Functional hemispheric asymmetries are considered a key factor in intra- and interindividual variability of global precedence effects. However, research in this area is permeated by a considerable number of inconsistent findings which may stem from significant methodological limitations. In pursuit of a more detailed model of global-local processing by combining both high temporal and spatial resolution, we employed Multivariate Pattern Analysis (MVPA) on Magnetoencephalography (MEG) recordings from 63 participants performing a divided visual field, divided attention Navon paradigm. The resulting decoding accuracies between various hierarchical letter forms and target levels were used to pinpoint potentially involved spatial networks and temporal processing sequences. Linear Discriminant Analysis (LDA) revealed temporal precedence of global over local letter form decoding accuracy peaks. Furthermore, searchlight analysis provided a nuanced spatial mapping that not only validated previously established core regions (lingual gyrus for local processing; inferior occipital gyrus for global processing) but also identified potential regions implicated in global-local integration. Yet, we observed substantial variation in lateralization patterns across our study sample, challenging the conventional assumption of right-hemispheric dominance for global and left-hemispheric dominance for local processing in the context of MVPA. Overall, our findings validate and broaden the scope of prior research by providing, for the first time, accurate temporal and spatial data on global-local processing from a single measurement. Moreover, we introduce interindividual variability in lateralization patterns as a potential factor contributing to past inconsistencies.
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http://dx.doi.org/10.1111/psyp.70032 | DOI Listing |
Psychophysiology
March 2025
Centre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria.
Functional hemispheric asymmetries are considered a key factor in intra- and interindividual variability of global precedence effects. However, research in this area is permeated by a considerable number of inconsistent findings which may stem from significant methodological limitations. In pursuit of a more detailed model of global-local processing by combining both high temporal and spatial resolution, we employed Multivariate Pattern Analysis (MVPA) on Magnetoencephalography (MEG) recordings from 63 participants performing a divided visual field, divided attention Navon paradigm.
View Article and Find Full Text PDFIEEE Trans Cybern
March 2025
Meta-reinforcement learning (meta-RL) algorithms extract task information from experienced context in order to reason about new tasks, and facilitate rapid adaptation. The quality of these contextual representations (or embeddings) is therefore crucial for a meta-RL agent to make effective decisions in unknown environments. Current methods predominantly assume the existence of a single underlying task, but using a single contextual embedding may not be expressive enough to fully capture the broader distribution of task variations that an agent might encounter.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
January 2025
Near-infrared (NIR) technology has gained wide acceptance in practical processes and is now the measurement of choice in many sectors. However, with increasing spectral dimensionality, it is challenging to establish a prediction model with satisfactory stability and generalization. Stochastic configuration networks (SCNs) based on supervisory learning mechanism have demonstrated significant advantages in developing nonlinear learners.
View Article and Find Full Text PDFThe joint use of multiple modalities for medical image processing has been widely studied in recent years. The fusion of information from different modalities has demonstrated the performance improvement for a lot of medical tasks. For nephropathy diagnosis, immunofluorescence (IF) is one of the most widely-used multi-modality medical images due to its ease of acquisition and the effectiveness for certain nephropathy.
View Article and Find Full Text PDFJ Imaging Inform Med
February 2025
Department of Radiology, The University of Alabama at Birmingham, 1720 2Nd Avenue South, VH G082, Birmingham, AL, 35294, USA.
This study presents a novel pseudo-3D Global-Local Channel Spatial Attention (GLCSA) mechanism designed to enhance prostate zonal segmentation in high-resolution T2-weighted MRI images. GLCSA captures complex, multi-dimensional features while maintaining computational efficiency by integrating global and local attention in channel and spatial domains, complemented by a slice interaction module simulating 3D processing. Applied across various U-Net architectures, GLCSA was evaluated on two datasets: a proprietary set of 44 patients and the public ProstateX dataset of 204 patients.
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