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Prior studies of A:B::C:D verbal analogies have identified several factors that affect performance, including the semantic similarity between source and target domains (semantic distance), the semantic association between the C-term and incorrect answers (distracter salience), and the type of relations between word pairs. However, it is unclear how these stimulus properties affect performance when utilized together. To test their interactive effects, we created a verbal analogy stimulus set that factorially crossed these factors and presented participants with an analogical stem (A:B::C:?) with two response choices: an analogically correct (D) and incorrect distracter (D') term. The semantic distance between source and target word pairs was manipulated creating near (BOWL:DISH::SPOON:SILVERWARE) and far (WRENCH:TOOL::SAD:MOOD) analogies. The salience of an incorrect distracter (D') was manipulated using the sematic distance with the C-term creating low (DRAWER) and high (FORK) salience distracters. Causal, compositional, and categorical relations were presented across these conditions. Accuracies were higher for semantically near than far analogies and when distracter salience was low than high. Categorical relations yielded better performance than the causal and compositional relations. Moreover, a three-way interaction demonstrated that the effects of semantic distance and distracter salience had a greater impact on performance for compositional and causal relations than for the categorical ones. We theorize that causal and compositional analogies, given their less semantically constrained responses, require more inhibitory control than more constraining relations (e.g., categorical).
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http://dx.doi.org/10.3758/s13423-022-02062-8 | DOI Listing |
Neural Netw
September 2025
Shanghai Maritime University, Shanghai, 201306, China. Electronic address:
Cross-modal hashing aims to leverage hashing functions to map multimodal data into a unified low-dimensional space, realizing efficient cross-modal retrieval. In particular, unsupervised cross-modal hashing methods attract significant attention for not needing external label information. However, in the field of unsupervised cross-modal hashing, there are several pressing issues to address: (1) how to facilitate semantic alignment between modalities, and (2) how to effectively capture the intrinsic relationships between data, thereby constructing a more reliable affinity matrix to assist in the learning of hash codes.
View Article and Find Full Text PDFNeural Netw
August 2025
Inspur Electronic Information Industry Co., Ltd, China.
Knowledge distillation (KD) makes it possible to deploy high-accuracy models on devices with limited resources and is an effective means of achieving lightweight models. With the advancement of technology, the methods of knowledge distillation are also continuously developing and improving to adapt to different application scenarios and needs. To facilitate the transfer of knowledge from larger networks to smaller and lighter networks, KD has been employed to bridge the gap in probability outputs or middle-layer representations between teacher and student networks.
View Article and Find Full Text PDFComput Med Imaging Graph
September 2025
Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai 200062, China.
An increasing number of publicly available datasets have facilitated the exploration of building universal medical segmentation models. Existing approaches address partially labeled problem of each dataset by harmonizing labels across datasets and independently focusing on the labeled foreground regions. However, significant challenges persist, particularly in the form of cross-site domain shifts and the limited utilization of partially labeled datasets.
View Article and Find Full Text PDFSci Rep
September 2025
Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kaferelshikh University, Kaferelshikh, 33511, Egypt.
Medical images have become indispensable for decision-making and significantly affect treatment planning. However, increasing medical imaging has widened the gap between medical images and available radiologists, leading to delays and diagnosis errors. Recent studies highlight the potential of deep learning (DL) in medical image diagnosis.
View Article and Find Full Text PDFDiagnostics (Basel)
August 2025
Department of Orthopedics, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
: Lumbar spondylolisthesis (LS) is a common spinal disorder characterized by the forward displacement of the vertebra. Early detection is challenging due to asymptomatic presentation in the early stages. This study develops and validates an AI-based deep learning model for the early, high-precision diagnosis of LS using lumbar X-ray images.
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