Working memory, the ability to actively maintain and manipulate information across time, is key to intelligent behavior. Because of the limited capacity of working memory, relevant information needs to be protected against distracting representations. Whether birds can resist distractors and safeguard memorized relevant information is unclear. We trained carrion crows in a delayed match-to-sample task to memorize an image while resisting other, interfering stimuli. We found that the repetition of the sample stimulus during the memory delay improved performance accuracy and accelerated reaction time relative to a reference condition with a neutral interfering stimulus. In contrast, the presentation of the image that constituted the subsequent non-match test stimulus mildly weakened performance. However, the crows' robust performance in this most demanding distractor condition indicates that sample information was actively protected from being overwritten by the distractor. These data show that crows can cognitively control and safeguard behaviorally relevant working memory contents.
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http://dx.doi.org/10.1242/jeb.245453 | DOI Listing |
Eur Heart J Digit Health
January 2025
Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, No. 88 West Taishan Road, Zhuzhou 412007, Hunan, China.
Aims: The electrocardiogram (ECG) is the primary method for diagnosing atrial fibrillation (AF), but interpreting ECGs can be time-consuming and labour-intensive, which deserves more exploration.
Methods And Results: We collected ECG data from 6590 patients as YY2023, classified as Normal, AF, and Other. Convolutional Neural Network (CNN), bidirectional Long Short-Term Memory (BiLSTM), and Attention construct the AF recognition model CNN BiLSTM Attention-Atrial Fibrillation (CLA-AF).
Acta Endocrinol (Buchar)
January 2025
Universidad Autónoma de Aguascalientes, Centro de Ciencias Básicas, Department of Physiology and Pharmacology.
Context: Studies indicate a decrease in spatial memory across species as they age. Moreover, consistent administration of Gonadotropin-releasing hormone (GnRH) improves learning abilities in older rats that have undergone gonadectomy.
Objective: The aim of this study was to investigate the effects of the GnRH agonist, leuprolide acetate (LA) on spatial memory in aged intact male rats and the expression of proteins associated with hippocampal plasticity.
Digit Health
January 2025
Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany.
Background: Advancing evidence-based, tailored interventions for substance use disorders (SUDs) requires understanding temporal directionality while upholding ecological validity. Previous studies identified loneliness and craving as pivotal factors associated with alcohol consumption, yet the precise directionality of these relationships remains ambiguous.
Objective: This study aims to establish a smartphone-based real-life intervention platform that integrates momentary assessment and intervention into everyday life.
Front Comput Neurosci
January 2025
Interdisciplinary Research Center for Finance and Digital Economy, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.
Marketing plays a vital role in the success of a business, driving customer engagement, brand recognition, and revenue growth. Neuromarketing adds depth to this by employing insights into consumer behavior through brain activity and emotional responses to create more effective marketing strategies. Electroencephalogram (EEG) has typically been utilized by researchers for neuromarketing, whereas Eye Tracking (ET) has remained unexplored.
View Article and Find Full Text PDFAnn N Y Acad Sci
January 2025
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Deep learning has revolutionized electroencephalograph (EEG) decoding, with convolutional neural networks (CNNs) being a predominant tool. However, CNNs struggle with long-term dependencies in sequential EEG data. Models like long short-term memory and transformers improve performance but still face challenges of computational efficiency and long sequences.
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