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  Bridging artificial and natural language learning: Comparing processing- and reflection-based measures of learning

Isbilen, E., Frost, R. L. A., Monaghan, P., & Christiansen, M. (2018). Bridging artificial and natural language learning: Comparing processing- and reflection-based measures of learning. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 1856-1861). Austin, TX: Cognitive Science Society.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0001-BEC9-7 Version Permalink: https://hdl.handle.net/21.11116/0000-0001-C5B9-0
Genre: Conference Paper

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http://mindmodeling.org/cogsci2018/papers/0358/0358.pdf (Supplementary material)
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 Creators:
Isbilen, Erin1, Author
Frost, Rebecca Louise Ann2, Author           
Monaghan, Padraic3, Author           
Christiansen, Morten1, Author
Affiliations:
1Cornell University, Ithaca, NY, USA, ou_persistent22              
2Language Development Department, MPI for Psycholinguistics, Max Planck Society, ou_2340691              
3Lancaster University, Lancaster, UK, ou_persistent22              

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 Abstract: A common assumption in the cognitive sciences is that artificial and natural language learning rely on shared mechanisms. However, attempts to bridge the two have yielded ambiguous results. We suggest that an empirical disconnect between the computations employed during learning and the methods employed at test may explain these mixed results. Further, we propose statistically-based chunking as a potential computational link between artificial and natural language learning. We compare the acquisition of non-adjacent dependencies to that of natural language structure using two types of tasks: reflection-based 2AFC measures, and processing-based recall measures, the latter being more computationally analogous to the processes used during language acquisition. Our results demonstrate that task-type significantly influences the correlations observed between artificial and natural language acquisition, with reflection-based and processing-based measures correlating within – but not across – task-type. These findings have fundamental implications for artificial-to-natural language comparisons, both methodologically and theoretically.

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Language(s): eng - English
 Dates: 2018-07
 Publication Status: Published online
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 Rev. Type: Peer
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Title: the 40th Annual Conference of the Cognitive Science Society (CogSci 2018)
Place of Event: Madison, WI, USA
Start-/End Date: 2018-07-25 - 2017-07-28

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Title: Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018)
Source Genre: Proceedings
 Creator(s):
Kalish, Charles, Editor
Rau, Martina, Editor
Zhu, Jerry, Editor
Rogers, Timothy T., Editor
Affiliations:
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Publ. Info: Austin, TX : Cognitive Science Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1856 - 1861 Identifier: ISBN: 978-0-9911967-8-4