Different people listening to the same story may converge upon a largely shared interpretation while still developing idiosyncratic experiences atop that shared foundation. What linguistic properties support this individualized experience of natural language? Here, we investigate how the "concrete-abstract" axis-the extent to which a word is grounded in sensory experience-relates to within- and across-subject variability in the neural representations of language. Leveraging a dataset of human participants of both sexes who each listened to four auditory stories while undergoing functional magnetic resonance imaging, we demonstrate that neural representations of "concreteness" are both reliable across stories and relatively unique to individuals, while neural representations of "abstractness" are variable both within individuals and across the population. Using natural language processing tools, we show that concrete words exhibit similar neural representations despite spanning larger distances within a high-dimensional semantic space, which potentially reflects an underlying representational signature of sensory experience-namely, imageability-shared by concrete words but absent from abstract words. Our findings situate the concrete-abstract axis as a core dimension that supports both shared and individualized representations of natural language.
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http://dx.doi.org/10.1523/JNEUROSCI.0288-24.2024 | DOI Listing |
Neural Netw
December 2024
School of Artificial Intelligence, Anhui University, Hefei, 230601, Anhui, China; Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Hefei, 230601, Anhui, China; Anhui Provincial Engineering Research Center for Unmanned Systems and Intelligent Technology, Hefei, 230601, Anhui, China; School of Automation, Southeast University, Nanjing, 211189, Jiangsu, China. Electronic address:
Deep reinforcement learning (DRL) exploits the powerful representational capabilities of deep neural networks (DNNs) and has achieved significant success. However, compared to DNNs, spiking neural networks (SNNs), which operate on binary signals, more closely resemble the biological characteristics of efficient learning observed in the brain. In SNNs, spiking neurons exhibit complex dynamic characteristics and learn based on principles of biological plasticity.
View Article and Find Full Text PDFNeural Netw
December 2024
School of Computer Science, Wuhan University, Luojiashan Road, Wuchang District., Wuhan, 430072, Hubei Province, China; Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, No. 8, Yangqiaohu Avenue, Zanglong Island Development Zone, Jiangxia District, Wuhan, 2007, Hubei Province, China. Electronic address:
The remarkable success of Graph Neural Networks underscores their formidable capacity to assimilate multimodal inputs, markedly enhancing performance across a broad spectrum of domains. In the context of molecular modeling, considerable efforts have been made to enrich molecular representations by integrating data from diverse aspects. Nevertheless, current methodologies frequently compartmentalize geometric and semantic components, resulting in a fragmented approach that impairs the holistic integration of molecular attributes.
View Article and Find Full Text PDFNeural Netw
December 2024
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China; Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100190, China.
Session-based recommendation aims to recommend the next item based on short-term interactions. Traditional session-based recommendation methods assume that all interacted items are closely related to the user's interests. However, noise (e.
View Article and Find Full Text PDFComput Biol Med
December 2024
Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, China. Electronic address:
Background: Studying influential nodes (I-nodes) in brain networks is of great significance in the field of brain imaging. Most existing studies consider brain connectivity hubs as I-nodes such as the regions of high centrality or rich-club organization. However, this approach relies heavily on prior knowledge from graph theory, which may overlook the intrinsic characteristics of the brain network, especially when its architecture is not fully understood.
View Article and Find Full Text PDFJ Cheminform
December 2024
Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743, Jena, Germany.
Naming chemical compounds systematically is a complex task governed by a set of rules established by the International Union of Pure and Applied Chemistry (IUPAC). These rules are universal and widely accepted by chemists worldwide, but their complexity makes it challenging for individuals to consistently apply them accurately. A translation method can be employed to address this challenge.
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