Many models of reference production suggest that speakers tend to use a reduced referential form, such as a pronoun, to signal the topicality of a particular referent, that is, the Topichood Hypothesis. However, little is known about the precise nature of the mapping between topichood and referential form and the mechanisms by which topichood affects referential form. The current study aims to address these issues by investigating how topicality influences different kinds of reduced expressions, namely, null and overt pronouns in Mandarin. We manipulated topicality using a left-dislocation structure in Experiment 1. We found that topicality increased the use of null pronouns, but not overt pronouns. This suggests that topicality may increase only the most reduced expression available in a given language. Experiment 2 examined whether the topicality effect was related to predictability. We found that participants used more null pronouns for less predictable referents. We suggest that the topicality effect could be better explained by an accessibility mechanism.
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http://dx.doi.org/10.1111/cogs.13190 | DOI Listing |
Purpose The aim of this study is to investigate the capability of generative pre-trained transformer 4 (GPT-4) and GPT-4o in identifying chest radiography reports requiring further assessment. Materials and methods This retrospective study included 100 cases from the National Institutes of Health chest radiography dataset, including 50 abnormal and 50 normal cases. A radiologist blinded to the study's purpose interpreted and reported the radiological findings for each case in English and separately determined the necessity for further assessment based on predefined criteria as referential standards.
View Article and Find Full Text PDFCureus
December 2024
Department of Integrated Psychological Sciences, School of Humanities, Kwansei Gakuin University, Nishinomiya, JPN.
Background and aim Subthreshold depression is a potential risk factor for major depressive disorder. Although the neurobiological mechanism underlying major depressive disorder is well-established, the mechanism underlying subthreshold depression has not yet been fully elucidated. We investigated the characteristics of brain abnormalities in participants with subthreshold depression using near-infrared spectroscopy (NIRS) owing to its portability.
View Article and Find Full Text PDFPsychiatry Res
December 2024
Department of Translation and Language Sciences, Universitat Pompeu Fabra, Barcelona, Spain; Catalan Institute for Advanced Studies and Research (ICREA), Barcelona, Spain.
Narrative speech production requires the retrieval of concepts to refer to entities, which need to be referenceable more than once for any form of narrative coherence to arise. Such coherence has long been observed to be affected in schizophrenia spectrum disorders (SSD), yet the underlying mechanisms have been a longstanding puzzle, with existing evidence predominantly derived from Indo-European languages. Here we analyzed two picture descriptions from 22 native Mandarin Chinese speakers with SSD and 15 healthy controls.
View Article and Find Full Text PDFJ Lesbian Stud
January 2025
Department of English, University of Miskolc, Miskolc, Hungary.
My paper analyses Ali Smith's innovative use of queering as a narrative strategy in (2007) and (2008), focusing on her transformation of narrative structures, epistemic realities, and identity through intertextual engagement. Smith's fiction queers temporality and narrative agency by reimagining classical and literary texts, including Ovid's , John Lyly's , Shakespeare's plays, and . I suggest that in , Smith reinterprets Ovid's myth of Iphis and Ianthe to celebrate fluid and transformative identities, intertwining this with feminist activism and queer desire.
View Article and Find Full Text PDFBehav Res Methods
December 2024
CAP Team, Centre de Recherche en Neurosciences de Lyon - INSERM U1028 - CNRS UMR 5292 - UCBL - UJM, 95 Boulevard Pinel, 69675, Bron, France.
Artificial intelligence techniques offer promising avenues for exploring human body features from videos, yet no freely accessible tool has reliably provided holistic and fine-grained behavioral analyses to date. To address this, we developed a machine learning tool based on a two-level approach: a first lower-level processing using computer vision for extracting fine-grained and comprehensive behavioral features such as skeleton or facial points, gaze, and action units; a second level of machine learning classification coupled with explainability providing modularity, to determine which behavioral features are triggered by specific environments. To validate our tool, we filmed 16 participants across six conditions, varying according to the presence of a person ("Pers"), a sound ("Snd"), or silence ("Rest"), and according to emotional levels using self-referential ("Self") and control ("Ctrl") stimuli.
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