In the frame of Subba Oota’s Phd defense : « Neuro-Computational Models of Language Comprehension: characterizing similarities and differences between language processing in brains and language models »
Venue: CGFB
Program
9h15 : Accueil café
9h35 : Présentation de la matinée
9h45 : Stefan Frank (Centre for Language Studies, Radboud University, Nijmegen, Netherlands)
**Stefan Frank**
Associate Professor of Psycholinguistics, Universitair Hoofddocent Taalpsychologie, Centre for Language Studies, Radboud University, Nijmegen, Netherlands
Talk: **The role of backpropagated prediction error in sentence acceptability and ERPs**
Fitz & Chang (2019) argue that event-related brain potentials (ERPs) during sentence comprehension result from the detection and incorporation of word-prediction error. The N400 component would correlate with prediction error while the P600 would be indicative of error backpropagation in the language system. Psycholinguistically speaking, the latter is an estimate of how much the comprehended sentence can engender change in a person’s believes about the language’s grammar. I will present an evaluation of this hypothesis on a corpus of EEG data recorded from participants reading naturalistic English sentences. Moreover, I will demonstrate that backpropagated error also accounts for the subjective acceptability of sentences.
10h40 : Courte pause
10h50 : Christophe Pallier (NeuroSpin, INSERM-CEA, Saclay, France)
**Christophe Pallier**
Directeur de Recherche CNRS, EMR CNRS 9003 & INSERM-CEA Cognitive Neuroimaging, Lab U992, Neurospin, CEA/SAC/DRF/Joliot, Saclay, France)
Talk : In search of syntactic representations in the brain
Do representations proposed in linguistic theories, such as constituent trees, correspond to actual data structures constructed in real-time in the brain during language comprehension? And if so, what are the brain regions involved? This question was investigated in a series of functional magnetic resonance studies using various experimental paradigms, including repetition priming, syntactic complexity manipulation, and NLP models trained on limited corpora. I will argue that while many questions remain unanswered, progress has been made. For example, the results suggest that full syntactic parsing of sentences may not happen automatically, but that local syntactic operations (merge) do. The use of deep learning models to locate syntactic and semantic information in the brain will also be discussed.
11h45 : Table ronde + Fabien Lotte (Inria) + Gaël Jobard (U. Bordeaux, IMN, GIN)
12h15 : Fin