Unlearnable phonotactics

• Main Authors: Enes Avcu, Arild Hestvik
• Format: Article
• Language: English
• Published: Open Library of Humanities 2020-06-01
• Series: Glossa
• Subjects: phonotactics; learnability; computational complexity; subregular hypothesis; domain specificity
• Online Access: https://www.glossa-journal.org/articles/892

The Subregular Hypothesis (Heinz 2010) states that only patterns with specific subregular computational properties are phonologically learnable. Lai (2015) provided the initial laboratory support for this hypothesis. The current study aimed to replicate and extend the earlier findings by using a different experimental paradigm (oddball task) and a different measure of learning (sensitivity index, 'd'′). Specifically, we compared the learnability of two phonotactic patterns that differ computationally and typologically: a simple rule (“First-Last Assimilation”) that requires agreement between the first and last segment of a word (predicted to be unlearnable), and a harmony rule (“Sibilant Harmony”) that requires the agreement of features throughout the word (predicted to be learnable). The First-Last Assimilation rule was tested under two experimental conditions: one where the training data were also consistent with the Sibilant Harmony rule, and one where the training data were only consistent with the First-Last rule. As in Lai (2015), we found that participants were significantly more sensitive to violations of the Sibilant Harmony (SH) rule than to the First-Last Assimilation (FL) rules. However, unlike Lai (2015), we also found that participants showed some residual sensitivity to the First-Last rule, but that sensitivity interacted with rule type so that participants were significantly more sensitive to SH rule violations. We conclude that participants in Artificial Grammar Learning experiments exhibit evidence of Universal Grammar constraining their learning, but patterns predicted to be unlearnable as a linguistic system can still be learned to some degree, due to non-linguistic learning mechanisms.