Abstract
This study used behavioral measures and ERP difference waves to measure the underlying brain processes during the categorization of grammatical vs ungrammatical stimuli according to a lab learned phonotactic rule. The results show that participants learned the simple rule at the behavioral level (as measured with d-prime, a sensitivity measure to rule violations). This rule learning is also reflected in the brain response to violations of the rule, which is indexed by the P3 rare-minus-frequent difference waveform. The neural results indicate that this learning took the form of a neural commitment. Participants learned the rule and used it to make active predictions, categorizing words as ungrammatical at the exact point of violation. This ability must be instantiated at the neural level, meaning rapid neural tuning has occurred in this lab setting.
Highlights
Artificial grammar learning (AGL) studies have shown that learners can extract adjacent and nonadjacent phonotactic patterns with relatively short training in laboratory settings (see Moreton & Pater (2012a; 2012b) and Folia et al, (2010) for a review)
Using Event-Related Potentials (ERPs), we looked for neurophysiological correlates of implicit learning of a phonotactic rule
Using an artificial grammar learning design, this study showed that novel words violating a phonotactic constraint elicited a larger late positivity component (LPC) than novel words that satisfied it
Summary
Artificial grammar learning (AGL) studies have shown that learners can extract adjacent and nonadjacent phonotactic patterns with relatively short training in laboratory settings (see Moreton & Pater (2012a; 2012b) and Folia et al, (2010) for a review) This has been shown via behavioral measures such as reaction time, mean rating, accuracy, and d-prime: these behavioral measures show that learners can generalize from training stimuli. Initial exposure to a linguistic stimulus causes physical changes in neural tissue, which in turn reflect properties of the language input This means that if a rule is learned after exposure to repeated stimuli, this learning should lead to an instant, measurable "tuning" of the perceptual system. The MMN is a negative-going potential peaking at fronto-central electrode sites at early latencies (usually at 150–250 ms from the onset of the sound) It is an ERP component which reflects an automatic auditory change-detection response originating in the auditory cortex (Alho 1995; Näätänen et al 2007)
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