In this study, we jointly reported in an empirical and a theoretical way, for the first time, two main theories: Lavie's perceptual load theory and Gaspelin et al.'s attentional dwelling hypothesis. These theories explain in different ways the modulation of the perceptual load/task difficulty over attentional capture by irrelevant distractors and lead to the observation of the opposite results with similar manipulations. We hypothesized that these opposite results may critically depend on the distractor type used by the two experimental procedures (i.e., distractors inside vs. outside the attentional focus, which could be, respectively, considered as potentially relevant vs. completely irrelevant to the main task). Across a series of experiments, we compared both theories within the same paradigm by manipulating both the perceptual load/task difficulty and the distractor type. The results were strongly consistent, suggesting that the influence of task demands on attentional capture varies as a function of the distractor type: while the interference from (relevant) distractors presented inside the attentional focus was consistently higher for high vs. low load conditions, there was no modulation by (irrelevant) distractors presented outside the attentional focus. Moreover, we critically analyzed the theoretical conceptualization of interference using both theories, disentangling important outcomes for the dwelling hypothesis. Our results provide specific insights into new aspects of attentional capture, which can critically redefine these two predominant theories.
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http://dx.doi.org/10.3389/fpsyg.2021.758747 | DOI Listing |
Sensors (Basel)
January 2025
Department of Computer Science, King AbdulAziz University, Jeddah 21589, Saudi Arabia.
Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed.
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January 2025
Department of AI & Big Data, Honam University, Gwangju 62399, Republic of Korea.
This study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model. The framework introduces a novel attention mechanism to dynamically prioritize relevant features from multiple leaf images, overcoming the limitations of single-leaf-based diagnoses. Building on the Vision Transformer (ViT) architecture, the Multi-ViT model aggregates diverse feature representations by combining outputs from multiple ViTs, each capturing unique visual patterns.
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January 2025
School of Information Engineering, China University of Geosciences, Beijing 100083, China.
Extracting fragmented cropland is essential for effective cropland management and sustainable agricultural development. However, extracting fragmented cropland presents significant challenges due to its irregular and blurred boundaries, as well as the diversity in crop types and distribution. Deep learning methods are widely used for land cover classification.
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January 2025
Department of Information Technology, Quaid e Awam University, Nawabshah 67450, Pakistan.
Detection of anomalies in video surveillance plays a key role in ensuring the safety and security of public spaces. The number of surveillance cameras is growing, making it harder to monitor them manually. So, automated systems are needed.
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January 2025
Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China.
The Chang'e-6 (CE-6) landing area on the far side of the Moon is located in the southern part of the Apollo basin within the South Pole-Aitken (SPA) basin. The statistical analysis of impact craters in this region is crucial for ensuring a safe landing and supporting geological research. Aiming at existing impact crater identification problems such as complex background, low identification accuracy, and high computational costs, an efficient impact crater automatic detection model named YOLOv8-LCNET (YOLOv8-Lunar Crater Net) based on the YOLOv8 network is proposed.
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