Meaning-driven syntactic predictions in a parallel processing architecture: Theory and algorithmic modeling of ERP effects.
Syntactic and semantic information processing can interact selectively during language comprehension. However, the nature and extent of the interactions, in particular of semantic effects on syntax, remain to some extent elusive. We revisit an influential ERP result by Kim and Osterhout (2005), later replicated by Kim and Sikos (2011), that the verb in sentences such as 'The hearty meal was devouring … ' evokes a P600 effect-a signature of syntactic processing difficulty-even though all stimuli were grammatically well-formed. We view this effect as a manifestation of a conflict in the assignment of grammatical subject and object roles to the verb's arguments as performed independently by a semantic system (predicting that meal should be the object) and by a syntactic system (labeling meal as the subject). More specifically, we develop an explicit algorithmic implementation of a parallel processing architecture that supports (i) meaning-based prediction of grammatical role labels, using either a probabilistic label guesser or a neural network, and (ii) comparison of the predicted labels with labels assigned by a state-of-the-art dependency parser. We demonstrate that the system can classify sentences from the Kim and Osterhout (2005) corpus with adequate accuracy, and can detect labeling conflicts as intended. Some implications of our results for models of prediction in language processing are discussed.
['Michalon|Olivier|O|', 'Baggio|Giosuè|G|']
D000465:Algorithms D032882:Comprehension D003198:Computer Simulation D005071:Evoked Potentials D006801:Humans D007802:Language D008962:Models, Theoretical D016571:Neural Networks, Computer
Prediction ERP Semantics Syntax P600 NLP Parallel architecture Parsing