This work represents one of the first attempts to examine the effects of meditation on the processing of written single words. In the present longitudinal study, participants conducted a lexical decision task and rated the affective valence of nouns before and after a 7-week class in mindfulness meditation, loving-kindness meditation, or a control intervention. Both meditation groups rated the emotional valence of nouns more neutral after the interventions, suggesting a general down-regulation of emotions. In the loving-kindness group, positive words were rated more positively after the intervention, suggesting a specific intensification of positive feelings. After both meditation interventions, response times in the lexical decision task accelerated significantly, with the largest facilitation occurring in the loving-kindness group. We assume that meditation might have led to increased attention, better visual discrimination, a broadened attentional focus, and reduced mind-wandering, which in turn enabled accelerated word recognition. These results extend findings from a previous study with expert Zen meditators, in which we found that one session of advanced meditation can affect word recognition in a very similar way.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8942899 | PMC |
http://dx.doi.org/10.1007/s00426-021-01522-5 | DOI Listing |
Sensors (Basel)
January 2025
SHCCIG Yubei Coal Industry Co., Ltd., Xi'an 710900, China.
The coal mining industry in Northern Shaanxi is robust, with a prevalent use of the local dialect, known as "Shapu", characterized by a distinct Northern Shaanxi accent. This study addresses the practical need for speech recognition in this dialect. We propose an end-to-end speech recognition model for the North Shaanxi dialect, leveraging the Conformer architecture.
View Article and Find Full Text PDFSci Rep
January 2025
Nanfang College Guangzhou, Guangzhou, 510970, China.
Named Entity Recognition (NER) is an essential component of numerous Natural Language Processing (NLP) systems, with the aim of identifying and classifying entities that have specific meanings in raw text, such as person (PER), location (LOC), and organization (ORG). Recently, Deep Neural Networks (DNNs) have been extensively applied to NER tasks owing to the rapid development of deep learning technology. However, despite their advancements, these models fail to take full advantage of the multi-level features (e.
View Article and Find Full Text PDFDisabil Rehabil Assist Technol
January 2025
School of Rehabilitation Therapy, Queen's University, Kingston, Ontario, Canada.
This article explores the existing research evidence on the potential effectiveness of lipreading as a communication strategy to enhance speech recognition in individuals with hearing impairment. A scoping review was conducted, involving a search of six electronic databases (MEDLINE, Embase, Web of Science, Engineering Village, CINAHL, and PsycINFO) for research papers published between January 2013 and June 2023. This study included original research papers with full texts available in English, covering all study designs: qualitative, quantitative, and mixed methods.
View Article and Find Full Text PDFBehav Sci (Basel)
January 2025
School of Foreign Languages, Ocean University of China, Qingdao 266005, China.
Collocations typically refer to habitual word combinations, which not only occur in texts but also constitute an essential component of the mental lexicon. This study focuses on the mental lexicon of Chinese learners of English as a foreign language (EFL), investigating the representation of collocations and the influence of input frequency and L2 proficiency by employing a phrasal decision task. The findings reveal the following: (1) Collocations elicited faster response times and higher accuracy rates than non-collocations.
View Article and Find Full Text PDFFront Neurosci
January 2025
The Basic Department, The Tourism College of Changchun University, Changchun, China.
Introduction: In the field of medical listening assessments,accurate transcription and effective cognitive load management are critical for enhancing healthcare delivery. Traditional speech recognition systems, while successful in general applications often struggle in medical contexts where the cognitive state of the listener plays a significant role. These conventional methods typically rely on audio-only inputs and lack the ability to account for the listener's cognitive load, leading to reduced accuracy and effectiveness in complex medical environments.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!