Congratulations to Masha Fedzechkina on her article on a bias for efficient information transfer during language learning that has just appeared in the Proceedings of the National Academy of Sciences (link to article).
Here’s some news coverage
- Thanks to the extended podcast coverage by the Skeptic’s Guide to the Universe, which present an interesting take on our study (a couple of details about ambiguity vs. uncertainty are a bit off, but the general message is captured well). Have a look at minutes 13:30 to 22:02. Thanks to Nick Kloehn for making us aware of this piece.
- www.eurekalert.org (e.g., here).
- Check out an extended article on ScienceOmega (Language learning balances clarity and effort)
- Universities at which the research was conducted:
- University of Rochester (Language is shaped by brain’s desire for clarity and ease)
- Georgetown University (PNAS Study: Language Structure Arises from Balance of Clear and Effective Communication).
- Blogs and more: Futurity, Phys.org, ScienceBlog, Science Daily, E-Science News , Sify, TruthDive , BioSpace
More to come soon.
Errata: We are sorry that in our paper we forgot to acknowledge the help of three undergraduate research assistants, Andy Wood, Irene Minkina, and Cassandra Donatelli, in preparing the video animations used during our artificial language learning task.
In case, there’s interest, have a look at the papers to be presented at this year’s Cognitive Science meeting in Boston (July, 20th-23rd). HLP lab will be represented by two talks and four posters. The two talks will presenting work employing artificial language learning to address questions about typological generalizations:
- Masha Fedzechkina(BCS, University of Rochester) will present evidence that language learners are biased to reduced the uncertainty in the mapping from form to meaning. Her work is comparing the acquisition of miniature languages with and without case-marking in terms of to what extent learners tend to regularize or even fix variable word orders for these two types of languages (Fedzechkina, Jaeger, & Newport, 2011). Together with other recent work (e.g. by Newport, by Culbertson), this work provides evidence that language learners deviate from the input provided to them in a predictable manner. In this case, we designed the experiment to directly test the functionalist claim that language learners are biases towards acquiring languages that support communication (cf. Bates and MacWhinney’s early work).
- Hal Tily (BCS, MIT) will present work employing a novel web-based artificial language learning paradigm, in which hundreds of participants can be run within a matter of a few days. Using this paradigm, we first replicated and extended a well-known study on determiner learning (Hudson Kam and Newport, 2004) and then investigate to what extent cross-linguistically observed quantitative patterns in argument and determiner order are replicated by language learners. We discuss how this paradigm will facilitate further tests of typological generalizations (Tily, Frank, & Jaeger, 2011).
We presented the results of our artificial language learning study on the use of case-marking and word order as cues in processing and learning at the LSA annual meeting. This is work done with Florian Jaeger and Elissa Newport. We investigated whether functional pressures (e.g., ambiguity reduction) operate during language acquisition, biasing learners to (subtly) deviate from the input they receive. Our results suggest that language learners indeed have a bias to reduce uncertainty (or ambiguity) in the input language: The learners are more likely to fix the word order if a language does not have case. See the image below for the details of the study or download the poster as a pdf here. Feedback welcome!
Update 11/29/11: This work was published in the 2011 CogSci Proceedings as
- Fedzechkina, M., Jaeger, T. F., and Newport, E. L. 2011. Functional Biases in Language Learning: Evidence from Word Order and Case-Marking Interaction. Proceedings of the 33rd Annual Meeting of the Cognitive Science Society (CogSci11), 318-323.