UNCERTAIN · How the Brain Predicts under Uncertainty in Language Comprehension
„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“
- Период
- 2026-10-01 → 2028-09-30
- Финансиране от ЕС
- 232 916 €
- Участници
- 1
- Схема
- HORIZON-TMA-MSCA-PF-EF
Линиите свързват координатора с партньорите.
Накратко на български
Мозъкът се изследва при разбирането на езика, за да се види как променя прогнозите си, когато контекстът е неясен. Това помага да се разбере как хората комуникират и как се адаптират към неопределеността в реални ситуации.
Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.
Цел на проекта
The human brain constantly predicts input. In language comprehension, such predictions are shaped by contextual uncertainty: listeners rely on context when input is unreliable. Yet, it remains unclear how the brain adapts predictive strategies when semantic context is uncertain. This project investigates whether language comprehension under uncertainty follows the principle of precision-weighted prediction errors, a hallmark of predictive coding, whereby neural prediction errors are dampened in uncertain contexts. To test this, I will use advanced MEG/EEG neuroimaging and computational modeling across two studies that complement the causal power of a controlled experiment with the ecological validity of naturalistic comprehension. Study 1 employs a semantic volatility 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.
Оригинален текст от CORDIS (на английски).
Участници
- STICHTING RADBOUD UNIVERSITEIT · NijmegenКоординаторНидерландия
Връзки
Данни: CORDIS, © Европейски съюз
