Artificial Grammar Learning (AGL) has been a rich paradigm for testing the limits of our linguistic computational machinery. Starting from a formal learning framework, my colleague Enes Avcu and I have been investigating the learnability of long distance phonotactic patterns to test hypotheses about the complexity of patterns that our phonological module can and cannot acquire.
We know that learners can learn and extract adjacent and non-adjacent dependencies with relatively minimal exposure in the lab. Less is known about how lab-learned patterns are encoded at the neurophysiological level. The aim of this project is to examine the neurophysiological correlates of different learning mechanisms when learning non-adjacent phonotactic patterns. We believe that understanding phonological processing will illuminate the learning mechanisms (domain-specific vs. domain-general) used to acquire language.
Using behavioral and ERP measures, we are interested in the following questions:
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- Do domain-specific (linguistic) vs. domain-general mechanisms support learning new phonological patterns?
- Are there reliable neurophysiological correlates of processing sound patterns?
- Do different learning mechanisms lead to different types of neural observations?
In a series of ERP and behavioral experiments, we have found that learners are sensitive not only to complexity but also to locality. This suggests that our (native English) learners are defaulting to a Strictly Local representation when exposed to novel long distance phonotactic dependencies – despite the patterns being better explained as Strictly Piecewise.
We also find ERPs related to learning and categorization for learnable and local patterns – the P300 and the late positive component (LPC). In a study comparing explicit and implicit learning, we find a curious pattern of results. Implicit learners have above-chance behavioral results and robust ERPs (P300 and LPC). Explicit learners show much better behavioral results, but none of the expected brain responses.
These results so far are a mystery, but we speculate that the process of pattern finding may play a large role in how these rules are ultimately represented and implemented in the brain.
