The timing of semantic processing during object recognition in the brain is a topic of ongoing discussion. One way of addressing this question is by applying multivariate pattern analysis to human electrophysiological responses to object images of different semantic categories. However, although multivariate pattern analysis can reveal whether neuronal activity patterns are distinct for different stimulus categories, concerns remain on whether low-level visual features also contribute to the classification results. To circumvent this issue, we applied a cross-decoding approach to magnetoencephalography data from stimuli from two different modalities: images and their corresponding written words. We employed items from three categories and presented them in a randomized order. We show that if the classifier is trained on words, pictures are classified between 150 and 430 msec after stimulus onset, and when training on pictures, words are classified between 225 and 430 msec. The topographical map, identified using a searchlight approach for cross-modal activation in both directions, showed left lateralization, confirming the involvement of linguistic representations. These results point to semantic activation of pictorial stimuli occurring at ∼150 msec, whereas for words, the semantic activation occurs at ∼230 msec.
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Sci Rep
January 2025
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China.
Aspect Category Sentiment Analysis (ACSA) is a fine-grained sentiment analysis task aimed at predicting the sentiment polarity associated with aspect categories within a sentence.Most existing ACSA methods are based on a given aspect category to locate sentiment words related to it. When irrelevant sentiment words have semantic meaning for the given aspect category, it may cause the problem that sentiment words cannot be matched with aspect categories.
View Article and Find Full Text PDFCortex
December 2024
Humboldt-Universität zu Berlin, Berlin School of Mind and Brain, Berlin, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, Leipzig, Germany; University Hospital and Faculty of Medicine Leipzig, Clinic for Cognitive Neurology, Leipzig, Germany.
Retrieving words quickly and correctly is an important language competence. Semantic contexts, such as prior naming of categorically related objects, can induce conceptual priming but also lexical-semantic interference, the latter likely due to enhanced competition during lexical selection. In the continuous naming (CN) paradigm, such semantic interference is evident in a linear increase in naming latency with each additional member of a category out of a seemingly random sequence of pictures being named (cumulative semantic interference/CSI effect).
View Article and Find Full Text PDFSensors (Basel)
January 2025
The 54th Research Institute, China Electronics Technology Group Corporation, College of Signal and Information Processing, Shijiazhuang 050081, China.
The multi-sensor fusion, such as LiDAR and camera-based 3D object detection, is a key technology in autonomous driving and robotics. However, traditional 3D detection models are limited to recognizing predefined categories and struggle with unknown or novel objects. Given the complexity of real-world environments, research into open-vocabulary 3D object detection is essential.
View Article and Find Full Text PDFNeural Netw
January 2025
National Key Laboratory of Space Integrated Information System, Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
Vision-language models are pre-trained by aligning image-text pairs in a common space to deal with open-set visual concepts. Recent works adopt fixed or learnable prompts, i.e.
View Article and Find Full Text PDFBMC Emerg Med
January 2025
Department of Health in Disasters and Emergencies, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
Background: Volunteers providing nursing services are among the first individuals to arrive at the scene after an incident; therefore, they must use their skills and capabilities to provide necessary care for the injured to prevent problems from worsening and complications from arising. Consequently, having structured empowerment courses for volunteers before disasters seems essential. This research aimed to determine the dimensions and components of empowering volunteer nursing service providers in disasters.
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