While most previous studies of "semantic" priming confound associative and semantic relations, here we use a simple co-occurrence-based approach to examine "pure" semantic priming, while experimentally controlling for associative relations. We define associative relations by the co-occurrence of words in the sentences of a large text corpus. Contextual-semantic feature overlap, in contrast, is defined by the number of common associates that the prime shares with the target. Then we revisit the spreading activation theory and examine whether a long vs. short time available for semantic feature activation leads to early vs. late viewing time effects on the target words of a sentence reading experiment. We independently manipulate contextual-semantic feature overlap of two primes with one target word in sentences of the form pronoun, verb prime, article, adjective prime and target noun, e. g., "She rides the gray elephant." The results showed that long-SOA (verb-noun) overlap reduces early single and first fixation durations of the target noun, and short-SOA (adjective-noun) overlap reduces late go-past durations. This result pattern can be explained by the spreading activation theory: The semantic features of the prime words need some time to become sufficiently active before they can reliably affect target processing. Therefore, the verb can act on the target noun's early eye-movement measures presented three words later, while the adjective is presented immediately prior to the target-thus a difficult adjective-noun semantic integration leads to a late sentence re-examination of the preceding words.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9072456 | PMC |
http://dx.doi.org/10.1007/s10339-022-01084-3 | DOI Listing |
Sensors (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 PDFSensors (Basel)
January 2025
Mechnical and Vehicle Engineering, Hunan University, Changsha 411082, China.
Chip defect detection is a crucial aspect of the semiconductor production industry, given its significant impact on chip performance. This paper proposes a lightweight neural network with dual decoding paths for LED chip segmentation, named LDDP-Net. Within the LDDP-Net framework, the receptive field of the MobileNetv3 backbone is modified to mitigate information loss.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100811, China.
While deep learning techniques have been extensively employed in malware detection, there is a notable challenge in effectively embedding malware features. Current neural network methods primarily capture superficial characteristics, lacking in-depth semantic exploration of functions and failing to preserve structural information at the file level. Motivated by the aforementioned challenges, this paper introduces MalHAPGNN, a novel framework for malware detection that leverages a hierarchical attention pooling graph neural network based on enhanced call graphs.
View Article and Find Full Text PDFMaterials (Basel)
January 2025
Hubei Key Laboratory of Plasma Chemistry and Advanced Materials, School of Materials Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
The grain size of metal materials has a significant impact on their macroscopic properties. However, original metallographic images often suffer from issues such as substantial noise, missing grain boundaries, low contrast, and blurred edges. These challenges hinder the accurate extraction of complete grain boundaries, limiting the precision of grain size measurement and material performance prediction.
View Article and Find Full Text PDFAnimals (Basel)
January 2025
School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China.
Conflicts between humans and animals in agricultural and settlement areas have recently increased, resulting in significant resource loss and risks to human and animal lives. This growing issue presents a global challenge. This paper addresses the detection and identification of offending animals, particularly in obscured or blurry nighttime images.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!