The question of apparent discrepancies in short-term memory capacity for sign language and speech has long presented difficulties for the models of verbal working memory. While short-term memory (STM) capacity for spoken language spans up to 7 ± 2 items, the verbal working memory capacity for sign languages appears to be lower at 5 ± 2. The assumption that both auditory and visual communication (sign language) rely on the same memory buffers led to the claims of impairment of STM buffers in sign language users. Yet, no common model deals with both the sensory and linguistic nature of spoken and sign languages. The authors present a generalized neural model (GNM) of short-term memory use across modalities, which accounts for experimental results in both sign and spoken languages. GNM postulates that during hierarchically organized processing phases in language comprehension, spoken language users rely on neural resources for spatial representation in sequential rehearsal strategy, i.e., the phonological loop. The spatial nature of sign language precludes signers from utilizing a similar 'overflow' strategy, which speakers rely on to extend their STM capacity. This model offers a parsimonious neuroarchitectural explanation for the conflict between spatial and linguistic processing in spoken language, as well as the differences observed in STM capacity for sign and speech.
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http://dx.doi.org/10.1016/j.cortex.2018.05.020 | DOI Listing |
J Chem Inf Model
January 2025
Division of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.
Skin corrosion assessment is an essential toxicity end point that addresses safety concerns for topical dosage forms and cosmetic products. Previously, skin corrosion assessments required animal testing; however, differences in skin architecture and ethical concerns regarding animal models have fostered the advancement of alternative methods such as and models. This study aimed to develop deep learning (DL) models based on recurrent neural networks (RNNs) for classifying skin corrosion of chemical compounds based on chemical language notation, molecular substructure, physicochemical properties, and a combination of these three properties called conjoint fingerprints.
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January 2025
Neuro-robotics Laboratory, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan.
Reliable proprioception and feedback from soft sensors are crucial for enabling soft robots to function intelligently in real-world environments. Nevertheless, soft sensors are fragile and are susceptible to various damage sources in such environments. Some researchers have utilized redundant configuration, where healthy sensors compensate instantaneously for lost ones to maintain proprioception accuracy.
View Article and Find Full Text PDFFront Neurol
January 2025
Institution of Traditional Chinese Medicine Innovation Research, Shandong University of Traditional Chinese Medicine, Jinan, China.
Background: In nature, animals must learn to recognize danger signals and respond immediately to threats to improve their environmental adaptation. However, excessive fear responses can lead to diseases such as post-traumatic stress disorder, wherein traumatic events result in persistent traumatic memories. Therefore, erasing pathological fear memories is a crucial topic in neuroscience for understanding the nature of memories and treating clinically relevant diseases.
View Article and Find Full Text PDFFront Cardiovasc Med
January 2025
Department of Cardiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
Introduction: The risk of mortality associated with cardiac arrhythmias is considerable, and their diagnosis presents significant challenges, often resulting in misdiagnosis. This situation highlights the necessity for an automated, efficient, and real-time detection method aimed at enhancing diagnostic accuracy and improving patient outcomes.
Methods: The present study is centered on the development of a portable deep learning model for the detection of arrhythmias via electrocardiogram (ECG) signals, referred to as CardioAttentionNet (CANet).
Heliyon
January 2025
Department of Horticulture, Kongju National University, Yesan, 32439, Republic of Korea.
Machine learning has been used in various areas, but there are few studies on price prediction for agricultural products. Here, a machine learning technique for the price prediction of tomato and apple fruits was attempted based on environment and price data for 12 years. The goal of this study is to discover 1) how much can we accurately predict the product prices with the environmental factors and 2) how much each environmental factor affects to the product prices.
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