The cortical organization of the semantic network has been examined extensively in neuropsychological and neuroimaging studies; however, after decades of research, several issues remain controversial. A comprehensive and systematic investigation is needed to characterize the consistent patterns of category-specific activations as well as to examine factors that contribute to the varying findings across numerous neuroimaging studies. In this study, we reviewed 113 published papers that reported category-specific activations for living or nonliving concepts from the past two decades. Using the Activation Likelihood Estimate (ALE) method, we characterized the brain regions associated with living and nonliving concepts and revealed how the observed patterns were heavily influenced by methodological factors including imaging mode, task demand, and stimuli modality. Our findings provided the most comprehensive summary of category-specific activations for living and nonliving concepts and critically revealed that these activation patterns are highly contextually dependent. This work advanced our knowledge about the organization of the cortical semantic network and provided important insights into theoretical accounts and future research directions.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.neuropsychologia.2021.108002 | DOI Listing |
Res Sq
November 2024
Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California.
The hippocampus is crucial for forming new episodic memories. While the encoding of spatial and temporal information (where and when) in the hippocampus is well understood, the encoding of objects (what) remains less clear due to the high dimensions of object space. Rather than encoding each individual object separately, the hippocampus may instead encode categories of objects to reduce this dimensionality.
View Article and Find Full Text PDFJ Neurosci
December 2024
Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham B15 2TT, United Kingdom.
While humans typically saccade every ∼250 ms in natural settings, studies on vision tend to prevent or restrict eye movements. As it takes ∼50 ms to initiate and execute a saccade, this leaves only ∼200 ms to identify the fixated object and select the next saccade goal. How much detail can be derived about parafoveal objects in this short time interval, during which foveal processing and saccade planning both occur? Here, we had male and female human participants freely explore a set of natural images while we recorded magnetoencephalography and eye movements.
View Article and Find Full Text PDFPLoS Comput Biol
October 2024
Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America.
A central goal of neuroscience is to understand how function-relevant brain activations are generated. Here we test the hypothesis that function-relevant brain activations are generated primarily by distributed network flows. We focused on visual processing in human cortex, given the long-standing literature supporting the functional relevance of brain activations in visual cortex regions exhibiting visual category selectivity.
View Article and Find Full Text PDFJ Neurosci
December 2024
Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
Area TE is required for normal learning of visual categories based on perceptual similarity. To evaluate whether category learning changes neural activity in area TE, we trained two monkeys (both male) implanted with multielectrode arrays to categorize natural images of cats and dogs. Neural activity during a passive viewing task was compared pre- and post-training.
View Article and Find Full Text PDFFront Microbiol
October 2024
Shandong Normal University, Jinan, China.
Introduction: Fast, accurate, and automatic analysis of histopathological images using digital image processing and deep learning technology is a necessary task. Conventional histopathological image analysis algorithms require the manual design of features, while deep learning methods can achieve fast prediction and accurate analysis, but rely on the drive of a large amount of labeled data.
Methods: In this work, we introduce WSSS-CRAM a weakly-supervised semantic segmentation that can obtain detailed pixel-level labels from image-level annotated data.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!