analiza aiqbench
This study provides critical empirical evidence that contextual semantic relevance, rather than local surprisal alone, robustly predicts fMRI BOLD signals during naturalistic speech processing. By validating this metric across two distinct datasets using advanced modeling techniques like GAMMs and FIR deconvolution, the research refines computational models of language comprehension to better account for slow hemodynamic responses tied to contextual integration. These findings are essential for neuroscientists and AI researchers aiming to align neural decoding methods with more accurate semantic representations in continuous listening scenarios.
Contextual Semantic Relevance Tracks fMRI BOLD Responses During Naturalistic Speech Comprehension
arXiv:2607.15856v1 Announce Type: new Abstract: Naturalistic language comprehension requires listeners to process both local probabilistic expectations and contextual semantic relations. Surprisal has been widely used to quantify local word unexpectedness, but evidence that it robustly predicts fMRI BOLD responses during continuous comprehension has been mixed. This study investigates whether contextual semantic relevance, defined as how strongly an incoming word relates to its recent semantic context, predicts BOLD responses during naturalistic speech comprehension. We analyzed two public fMRI datasets, the Alice dataset and the Moth dataset, treating them as complementary rather than identical replications. Transformed BOLD responses were modeled with generalized additive mixed models (GAMMs) and original continuous BOLD time series were tested with FIR/deconvolution analyses. In Alice, semantic relevance was significant across all 12 ROIs (region of interest), whereas surprisal was not significant after FDR correction. In Moth, semantic relevance showed consistent negative effects across all 30 ROIs, while surprisal showed no comparable pattern. These findings suggest that semantic relevance is a promising BOLD-sensitive metric of contextual semantic fit. More broadly, our findings support the view that slow hemodynamic responses during naturalistic speech comprehension may be especially sensitive to contextual semantic integration, whereas local probabilistic prediction error may be more difficult to detect reliably with fMRI. In this sense, semantic relevance extends computational models of language comprehension from prediction alone toward context-sensitive semantic integration.
Abstract:Naturalistic language comprehension requires listeners to process both local probabilistic expectations and contextual semantic relations. Surprisal has been widely used to quantify local word unexpectedness, but evidence that it robustly predicts fMRI BOLD responses during continuous comprehension has been mixed. This study investigates whether contextual semantic relevance, defined as how strongly an incoming word relates to its recent semantic context, predicts BOLD responses during naturalistic speech comprehension. We analyzed two public fMRI datasets, the Alice dataset and the Moth dataset, treating them as complementary rather than identical replications. Transformed BOLD responses were modeled with generalized additive mixed models (GAMMs) and original continuous BOLD time series were tested with FIR/deconvolution analyses. In Alice, semantic relevance was significant across all 12 ROIs (region of interest), whereas surprisal was not significant after FDR correction. In Moth, semantic relevance showed consistent negative effects across all 30 ROIs, while surprisal showed no comparable pattern. These findings suggest that semantic relevance is a promising BOLD-sensitive metric of contextual semantic fit. More broadly, our findings support the view that slow hemodynamic responses during naturalistic speech comprehension may be especially sensitive to contextual semantic integration, whereas local probabilistic prediction error may be more difficult to detect reliably with fMRI. In this sense, semantic relevance extends computational models of language comprehension from prediction alone toward context-sensitive semantic integration.
Computation and Language (cs.CL)
arXiv:2607.15856 [cs.CL]
(or arXiv:2607.15856v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.15856 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 314 cuvinte
Subiecte conexe
Contextual Semantic Relevance Tracks fMRI BOLD Responses During Naturalistic Speech Comprehension
arXiv · 4 aug. 2026
Research Note: Filtering Subversion-Relevant Information From Pretraining Data Is Feasible
LessWrong · 5 oct. 2026
Presentation: Building Evals for AI Adoption: from Principles to Practice
InfoQ · 29 mai 2026
[AINews] not much happened today
Latent Space · 10 sept. 2026