Presentation
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The Architecture of Auditory Statistical Learning in the Brain: A Dynamical Functional Connectivity Study
Poster D104 in Poster Session D, Wednesday, October 25, 4:45 - 6:30 pm CEST, Espace Vieux-Port
This poster is part of the Sandbox Series.
Will Decker1, Julie M. Schneider1, Christopher Cox1; 1Louisiana State University
Statistical learning (SL) is a neurocognitive mechanism used to identify patterns of statistical regularity embedded within the environment (Conway, 2020). SL is not a discrete process—it occurs continuously over time (Conway, 2020). In particular, SL supports the identification of boundaries across the temporal domain via tracking of transitional probabilities (TP) within patterns of input (Cairns et al., 1997; Saffran et al., 1996;1999), like word boundaries in spoken language. In this regard, foundational observations from Saffran et al. (1996;1999) demonstrated that infants were able to identify words embedded within a continuous stream of auditory stimuli, elucidating SL’s role in learning language. From this, investigations into the cognitive mechanisms supporting SL of language arose: a dual model was constructed in which domain-general (i.e., cognitive computations occurring during SL are shared across domains/modalities) and domain-specific mechanisms (i.e., cognitive computations occurring during SL are distinct across domains/modalities) work in tandem to facilitate perceptual and executive functioning—in turn supporting SL (Conway, 2020; Frost et al., 2015). To further subserve this understanding of SL and language, explorations into the neural basis of SL demonstrated that domain-general regions, like the superior temporal gyrus (STG) and the inferior temporal gyrus (IFG; Karuza et al., 2013), as well as domain/modality-specific neural regions are activated during processing of patterns embedded within auditory input (Schneider, et al., preprint; Thothathiri & Rattinger, 2015). While informative, these findings limit their categorization of learning to modular when, rather, it is facilitated by distributed networks and connections across the brain (e.g., López-Barroso et al., 2015). To understand how the brain processes statistical patterns at a distributed level, functional connectivity (FC) analyses can be used to determine the interactions of brain regions underpinning cognitive functioning. Little research has utilized FC to examine auditory SL as it unfolds over time. This sparse literature has demonstrated activation of domain-general regions involved in executive functioning, such as working memory and attention (Sengupta et al., 2019). However, the lack of domain/modality-specific regions involved in SL is contrary to theoretical accounts of SL (see Conway et al., 2020), and may be attributed to the fact that previous FC analyses did not account for changes across time. Therefore, the current project aims to uncover the functional architecture of SL as it unfolds over time—making use of functional magnetic resonance imaging (fMRI) and dynamical FC analyses. Adult participants will complete a baseline resting state (RS) FC scan followed by a task-based auditory SL FC scan, similar to Schneider et al., (2020). They will be tested on accuracy during a 4AFC post-test. We hypothesize that the stimulus onset will probe connections in perceptual regions involved in auditory SL (STG, IFG) and an increase in stimulus duration will reduce these connections while simultaneously increasing connections in top-down, executive regions. We further hypothesize that characteristics in FC will be related to participants’ accuracy on the post-test. These findings would provide a characterization of the neural mechanisms supporting SL over time, a novel finding that would bolster theoretical accounts of SL.
Topic Areas: Control, Selection, and Executive Processes,