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Retrieved memories of past events are often inaccurate. The scenario construction model (SCM) postulates that during encoding, only the gist of an episode is stored in the episodic memory trace and during retrieval, information missing from that trace is constructed from semantic information. The current study aimed to find behavioural evidence for semantic construction in a realistic, yet controlled setting by introducing a completely new paradigm and adjusted memory tests that measure semantic construction. Using a desktop virtual reality (VR), participants navigated through a flat in which some household objects appeared in unexpected rooms, creating conflicts between the experienced episode and semantic expectations. The manipulation of congruence enabled us to identify influences from semantic information in cases of episodic memory failure during recall. Besides, we controlled for objects to be task-relevant or task-irrelevant to the sequence of action. In addition to an established old/new recognition task we introduced spatial and temporal recall measures as possible superior memory measures quantifying semantic construction. The recognition task and the spatial recall revealed that both congruence and task-relevance predicted correct episodic memory retrieval. In cases of episodic memory failure, semantic construction was more likely than guessing and occurred more frequently for task-irrelevant objects. In the temporal recall object-pairs belonging to the same semantic room-category were temporally clustered together compared with object-pairs from different semantic categories (at the second retrieval delay). Taken together, our findings support the predictions of the SCM. The new VR paradigm, including the new memory measures appears to be a promising tool for investigating semantic construction.
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http://dx.doi.org/10.1177/17470218221116610 | DOI Listing |
Cereb Cortex
August 2025
Department of Psychology, University of Milano-Bicocca, Milan, Italy.
Semantic composition allows us to construct complex meanings (e.g., "dog house", "house dog") from simpler constituents ("dog", "house").
View Article and Find Full Text PDFJ Korean Med Sci
September 2025
Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, Korea.
Background: With the increasing incidence of skin cancer, the workload for pathologists has surged. The diagnosis of skin samples, especially for complex lesions such as malignant melanomas and melanocytic lesions, has shown higher diagnostic variability compared to other organ samples. Consequently, artificial intelligence (AI)-based diagnostic assistance programs are increasingly needed to support dermatopathologists in achieving more consistent diagnoses.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000 China.
Leveraging natural language processing to identify anxiety states from social media has been widely studied. However, existing research lacks deep user-level semantic modeling and effective anxiety feature extraction. Additionally, the absence of clinical domain knowledge in current models limits their interpretability and medical relevance.
View Article and Find Full Text PDFMed Biol Eng Comput
September 2025
Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300072, China.
Surgical instrument segmentation plays an important role in robotic autonomous surgical navigation systems as it can accurately locate surgical instruments and estimate their posture, which helps surgeons understand the position and orientation of the instruments. However, there are still some problems affecting segmentation accuracy, like insufficient attention to the edges and center of surgical instruments, insufficient usage of low-level feature details, etc. To address these issues, a lightweight network for surgical instrument segmentation in gastrointestinal (GI) endoscopy (GESur_Net) is proposed.
View Article and Find Full Text PDFNeural 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.
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