Meaning-driven syntactic predictions in a parallel processing architecture: Theory and algorithmic modeling of ERP effects.

Clicks: 457
ID: 96738
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #1 of 20 articles by views in neuropsychologia

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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.
Reference Key
michalon2019meaningdrivenneuropsychologia Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Michalon, Olivier;Baggio, Giosuè;
Journal neuropsychologia
Year 2019
DOI
S0028-3932(19)30116-2
URL
Keywords
semantics prediction erp p600 syntax nlp parallel architecture parsing

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.