It was recently proposed that lexical prediction in sentence context encompasses two qualitatively distinct prediction mechanisms: "pre-activation," namely, activating representations stored in long-term memory, and "pre-updating," namely, updating the sentence's representation, built online in working memory (WM), to include the predicted content [Lau, E. F., Holcomb, P. J., & Kuperberg, G. R. Dissociating N400 effects of prediction from association in single-word contexts. 484-502, 2013]. The current study sought to find evidence for pre-updating and test the influence of individual differences in WM capacity on the tendency to engage in this process. Participants read strongly and weakly constraining sentences. ERPs were measured on the predictable noun as well as on the preceding verb, where the prediction is generated. Increased P600 amplitude was observed at the verb in the strongly constraining sentences, reflecting integration of the predicted upcoming argument, thus providing evidence for pre-updating. This effect was greater for participants with higher WM capacity, indicating that the tendency to engage in pre-updating is highly affected by WM capacity. The opposite effect was observed at the noun, that is, for participants with higher WM span, a greater decrease in P600 amplitude in the strongly constraining sentences was observed, indicating that the integration of a pre-updated word was easier. We discuss these results in light of previous literature and propose a plausible architecture to account for the interplay between pre-activation and pre-updating, mediating the influence of factors such as WM capacity.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1162/jocn_a_01322 | DOI Listing |
J Exp Psychol Gen
December 2024
Department of Psychology, Harvard University.
It is well-established that people make predictions during language comprehension--the nature and specificity of these predictions, however, remain unclear. For example, do comprehenders routinely make predictions about which words (and phonological forms) might come next in a conversation, or do they simply make broad predictions about the gist of the unfolding context? Prior EEG studies using tightly controlled experimental designs have shown that form-based prediction can occur during comprehension, as N400s to unexpected words are reduced when they resemble the form of a predicted word (e.g.
View Article and Find Full Text PDFEar Hear
November 2024
Department of Communication Sciences and Disorders, University of Haifa, Haifa, Israel; and.
Objectives: The present study aimed to examine the involvement of listening effort among multilinguals in their first (L1) and second (L2) languages in quiet and noisy listening conditions and investigate how the presence of a constraining context within sentences influences listening effort.
Design: A group of 46 young adult Arabic (L1)-Hebrew (L2) multilinguals participated in a listening task. This task aimed to assess participants' perceptual performance and the effort they exert (as measured through pupillometry) while listening to single words and sentences presented in their L1 and L2, in quiet and noisy environments (signal to noise ratio = 0 dB).
Sci Data
November 2024
Harbin Institute of Technology, Department of Computer Science, Harbin, 150000, China.
The rapid advancement of deep learning has enabled Brain-Computer Interfaces (BCIs) technology, particularly neural decoding techniques, to achieve higher accuracy and deeper levels of interpretation. Interest in decoding imagined speech has significantly increased because its concept akin to "mind reading". However, previous studies on decoding neural language have predominantly focused on brain activity patterns during human reading.
View Article and Find Full Text PDFCognition
January 2025
Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
When producing a sentence, speakers must rapidly select appropriate words in the correct order. Models of lexical access often assume that this lexical selection process is competitive and that each word is chosen from a set of competing candidates. Therefore, an important theoretical issue is which factors constrain this choice.
View Article and Find Full Text PDFJ Speech Lang Hear Res
December 2024
Department of Speech, Language, and Hearing Sciences, University of Florida, Gainesville.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!