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Specific pairs of semantic entities have symmetric relationships, such as word pairs with opposite meanings (e.g., "intelligent" and "stupid"; "human" and "mechanical"). Such semantic symmetry is a key feature of semantic information. However, the representation of symmetric semantic information in the brain is not yet understood. For example, it remains unclear whether symmetric pairs of semantic information are represented in overlapping or distinct brain regions. We addressed this question in a data-driven manner by using the voxelwise modeling of movie-evoked cortical response measured by functional magnetic resonance imaging. In this modeling, response in each voxel was predicted from semantic labels designated for each movie scene. The semantic labels consisted of 30 different concepts, including 15 pairs of semantically symmetric concepts. Each concept was manually evaluated using a 5-point scale. By localizing the semantic representation associated with each concept based on the voxelwise accuracy of brain-response predictions, we found that semantic representations of symmetric concept pairs are broadly distributed but with little overlap in the cortex. Additionally, the weight of voxelwise models revealed highly complex, various patterns of cortical representations for each concept pair. These results suggest that symmetric semantic information has rather asymmetric and heterogeneous representations in the human brain.
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http://dx.doi.org/10.1016/j.ynirp.2025.100243 | DOI Listing |
Cereb Cortex
August 2025
Department of Psychology, University of Milano-Bicocca, Milan, Italy.
Semantic composition allows us to construct complex meanings (e.g., "dog house", "house dog") from simpler constituents ("dog", "house").
View Article and Find Full Text PDFFront Artif Intell
August 2025
Aviation Industry Development Research Center of China, Beijing, China.
Autonomous systems operating in high-dimensional environments increasingly rely on prioritization heuristics to allocate attention and assess risk, yet these mechanisms can introduce cognitive biases such as salience, spatial framing, and temporal familiarity that influence decision-making without altering the input or accessing internal states. This study presents Priority Inversion via Operational Reasoning (PRIOR), a black-box, non-perturbative diagnostic framework that employs structurally biased but semantically neutral scenario cues to probe inference-level vulnerabilities without modifying pixel-level, statistical, or surface semantic properties. Given the limited accessibility of embodied vision-based systems, we evaluate PRIOR using large language models (LLMs) as abstract reasoning proxies to simulate cognitive prioritization in constrained textual surveillance scenarios inspired by Unmanned Aerial Vehicle (UAV) operations.
View Article and Find Full Text PDFSensors (Basel)
July 2025
Automotive and Transportation School, Tianjin University of Technology and Education, Tianjin 300222, China.
Aiming to enable intelligent vehicles to achieve autonomous charging under low-battery conditions, this paper presents a navigation system for autonomous charging that integrates an improved bidirectional A* algorithm for path planning and an optimized YOLOv11n model for visual recognition. The system utilizes the improved bidirectional A* algorithm to generate collision-free paths from the starting point to the charging area, dynamically adjusting the heuristic function by combining node-target distance and search iterations to optimize bidirectional search weights, pruning expanded nodes via a greedy strategy and smoothing paths into cubic Bézier curves for practical vehicle motion. For precise localization of charging areas and piles, the YOLOv11n model is enhanced with a CAFMFusion mechanism to bridge semantic gaps between shallow and deep features, enabling effective local-global feature fusion and improving detection accuracy.
View Article and Find Full Text PDFInterdiscip Sci
July 2025
School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China.
The advancement of deep learning has driven extensive research validating the effectiveness of U-Net-style symmetric encoder-decoder architectures based on Transformers for medical image segmentation. However, the inherent design requiring attention mechanisms to compute token affinities across all spatial locations leads to prohibitive computational complexity and substantial memory demands. Recent efforts have attempted to address these limitations through sparse attention mechanisms.
View Article and Find Full Text PDFFront Hum Neurosci
June 2025
Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, HSE University, Russian Federation, Moscow, Russia.
Background: Motor planning critically supports efficient hand grasping and object manipulation, involving the precise integration of sensory cues and anticipatory motor commands. Current methods often inadequately separate motor planning from movement execution, thus limiting our understanding of anticipatory motor control mechanisms.
Objective: This study aimed to establish and validate a structured methodological approach to investigate motor planning and execution during grasping tasks, using advanced motion tracking technology and standardized 3D-printed geometric objects.