Working memory and attention are intimately connected. However, understanding the relationship between the two is challenging. Currently, there is an important controversy about whether objects in working memory are maintained automatically or require resources that are also deployed for visual or auditory attention. Here we investigated the effects of loading attention resources on precision of visual working memory, specifically on correct maintenance of feature-bound objects, using a dual-task paradigm. Participants were presented with a memory array and were asked to remember either direction of motion of random dot kinematograms of different colour, or orientation of coloured bars. During the maintenance period, they performed a secondary visual or auditory task, with varying levels of load. Following a retention period, they adjusted a coloured probe to match either the motion direction or orientation of stimuli with the same colour in the memory array. This allowed us to examine the effects of an attention-demanding task performed during maintenance on precision of recall on the concurrent working memory task. Systematic increase in attention load during maintenance resulted in a significant decrease in overall working memory performance. Changes in overall performance were specifically accompanied by an increase in feature misbinding errors: erroneous reporting of nontarget motion or orientation. Thus in trials where attention resources were taxed, participants were more likely to respond with nontarget values rather than simply making random responses. Our findings suggest that resources used during attention-demanding visual or auditory tasks also contribute to maintaining feature-bound representations in visual working memory-but not necessarily other aspects of working memory.
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http://dx.doi.org/10.1080/17470218.2013.852232 | DOI Listing |
In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance.
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Sports, Exercise and Brain Sciences Laboratory, Sports Coaching College, Beijing Sport University, 100084 Beijing, China.
Background: Sports fatigue in soccer athletes has been shown to decrease neural activity, impairing cognitive function and negatively affecting motor performance. Transcranial direct current stimulation (tDCS) can alter cortical excitability, augment synaptic plasticity, and enhance cognitive function. However, its potential to ameliorate cognitive impairment during sports fatigue remains largely unexplored.
View Article and Find Full Text PDFNutrients
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Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029 Madrid, Spain.
Decompensated cirrhosis is characterized by systemic inflammation and innate and adaptive immune dysfunction. Hepatic encephalopathy (HE) is a prevalent and debilitating condition characterized by cognitive disturbances in which ammonia and inflammation play a synergistic pathogenic role. Extraskeletal functions of vitamin D include immunomodulation, and its deficiency has been implicated in immune dysfunction and different forms of cognitive impairment.
View Article and Find Full Text PDFSensors (Basel)
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Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia.
The Internet of Things (IoT) has emerged as a crucial element in everyday life. The IoT environment is currently facing significant security concerns due to the numerous problems related to its architecture and supporting technology. In order to guarantee the complete security of the IoT, it is important to deal with these challenges.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China.
Real-time and accurate traffic forecasting aids in traffic planning and design and helps to alleviate congestion. Addressing the negative impacts of partial data loss in traffic forecasting, and the challenge of simultaneously capturing short-term fluctuations and long-term trends, this paper presents a traffic forecasting model, D-MGDCN-CLSTM, based on Multi-Graph Gated Dilated Convolution and Conv-LSTM. The model uses the DTWN algorithm to fill in missing data.
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