In this paper, we investigate the process by which new experiences reactivate and potentially update old memories. Such memory reconsolidation appears dependent on the extent to which current experience deviates from what is predicted by the reactivated memory (i.e. prediction error). If prediction error is low, the reactivated memory is likely to be updated with new information. If it is high, however, a new, separate, memory is more likely to be formed. The temporal parietal junction TPJ has been shown across a broad range of content areas (attention, social cognition, decision making and episodic memory) to be sensitive to the degree to which current information violates the observer's expectations - in other words, prediction error. In the current paper, we investigate whether the level of TPJ activation during encoding predicts if the encoded information will be used to form a new memory or update a previous memory. We find that high TPJ activation predicts new memory formation. In a secondary analysis, we examine whether reactivation strength - which we assume leads to a strong memory-based prediction - mediates the likelihood that a given individual will use new information to form a new memory rather than update a previous memory. Individuals who strongly reactivate previous memories are less likely to update them than individuals who weakly reactivate them. We interpret this outcome as indicating that strong predictions lead to high prediction error, which favors new memory formation rather than updating of a previous memory.
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http://dx.doi.org/10.1016/j.nlm.2017.03.003 | DOI Listing |
Tomography
November 2024
Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong 999077.
Assessment of skeletal maturity is a common clinical practice to investigate adolescent growth and endocrine disorders. The distal radius and ulna (DRU) maturity classification is a practical and easy-to-use scheme that was designed for adolescent idiopathic scoliosis clinical management and presents high sensitivity in predicting the growth peak and cessation among adolescents. However, time-consuming and error-prone manual assessment limits DRU in clinical application.
View Article and Find Full Text PDFNanomaterials (Basel)
December 2024
College of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China.
The corrosion resistance of nickel-titanium nitride (Ni/TiN) composites is significantly influenced by the operation parameters during the jet pulse electrodeposition (JPE) process. The effect of current density, jet rate, TiN concentration, and duty cycle impact on the anti-corrosion property of Ni/TiN composites were investigated and optimized using the response surface method (RSM). After the optimization of the operation parameters, the corrosion current of Ni/TiN composites decreased from 9.
View Article and Find Full Text PDFJ Imaging
December 2024
Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany.
In recent years, synthetic Computed Tomography (CT) images generated from Magnetic Resonance (MR) or Cone Beam Computed Tomography (CBCT) acquisitions have been shown to be comparable to real CT images in terms of dose computation for radiotherapy simulation. However, until now, there has been no independent strategy to assess the quality of each synthetic image in the absence of ground truth. In this work, we propose a Deep Learning (DL)-based framework to predict the accuracy of synthetic CT in terms of Mean Absolute Error (MAE) without the need for a ground truth (GT).
View Article and Find Full Text PDFJ Intell
December 2024
School of Computer and Artificial Intelligence, Huaihua University, Huaihua 418000, China.
Universities and schools rely heavily on the ability to forecast student performance, as it enables them to develop efficient strategies for enhancing academic results and averting student attrition. The automation of processes and the management of large datasets generated by technology-enhanced learning tools can facilitate the analysis and processing of these data, which provides crucial insights into the knowledge of students and their engagement with academic endeavors. The method under consideration aims to forecast the academic achievement of students through an ensemble of deep neural networks.
View Article and Find Full Text PDFInfect Dis Rep
December 2024
Department of Laboratory Medicine and Pathology, Walter Sisulu University, Private Bag X5117, Mthatha 5099, South Africa.
Background: The global push to eliminate tuberculosis (TB) as a public health threat is increasingly urgent, particularly in high-burden areas like the Oliver Reginald Tambo District Municipality, South Africa. Drug-resistant TB (DR-TB) poses a significant challenge to TB control efforts and is a leading cause of TB-related deaths. This study aimed to assess DR-TB transmission patterns and predict future cases using geospatial and predictive modeling techniques.
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