There is a simplicity bias when generalizing from ambiguous data
Karthik Durvasula, Adam Liter
April 2020

How exactly do learners generalize in the face of ambiguous data? While there has been a substantial amount of research studying the biases that learners employ, there has been very little work on what sorts of biases learners employ in the face of data that is ambiguous between phonological generalizations with different degrees of simplicity/complexity. In this article, we present the results from 3 artificial language learning experiments that suggest that, at least for phonotactic sequence patterns, learners are able to keep track of multiple generalizations related to the same segmental co-occurrences; however, the generalizations they learn are only the simplest ones that are consistent with the data.
Format: [ pdf ]
Reference: lingbuzz/005120
(please use that when you cite this article)
Published in: Phonology (accepted)
keywords: phonology, simplest generalizations, artificial language learning
Downloaded:201 times


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