Two experiments were conducted to investigate how linguistic information influences attention allocation in visual search and memory for words. In Experiment 1, participants searched for the synonym of a cue word among five words. The distractors included one antonym and three unrelated words. In Experiment 2, participants were asked to judge whether the five words presented on the screen comprise a valid sentence. The relationships among words were sentential, semantically related or unrelated. A memory recognition task followed. Results in both experiments showed that linguistically related words produced better memory performance. We also found that there were significant interactions between linguistic relation conditions and memorization on eye-movement measures, indicating that good memory for words relied on frequent and long fixations during search in the unrelated condition but to a much lesser extent in linguistically related conditions. We conclude that semantic and syntactic associations attenuate the link between overt attention allocation and subsequent memory performance, suggesting that linguistic relatedness can somewhat compensate for a relative lack of attention during word search.
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http://dx.doi.org/10.1080/00221309.2016.1258389 | DOI Listing |
Introduction: Broca's Aphasia (BA) is a language disorder that causes grammatical errors in the language production skills of patients. Contemporary studies revealed the fact that BA patients also have difficulty in analyzing the meaning of phrases and sentences and comprehending the real meaning of the discourse produced by the speaker. The purpose of this study is to investigate possible effect of syntactic movement by changing the word positions in the sentence with morphological markers in order to produce clauses without changing the meaning on the phrasal comprehension skills of Turkish speaking patients with BA.
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January 2025
Center for Cognitive Interaction Technology (CITEC), Technical Faculty, Bielefeld University, Bielefeld, Germany.
Background: In the field of structured information extraction, there are typically semantic and syntactic constraints on the output of information extraction (IE) systems. These constraints, however, can typically not be guaranteed using standard (fine-tuned) encoder-decoder architectures. This has led to the development of constrained decoding approaches which allow, e.
View Article and Find Full Text PDFData Brief
February 2025
Arabic Department, University of Sharjah, UAE.
This paper introduces the Morphologically-Analyzed and Syntactically-Annotated Quran (MASAQ) dataset, a comprehensive resource designed to address the scarcity of annotated Quranic Arabic corpora and facilitate the development of advanced Natural Language Processing (NLP) models. The Quran, being a cornerstone of classical Arabic, presents unique challenges for NLP due to its sacred nature and complex linguistic features. MASAQ provides a detailed syntactic and morphological annotation of the entire Quranic text, utilizing a rigorously verified text from Tanzil.
View Article and Find Full Text PDFSci 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.
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