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Is Reverse-Correlation Appropriate for Uncovering (Para)linguistic Inferences from Speech Prosody?| title | Is Reverse-Correlation Appropriate for Uncovering (Para)linguistic Inferences from Speech Prosody? |
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| start_date | 2025/07/09 |
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| schedule | 11h |
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| online | no |
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| location_info | amphi Jean-Jacques Gagnepain |
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| summary | Reverse-correlation applied to prosody provides a principled method for uncovering the prototypical representations underlying high-level inferences from speech signals. This approach holds promise for studying cross-cultural differences in emotional and social judgments, as well as impairments in individuals with specific pathologies or neurological disorders. However, a critical assumption of this method is that the brain’s inference algorithm can be approximated by linear template-matching—where prosodic judgments arise from comparing speech signals to a single internal prototype via a dot product. This assumption, though foundational, has never been empirically tested. In this talk, I will present two novel approaches to evaluate whether (para)linguistic inferences from prosody are well captured by linear template-matching, using existing published datasets. First, we explore alternative visualization techniques for reverse-correlation data to better discriminate between competing computational models. Second, we categorize trials based on linguistic-theoretical criteria and assess how effectively template-matching explains trial-by-trial responses across different categories. These directions aim to refine our understanding of the computational mechanisms behind prosodic inference and assess the validity of reverse-correlation in this domain. |
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| responsibles | Aucouturier, Villain |
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Workflow history| from state (1) | to state | comment | date |
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| submitted | published | | 2025/07/01 07:16 UTC |
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