We evaluated the process of inferential revision during text comprehension in adults. Participants with high or low working memory read short texts, in which the introduction supported two plausible concepts (e.g., 'guitar/violin'), although one was more probable ('guitar'). There were three possible continuations: a neutral sentence, which did not refer back to either concept; a no-revise sentence, which referred to a general property consistent with either concept (e.g., '…beautiful curved body'); and a revise sentence, which referred to a property that was consistent with only the less likely concept (e.g., '…matching bow'). Readers took longer to read the sentence in the revise condition, indicating that they were able to evaluate their comprehension and detect a mismatch. In a final sentence, a target noun referred to the alternative concept supported in the revise condition (e.g., 'violin'). ERPs indicated that both working memory groups were able to evaluate their comprehension of the text (P3a), but only high working memory readers were able to revise their initial incorrect interpretation (P3b) and integrate the new information (N400) when reading the revise sentence. Low working memory readers had difficulties inhibiting the no-longer-relevant interpretation and thus failed to revise their situation model, and they experienced problems integrating semantically related information into an accurate memory representation.
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http://dx.doi.org/10.3758/s13421-015-0528-0 | 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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