Modifications of the Wisconsin Card Sorting Test were established. In these new task variants, participants were asked to exert sequential control over attentional sets or over intentional sets (task domain factor). Attentional set shifting requires changing the priorities by which sensory stimuli are selected, whereas intentional set shifting requires changing the priorities by which motor responses are selected. Auditory stimuli that signaled to maintain or shift set were presented immediately before (precuing) or after (postcuing) the selection of cards (cue timing factor). Twenty-four healthy young individuals participated. Performance data (response times, error percentages) indicated that intentional tasks were easier to perform than attentional tasks. The electroencephalogram was recorded during task performance, and the N1, medial frontal negativity (MFN), P3a, and sustained potential (SP) components of the cue event-related brain potentials (ERPs) were analyzed. Irrespective of the task domain, shift precues led to increased N1 amplitudes compared to shift postcues. When intentional sets had to be shifted, the MFNs in the postcuing condition were more pronounced than in the precuing condition. On the other hand, shifts of attentional sets resulted in a more prominent P3a in response to postcues compared to precues. Irrespective of the task domain, the shift effect that was evident in SPs was more pronounced in precue ERPs compared to postcue ERPs. We conclude that ERPs provide valid measures to empirically constrain theories about the neural mechanisms of cognitive control. The domain hypothesis of the fractionation of the neural mechanisms of cognitive control is introduced.
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http://dx.doi.org/10.1162/jocn.2006.18.6.949 | DOI Listing |
Sensors (Basel)
December 2024
NUS-ISS, National University of Singapore, Singapore 119615, Singapore.
The attention mechanism is essential to (CNN) vision backbones used for sensing and imaging systems. Conventional attention modules are designed heuristically, relying heavily on empirical tuning. To tackle the challenge of designing attention mechanisms, this paper proposes a novel probabilistic attention mechanism.
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December 2024
Department of Information and Electronic Engineering, International Hellenic University, 57001 Thessaloniki, Greece.
Recent advances in emotion recognition through Artificial Intelligence (AI) have demonstrated potential applications in various fields (e.g., healthcare, advertising, and driving technology), with electroencephalogram (EEG)-based approaches demonstrating superior accuracy compared to facial or vocal methods due to their resistance to intentional manipulation.
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December 2024
School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Deep unfolding networks (DUNs) have attracted growing attention in compressed sensing (CS) due to their good interpretability and high performance. However, many DUNs often improve the reconstruction effect at the price of a large number of parameters and have the problem of feature information loss during iteration. This paper proposes a novel adaptive memory-augmented unfolding network for compressed sensing (AMAUN-CS).
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December 2024
School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China.
Data imbalances present a serious problem for intelligent fault diagnosis. They can lead to reduced diagnostic precision, which can jeopardize equipment reliability and safety. Based on that, this paper proposes a novel fault diagnosis method combining the denoising diffusion implicit model (DDIM) with a new convolutional neural network framework.
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December 2024
ENSTA Bretagne, Lab-STICC, UMR CNRS 6285, 29806 Brest, France.
Satellite SAR (synthetic aperture radar) imagery offers global coverage and all-weather recording capabilities, making it valuable for applications like remote sensing and maritime surveillance. However, its use in machine learning-based automatic target classification faces challenges, including the limited availability of SAR target training samples and the inherent constraints of SAR images, which provide less detailed features compared to natural images. These issues hinder the effective training of convolutional neural networks (CNNs) and complicate the transfer learning process due to the distinct imaging mechanisms of SAR and natural images.
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