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Visuo-spatial context and emotional valence are powerful cues to episodic retrieval, but the contribution of these inputs to semantic cognition has not been widely investigated. We examined the impact of visuo-spatial, facial emotion and prosody cues and miscues on the retrieval of dominant and subordinate meanings of ambiguous words. Cue photographs provided relevant visuo-spatial or emotional information, consistent with the interpretation of the ambiguous word being probed, while miscues were consistent with an alternative interpretation. We compared the impact of these cues in healthy controls and semantic aphasia patients with deficient control over semantic retrieval following left-hemisphere stroke. Patients showed greater deficits in retrieving the subordinate meanings of ambiguous words, and stronger effects of cueing and miscuing relative to healthy controls. These findings suggest that contextual cues that guide retrieval to the appropriate semantic information reduce the need to constrain semantic retrieval internally, while miscues that are not aligned with the task increase the need for semantic control. Moreover, both valence and visuo-spatial context can prime particular semantic interpretations, in line with theoretical frameworks that argue meaning is computed through the integration of these features. In semantic aphasia, residual comprehension relies heavily on facial expressions and visuospatial cues. This has important implications for patients, their families and clinicians when developing new or more effective modes of communication.
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http://dx.doi.org/10.1016/j.neuropsychologia.2019.05.030 | DOI Listing |
J Alzheimers Dis
September 2025
Institut des Sciences logopédiques, Faculté des Lettres et Sciences Humaines, University of Neuchâtel, Neuchâtel, Switzerland.
BackgroundThe production of verbal tenses is impaired in people with Alzheimer's disease (AD), as shown by several studies focusing on time reference and using sentence completion tasks. However, there is currently a limited understanding of how tense is produced in discourse with this disease. Discourse is interesting as it involves building a mental representation of the event to be narrated with its temporal framework and translating this framework into language using tense.
View Article and Find Full Text PDFBioinformatics
September 2025
Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA.
Summary: In the era of large data, the cloud is increasingly used as a computing environment, necessitating the development of cloud-compatible pipelines that can provide uniform analysis across disparate biological datasets. The Warp Analysis Research Pipelines (WARP) repository is a GitHub repository of open-source, cloud-optimized workflows for biological data processing that are semantically versioned, tested, and documented. A companion repository, WARP-Tools, hosts Docker containers and custom tools used in WARP workflows.
View Article and Find Full Text PDFIEEE Trans Med Imaging
September 2025
Computed Tomography (CT) to Cone-Beam Computed Tomography (CBCT) image registration is crucial for image-guided radiotherapy and surgical procedures. However, achieving accurate CT-CBCT registration remains challenging due to various factors such as inconsistent intensities, low contrast resolution and imaging artifacts. In this study, we propose a Context-Aware Semantics-driven Hierarchical Network (referred to as CASHNet), which hierarchically integrates context-aware semantics-encoded features into a coarse-to-fine registration scheme, to explicitly enhance semantic structural perception during progressive alignment.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
September 2025
In industrial scenarios, semantic segmentation of surface defects is vital for identifying, localizing, and delineating defects. However, new defect types constantly emerge with product iterations or process updates. Existing defect segmentation models lack incremental learning capabilities, and direct fine-tuning (FT) often leads to catastrophic forgetting.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
September 2025
Sentence-level semantics plays a key role in language understanding. There exist subtle relations and dependencies among sentence-level samples, which is to be exploited. For example, in relational triple extraction, existing models overemphasize extraction modules, ignoring the sentence-level semantics and relation information, which causes (1) the semantics fed to extraction modules is relation-unaware; (2) each sample is trained individually without considering inter-sample dependency.
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