Short-term memory load can impair attentional control, but prior work shows that the extent of the effect ranges from being very general to very specific. One factor for the mixed results may be reliance on point estimates of memory load effects on attention. Here we used auditory attention gradients as an analog measure to map-out the impact of short-term memory load over space. Verbal or spatial information was maintained during an auditory spatial attention task and compared to no-load. Stimuli were presented from five virtual locations in the frontal azimuth plane, and subjects focused on the midline. Reaction times progressively increased for lateral stimuli, indicating an attention gradient. Spatial load further slowed responses at lateral locations, particularly in the left hemispace, but had little effect at midline. Verbal memory load had no (Experiment 1), or a minimal (Experiment 2) influence on reaction times. Spatial and verbal load increased switch costs between memory encoding and attention tasks relative to the no load condition. The findings show that short-term memory influences the distribution of auditory attention over space; and that the specific pattern depends on the type of information in short-term memory.
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http://dx.doi.org/10.3389/fpsyg.2017.02028 | DOI Listing |
Sensors (Basel)
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
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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December 2024
Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.
Drowsy driving is a leading cause of commercial vehicle traffic crashes. The trend is to train fatigue detection models using deep neural networks on driver video data, but challenges remain in coarse and incomplete high-level feature extraction and network architecture optimization. This paper pioneers the use of the CLIP (Contrastive Language-Image Pre-training) model for fatigue detection.
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December 2024
Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, C/Camino de Vera, s/n, 46022 Valencia, Spain.
A Mixed Reality (MR) application using an optical see-through headset was developed to assess short-term spatial memory. A study with 29 participants was conducted. Data from this study were compared to two previous studies using mobile Augmented Reality (AR) and Virtual Reality (VR) with headsets.
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December 2024
Department of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, Italy.
Intrusion Detection Systems (IDSs) are a crucial component of modern corporate firewalls. The ability of IDS to identify malicious traffic is a powerful tool to prevent potential attacks and keep a corporate network secure. In this context, Machine Learning (ML)-based methods have proven to be very effective for attack identification.
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