational Model of Cross-Situational Word Learning. Cognitive Science, 34, 10171063. doi: 10.1111/j.1551-6709.2010.0104.xGoogle Scholar
Putnam, M., Carlson, M., & Reitter, D. (2018). Integrated, not isolated: Defining typological proximity in an integrated multilingual architecture. Frontiers in Psychology, 8:2212. doi: 10.3389/fpsyg.2017.02212Google Scholar
Smith, A. D., & Smith, K. (2012). Cross-Situational Learning. In Encyclopedia of the Sciences of Learning, pp. 864866. Dordrecht: Springer.Google Scholar