During conversations participants alternate smoothly between speaker and hearer roles with only brief pauses and overlaps. There are two competing types of accounts about how conversationalists accomplish this: (a) the signaling approach and (b) the anticipatory ('projection') approach. We wanted to investigate, first, the relative merits of these two accounts, and second, the relative contribution of semantic and syntactic information to the timing of next turn initiation. We performed three button-press experiments using turn fragments taken from natural conversations to address the following questions: (a) Is turn-taking predominantly based on anticipation or on reaction, and (b) what is the relative contribution of semantic and syntactic information to accurate turn-taking. In our first experiment we gradually manipulated the information available for anticipation of the turn end (providing information about the turn end in advance to completely removing linguistic information). The results of our first experiment show that the distribution of the participants' estimation of turn-endings for natural turns is very similar to the distribution for pure anticipation. We conclude that listeners are indeed able to anticipate a turn-end and that this strategy is predominantly used in turn-taking. In Experiment 2 we collected purely reacted responses. We used the distributions from Experiments 1 and 2 together to estimate a new dependent variable called Reaction Anticipation Proportion. We used this variable in our third experiment where we manipulated the presence vs. absence of semantic and syntactic information by low-pass filtering open-class and closed class words in the turn. The results suggest that for turn-end anticipation, both semantic and syntactic information are needed, but that the semantic information is a more important anticipation cue than syntactic information.
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http://dx.doi.org/10.3389/fpsyg.2015.00089 | DOI Listing |
Sci Rep
January 2025
EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data.
View Article and Find Full Text PDFBrain Lang
January 2025
School of Communication Sciences, Beijing Language and Culture University, Beijing 100083, China.
How our brain integrates single words into larger linguistic units is a central focus in neurolinguistic studies. Previous studies mainly explored this topic at the semantic or syntactic level, with few looking at how cortical activities track word sequences with different levels of semantic correlations. In addition, prior research did not tease apart the semantic factors from the syntactic ones in the word sequences.
View Article and Find Full Text PDFBrain Struct Funct
January 2025
CHRIST (Deemed to be University), Bangalore, Karnataka, India.
In this investigation, we delve into the neural underpinnings of auditory processing of Sanskrit verse comprehension, an area not previously explored by neuroscientific research. Our study examines a diverse group of 44 bilingual individuals, including both proficient and non-proficient Sanskrit speakers, to uncover the intricate neural patterns involved in processing verses of this ancient language. Employing an integrated neuroimaging approach that combines functional connectivity-multivariate pattern analysis (fc-MVPA), voxel-based univariate analysis, seed-based connectivity analysis, and the use of sparse fMRI techniques to minimize the interference of scanner noise, we highlight the brain's adaptability and ability to integrate multiple types of information.
View Article and Find Full Text PDFBrain Sci
December 2024
Faculty of Arts and Humanities, University of Macau, Macau SAR 999078, China.
Background/objectives: Previous studies have examined the role of working memory in cognitive tasks such as syntactic, semantic, and phonological processing, thereby contributing to our understanding of linguistic information management and retrieval. However, the real-time processing of phonological information-particularly in relation to suprasegmental features like tone, where its contour represents a time-varying signal-remains a relatively underexplored area within the framework of Information Processing Theory (IPT). This study aimed to address this gap by investigating the real-time processing of similar tonal information by native Cantonese speakers, thereby providing a deeper understanding of how IPT applies to auditory processing.
View Article and Find Full Text PDFNeural Netw
December 2024
Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China. Electronic address:
Document-level event causality identification (ECI) aims to detect causal relations in between event mentions in a document. Some recent approaches model diverse connections in between events, such as syntactic dependency and etc., with a graph neural network for event node representation learning.
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