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

Erin Isbilen*, Rebecca L.A. Frost, Padraic Monaghan, Morten H. Christiansen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

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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.
Original languageEnglish
Title of host publicationProceedings of the 40th Annual Conference of the Cognitive Science Society
EditorsC. Kalish, M. Rau, J. Zhu, T. T. Rogers
PublisherCognitive Science Society
ISBN (Electronic)9780991196784
Publication statusPublished - 2018
Externally publishedYes


  • Psychology


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