Testing the limits of non-adjacent dependency learning: Statistical segmentation and generalization across domains
- Rebecca Frost, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
- Erin Isbilen, Cornell University, Ithaca, New York, United States
- Morten Christiansen, Cornell University, Ithaca, New York, United States
- Padraic Monaghan, University of Amsterdam, Amsterdam, Netherlands
AbstractAchieving linguistic proficiency requires identifying words from speech, and discovering the constraints that govern the way those words are used. In a recent study of non-adjacent dependency learning, Frost and Monaghan (2016) demonstrated that learners may perform these tasks together, using similar statistical processes - contrary to prior suggestions. However, in their study, non-adjacent dependencies were marked by phonological cues (plosive-continuant-plosive structure), which may have influenced learning. Here, we test the necessity of these cues by comparing learning across three conditions; fixed phonology, which contains these cues, varied phonology, which omits them, and shapes, which uses visual shape sequences to assess the generality of statistical processing for these tasks. Participants segmented the sequences and generalized the structure in both auditory conditions, but learning was best when phonological cues were present. Learning was around chance on both tasks for the visual shapes group, indicating statistical processing may critically differ across domains.
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