Master of Science
Batterink, Laura J
Statistical learning (SL) refers to the ability to extract regularities in the environment and has been well-documented to play a key role in speech segmentation and language acquisition. Whether SL is automatic or requires top-down attention is an unresolved question, with conflicting results in the literature. The current proposal tests whether SL can occur outside the focus of attention. Participants either focused towards, or diverted their attention away from an auditory speech stream made of repeating nonsense trisyllabic words. Divided-attention participants either performed a concurrent visual task or a language-related task during exposure to the nonsense speech stream, while control participants focused their attention to the speech stream. Visual attention was taxed through the classic Multiple Object Tracking paradigm, requiring tracking of multiple randomly moving dots. Linguistic attention was taxed through a self-paced reading task. Following speech exposure, SL was assessed with offline tests, including a post-exposure explicit familiarity rating task, and an implicit reaction-time (RT) based syllable detection task.
On the explicit familiarity rating measure, participants showed a reduction in learning when language-specific processing was taxed as compared to when visual resources were taxed. On the more implicit reaction time-based measure of SL, both divided-attention and full-attention controls performed comparably, all showing evidence of SL. These results suggest SL can proceed even when domain-specific (visual) resources are limited, but is compromised when more specific, language-related resources are taxed. These results offer insight into the neural cognitive underpinnings of SL and have exciting practical applications for improving adult second language acquisition.
Summary for Lay Audience
Listening to an unfamiliar language can often be a disorienting experience. Natural speech is devoid of reliable pauses between words, making it difficult to determine where the words start and end. One way we can discover word boundaries is through statistical learning (SL), which refers to the ability to detect patterns in the world. SL is thought to play a key role in speech segmentation and language acquisition. Syllables within word boundaries tend to occur more frequently than syllables across word boundaries, and the ability to become sensitive to these statistical relationships between syllables is one way we can segment speech. Whether SL is automatic or requires focused attention is an unresolved question. The current study examined whether SL can occur outside the focus of attention, using both an explicit measure, as well as a more implicit reaction time-based task. Participants’ either focused their attention towards, or diverted their attention away from an auditory nonsense speech stream. Participants who did not pay attention to the nonsense speech stream completed a task designed to tax either visual resources, or linguistic resources. Visual resources were taxed with an object tracking task, involving tracking a subset of randomly moving dots. Linguistic resources were taxed with a self-paced reading task, where participants read sentences and answered comprehension questions. Results showed that explicit learning was only reduced when linguistic resources were taxed, but unimpaired when visual resources were taxed. On the more implicit measure of SL, both divided-attention and full-attention controls performed comparably. These results demonstrate that SL can still occur to some extent when linguistic resources are taxed, and can occur uninterrupted with visual resources are taxed, providing support for the relative automaticity of SL. Practically, these results suggest language learners may engage in visual tasks when learning a new language.
Reyes, Stacey, "The Role of Top-down Attention in Statistical Learning of Speech" (2021). Electronic Thesis and Dissertation Repository. 8090.