The goal of this study was to cast light on the existence of functional callosal channels for the interhemispheric transfer (IHT) of spatial and semantic information. To do so, we recorded event-related potentials in healthy humans while performing a primed odd-even discrimination task. Targets were visually presented numbers preceded by single-letter primes signaling the probable presentation of an odd or an even number. Primes and targets could appear either in the same or in different visual fields, thus requiring an IHT in the latter case. The P1 and N2 components were influenced by IHT of spatial information only, whereas the later N400 was influenced by IHT of both spatial and semantic information. This was not the case for the P3b, which was modulated by semantic validity only. These results provide novel evidence of the existence of a temporally separated interhemispheric exchange of spatial and semantic information.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1111/psyp.12025 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Weakly-supervised thyroid ultrasound segmentation: Leveraging multi-scale consistency, contextual features, and bounding box supervision for accurate target delineation.
Comput Biol Med
January 2025
Department of Artificial Intelligence, Faculty of Artificial Intelligence, Egyptian Russian University, 11829, Badr City, Egypt. Electronic address:
Weakly-supervised learning (WSL) methods have gained significant attention in medical image segmentation, but they often face challenges in accurately delineating boundaries due to overfitting to weak annotations such as bounding boxes. This issue is particularly pronounced in thyroid ultrasound images, where low contrast and noisy backgrounds hinder precise segmentation. In this paper, we propose a novel weakly-supervised segmentation framework that addresses these challenges.
View Article and Find Full Text PDFData-Efficient Bone Segmentation Using Feature Pyramid- Based SegFormer.
Sensors (Basel)
December 2024
Master's Program in Information and Computer Science, Doshisha University, Kyoto 610-0394, Japan.
The semantic segmentation of bone structures demands pixel-level classification accuracy to create reliable bone models for diagnosis. While Convolutional Neural Networks (CNNs) are commonly used for segmentation, they often struggle with complex shapes due to their focus on texture features and limited ability to incorporate positional information. As orthopedic surgery increasingly requires precise automatic diagnosis, we explored SegFormer, an enhanced Vision Transformer model that better handles spatial awareness in segmentation tasks.
View Article and Find Full Text PDFLW-YOLO11: A Lightweight Arbitrary-Oriented Ship Detection Method Based on Improved YOLO11.
Sensors (Basel)
December 2024
College of Power Engineering, Naval University of Engineering, Wuhan 430033, China.
Arbitrary-oriented ship detection has become challenging due to problems of high resolution, poor imaging clarity, and large size differences between targets in remote sensing images. Most of the existing ship detection methods are difficult to use simultaneously to meet the requirements of high accuracy and speed. Therefore, we designed a lightweight and efficient multi-scale feature dilated neck module in the YOLO11 network to achieve the high-precision detection of arbitrary-oriented ships in remote sensing images.
View Article and Find Full Text PDFDSC-SeNet: Unilateral Network with Feature Enhancement and Aggregation for Real-Time Segmentation of Carbon Trace in the Oil-Immersed Transformer.
Sensors (Basel)
December 2024
State Grid Tianjin Electric Power Research Institute, Tianjin 300180, China.
Large oil-immersed transformers have metal-enclosed shells, making it difficult to visually inspect the internal insulation condition. Visual inspection of internal defects is carried out using a self-developed micro-robot in this work. Carbon trace is the main visual characteristic of internal insulation defects.
View Article and Find Full Text PDFCortical encoding of spatial structure and semantic content in 3D natural scenes.
J Neurosci
January 2025
Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo FI-00076, Finland.
Our visual system enables us to effortlessly navigate and recognize real-world visual environments. Functional magnetic resonance imaging (fMRI) studies suggest a network of scene-responsive cortical visual areas, but much less is known about the temporal order in which different scene properties are analysed by the human visual system. In this study, we selected a set of 36 full-colour natural scenes that varied in spatial structure and semantic content that our male and female human participants viewed both in 2D and 3D while we recorded magnetoencephalography (MEG) data.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!