SOURCE-LINKED INTELLIGENCE

How the Brain Predicts under Uncertainty in Language Comprehension

CORDIS · observation · Publication date unknown

ility paradigm to test whether neural signals of prediction errors are dynamically modulated by contextual stability. Study 2 examines naturalistic story comprehension, using entropy estimates from a large language model to determine whether word-by-word prediction error signals are scaled by contextual uncertainty. Together, these studies will identify the neural markers of precision-weighted semantic prediction errors and establish whether predictive gain modulation under uncertainty shapes language processing. Moreover, findings will disentangle the role of two key types of uncertainty – volatility and entropy – reflecting distinct computational challenges. The project advances predictive coding theory by testing its domain-generality, bridges cognitive neuroscience, psycholinguistics, and artificial intelligence, and advances research on language processing. By clarifying how predictive mechanisms adapt to uncertainty in language, the project strengthens models of human communication and may provide a conceptual basis for future diagnostic and technological applications. cognitiv

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recordType

award

status

SIGNED

region

EU

value

232916.16

unit

EUR