The capacity for imagery, enabling us to visualise absent items and events, is a ubiquitous feature of our experience. This paper describes the case of a patient, MX, who abruptly lost the ability to generate visual images. He rated himself as experiencing almost no imagery on standard questionnaires, yet performed normally on standard tests of perception, visual imagery and visual memory. These unexpected findings were explored using functional MRI scanning (fMRI). Activation patterns while viewing famous faces were not significantly different between MX and controls, including expected activity in the fusiform gyrus. However, during attempted imagery, activation in MX's brain was significantly reduced in a network of posterior regions while activity in frontal regions was increased compared to controls. These findings are interpreted as suggesting that MX adopted a different cognitive strategy from controls when performing the imagery task. Evidence from experimental tasks thought to rely on mental imagery, such as the Brooks' matrices and mental rotation, support this interpretation. Taken together, these results indicate that successful performance in visual imagery and visual memory tasks can be dissociated from the phenomenal experience of visual imagery.
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http://dx.doi.org/10.1016/j.neuropsychologia.2009.08.024 | DOI Listing |
Sensors (Basel)
January 2025
Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy.
The increasing demand for hazelnut kernels is favoring an upsurge in hazelnut cultivation worldwide, but ongoing climate change threatens this crop, affecting yield decreases and subject to uncontrolled pathogen and parasite attacks. Technical advances in precision agriculture are expected to support farmers to more efficiently control the physio-pathological status of crops. Here, we report a straightforward approach to monitoring hazelnut trees in an open field, using aerial multispectral pictures taken by drones.
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January 2025
College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China.
To address the problems that exist in the target detection of vehicle-mounted visual sensors in foggy environments, a vehicle target detection method based on an improved YOLOX network is proposed. Firstly, to address the issue of vehicle target feature loss in foggy traffic scene images, specific characteristics of fog-affected imagery are integrated into the network training process. This not only augments the training data but also improves the robustness of the network in foggy environments.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 Valencia, Spain.
In this paper, a bibliometric review is conducted on brain-computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An exploratory and descriptive approach is used in the analysis. Computational tools such as the biblioshiny application for R-Bibliometrix and VOSViewer are employed to generate data on years, sources, authors, affiliation, country, documents, co-author, co-citation, and co-occurrence.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China.
Cross-view geo-localization (CVGL) aims to determine the capture location of street-view images by matching them with corresponding 2D maps, such as satellite imagery. While recent bird's eye view (BEV)-based methods have advanced this task by addressing viewpoint and appearance differences, the existing approaches typically rely solely on either OpenStreetMap (OSM) data or satellite imagery, limiting localization robustness due to single-modality constraints. This paper presents a novel CVGL method that fuses OSM data with satellite imagery, leveraging their complementary strengths to enhance localization robustness.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China. Electronic address:
Smoke is a critical indicator of forest fires, often detectable before flames ignite. Accurate smoke identification in remote sensing images is vital for effective forest fire monitoring within Internet of Things (IoT) systems. However, existing detection methods frequently falter in complex real-world scenarios, where variable smoke shapes and sizes, intricate backgrounds, and smoke-like phenomena (e.
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