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Idioms are semantically non-compositional multiword units whose meanings often go beyond literal interpretations of their component words (e.g., break the ice, kick the bucket, spill the beans). According to hybrid models of idiom processing, idioms are subject to both direct retrieval from the lexicon in early stages of processing, and word-by-word compositional reanalysis in later stages of comprehension. However, a less clear aspect is how disrupting an idiom's canonical form, and thus its direct retrieval, impacts the time course of comprehension. In this eye-tracking reading study, healthy English-French bilingual adults with English as their dominant language read sentences containing English idioms in their canonical form (e.g., break the ice), or in a switched form where the phrase-final noun was translated into French (e.g., break the glace). Thus, within this manipulation, momentary language switches modified the canonical form of idioms, while at the same time minimally altering the semantics of their component words, thus nudging readers towards a compositional processing route. Analyses of eye-movement data revealed switching costs in longer reading times at early (but not late) processing stages for idioms compared to matched literal phrases. Interestingly, the cost of language switching was attenuated by the availability of a translationally equivalent idiom in the non-target language (French, e.g., briser la glace). Taken together, these results suggest that direct retrieval is the preferential route in the comprehension of idioms' canonical forms, which acts as an effective repair strategy by the language-processing system when recovering the underlying form of modified idioms.
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http://dx.doi.org/10.3758/s13421-022-01334-x | DOI Listing |
J Telemed Telecare
September 2025
School of Medicine, The University of Queensland, St Lucia, QLD, Australia.
In this case, we describe the remote telehealth leadership of emergent tube thoracostomy in a patient with a critical respiratory status. The patient had presented to a small rural health care facility with breathlessness and hypoxia despite supplemental oxygen. A subsequent chest x-ray revealed a large pneumothorax requiring emergent treatment to prevent respiratory demise.
View Article and Find Full Text PDFClin Exp Dent Res
October 2025
Department of Dentistry, Universidade Federal do Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil.
Objective: Through a scoping review, this study meticulously mapped and characterized these nanostructured clays used to release antibacterial active compounds from direct restorative dental materials.
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Front Hum Neurosci
August 2025
Department of Psychology, Northeastern University, Boston, MA, United States.
Mentalizing skills-the capacity to attribute mental states-play critical roles in word learning during typical language development. In autism, mentalizing difficulties may constrain word-learning pathways, limiting language-acquisition opportunities. We ask how autistic children encode and retrieve novel words and what drives individual differences.
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Department of Orthopaedics, Lanzhou University Second Hospital, Lanzhou, Gansu, 730000, People's Republic of China.
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Methods: Randomized controlled trials (RCTs) evaluating PVB and ESPB for postoperative analgesia and recovery were retrieved from databases, including PubMed, Embase, MEDLINE, Cochrane Library, Science-Direct, and Google Scholar, from inception to January 2025. The primary outcome included resting Visual Analogue Scale (VAS) at 6 h and quality of recovery (QoR) score in first 24 h.
Biomed Eng Lett
September 2025
Department of Precision Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea.
Unlabelled: Foundation models, including large language models and vision-language models (VLMs), have revolutionized artificial intelligence by enabling efficient, scalable, and multimodal learning across diverse applications. By leveraging advancements in self-supervised and semi-supervised learning, these models integrate computer vision and natural language processing to address complex tasks, such as disease classification, segmentation, cross-modal retrieval, and automated report generation. Their ability to pretrain on vast, uncurated datasets minimizes reliance on annotated data while improving generalization and adaptability for a wide range of downstream tasks.
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