Syllabic rhythm and prior linguistic knowledge interact with individual differences to modulate phonological statistical learning.

Phonological statistical learning - our ability to extract meaningful regularities from spoken language - is considered critical in the early stages of language acquisition, in particular for helping to identify discrete words in continuous speech. Most phonological statistical learning studies use an experimental task introduced by Saffran et al. (1996), in which the syllables forming the words to be learned are presented continuously and isochronously. This raises the question of the extent to which this purportedly powerful learning mechanism is robust to the kinds of rhythmic variability that characterize natural speech. Here, we tested participants with arhythmic, semi-rhythmic, and isochronous speech during learning. In addition, we investigated how input rhythmicity interacts with two other factors previously shown to modulate learning: prior knowledge (syllable order plausibility with respect to participants' first language) and learners' speech auditory-motor synchronization ability. We show that words are extracted by all learners even when the speech input is completely arhythmic. Interestingly, high auditory-motor synchronization ability increases statistical learning when the speech input is temporally more predictable but only when prior knowledge can also be used. This suggests an additional mechanism for learning based on predictions not only about when but also about what upcoming speech will be.

Speech-to-Speech Synchronization protocol to classify human participants as high or low auditory-motor synchronizers.

The ability to synchronize a motor action to a rhythmic auditory stimulus is often considered an innate human skill. However, some individuals lack the ability to synchronize speech to a perceived syllabic rate. Here, we describe a simple and fast protocol to classify a single native English speaker as being or not being a speech synchronizer. This protocol consists of four parts: the pretest instructions and volume adjustment, the training procedure, the execution of the main task, and data analysis. For complete details on the use and execution of this protocol, please refer to Assaneo et al. (2019a).