Visual working memory (VWM) retains representations of past visual information for future action. Yet to date, most studies have approached VWM as just serving perception beyond the immediate. Whether and how prospective actions shape information in VWM remains largely unknown, in part because typical experimental setups limit behavior to simple button presses. In two experiments (one preregistered), using a novel interactive VWM task, we show that the similarity of the actions that we intend to perform on memory items adaptively distorts their representation. Participants memorized the orientation of two bars, after which they were informed as to which manual actions they should reproduce these orientations with in a memory recall test. We observed that perceptually similar items were remembered as more distinct when paired with different action plans versus the same action plan. A control experiment showed that this action-induced effect reflects a true change in the visual representation rather than a motor bias. These findings demonstrate that VWM representations are flexibly adapted to guide specific overt actions and provide evidence that action plans can retrospectively warp sensory feature space in VWM.
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http://dx.doi.org/10.1073/pnas.2413433121 | DOI Listing |
Radiol Case Rep
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Emergency Radiology Department, Ibn Sina University Hospital, Faculty of Medicine and Pharmacy of Rabat, Mohamed V University, Rabat, Morocco.
Emphysematous pyelonephritis is a necrotizing infection of the renal parenchyma by gas-forming organisms, with a risk of the gas extending into peri-nephric or para-renal spaces and in advanced cases, the involvement may be extensive and bilateral. It is a common complication in patients with long-term diabetes, primarily caused by Gram-negative organisms or, in some cases, anaerobes. The diagnosis of emphysematous pyelonephritis is made by clinical features and confirmed by computed tomography.
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Department of Radiology, Zhuhai Clinical Medical College of Jinan University (Zhuhai People's Hospital, The Affiliated Hospital of Beijing Institute of Technology), Jinan University, Zhuhai, China.
Background: Epstein-Barr virus-positive (EBV) inflammatory follicular dendritic cell sarcoma (IFDCS) is a rare stroma-derived neoplasm of lymphoid tissues. It typically involves the spleen and liver, and is often associated with the presence of EBV. Because of its nonspecific clinical and imaging findings, making a correct diagnosis at the time of initial diagnosis is challenging.
View Article and Find Full Text PDFMagn Reson Med
January 2025
Department of Radiology, Stanford University, Stanford, California, USA.
Purpose: To provide a fast quantitative imaging approach for a 0.55T scanner, where signal-to-noise ratio is limited by the field strength and k-space sampling speed is limited by a lower specification gradient system.
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BioData Min
January 2025
Department of Computer Science, Hanyang University, Seoul, Republic of Korea.
Background: Understanding the molecular properties of chemical compounds is essential for identifying potential candidates or ensuring safety in drug discovery. However, exploring the vast chemical space is time-consuming and costly, necessitating the development of time-efficient and cost-effective computational methods. Recent advances in deep learning approaches have offered deeper insights into molecular structures.
View Article and Find Full Text PDFSci Rep
January 2025
College of computer science and technology, China University of Petroleum (East China), No.66 Changjiang West Road, Huangdao, Qingdao, 266580, Shandong, China.
Addressing the issues of inadequate information exchange among subsequences in the operational time series of water injection pumps, leading to low accuracy and high false alarm rates in anomaly detection, this paper proposes a multidimensional time series anomaly detection method for water injection pump operations, leveraging Long Short-Term Memory Autoencoder augmented with Attention Mechanism (LSTMA-AE) and mechanistic constraints. The LSTMA-AE framework encompasses three primary modules: a Time Feature Extraction Module (Encoder), an Attention Layer, and a Data Reconstruction Module (Decoder). The Encoder captures temporal dependencies and features within the input sequences, mapping the input data into a higher-dimensional space.
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