Naming individual objects is accompanied with semantic recognition. Previous studies examined brain-networks responsible for these operations individually. However, it remains unclear how these brain-networks are related. To address this problem, we examined the brain-networks during a novel object-naming task, requiring participants to name animals in photographs at a specific-level (e.g., "pigeon"). When the participants could not remember specific names, they answered basic names (e.g., "bird"). After fMRI scanning during the object-naming task, the participants rated familiarity of the animals based on their sense of knowing. Since participants tend to remember specific names for familiar objects compared with unfamiliar objects, a typical issue in an object-naming task is an internal covariance between the naming and familiarity levels. We removed this confounding factor by adjusting the familiarity/naming level of stimuli, and demonstrated distinct brain regions related to the two operations. Among them, the left inferior frontal gyrus triangularis (IFGtri) contained object-naming and semantic-recognition related areas in its anterior-ventral and posterior-dorsal parts, respectively. Psychophysiological interaction analyses suggested that both parts show connectivity with the brain regions related to object-naming. By examining the connectivity under control tasks requiring nonlexical semantic retrieval (e.g., animal's body color), we found that both IFGtri parts altered their targeting brain areas according to the required memory attributes, while only the posterior-dorsal part connected the brain regions related to semantic recognition. Together, the semantic recognition may be processed by distinct brain network from those for voluntary semantic retrievals including object-naming although all these networks are mediated by the posterior-dorsal IFGtri.
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http://dx.doi.org/10.1002/hbm.24953 | DOI Listing |
Sensors (Basel)
December 2024
Automation Department, North China Electric Power University, Baoding 071003, China.
Aiming at the severe occlusion problem and the tiny-scale object problem in the multi-fitting detection task, the Scene Knowledge Integrating Network (SKIN), including the scene filter module (SFM) and scene structure information module (SSIM) is proposed. Firstly, the particularity of the scene in the multi-fitting detection task is analyzed. Hence, the aggregation of the fittings is defined as the scene according to the professional knowledge of the power field and the habit of the operators in identifying the fittings.
View Article and Find Full Text PDFBehav Sci (Basel)
December 2024
College of Chinese Language and Literature, Qufu Normal University, No. 57, Jingxuan Road, Qufu 273165, China.
Two experiments were conducted to examine native and non-native speakers' recognition of Chinese two-character words (2C-words) in the context of audio sentence comprehension. The recording was played of a sentence, in which a collocation composed of a number word, a sortal classifier, and a noun (NCN) was embedded. When the participants were about to hear the noun of the NCN (Noun), the playing stopped, and a target was visually presented, which was the Noun, the character-transposed word of the Noun (NounT), or a control word (NounC), or was a homophone nonword for Noun, NounT, or NounC.
View Article and Find Full Text PDFSci Rep
January 2025
Ministry of Higher Education & Scientific Research, Industrial Technical Institute in Mataria, Cairo, 11718, Egypt.
"PolynetDWTCADx" is a sophisticated hybrid model that was developed to identify and distinguish colorectal cancer. In this study, the CKHK-22 dataset, comprising 24 classes, served as the introduction. The proposed method, which combines CNNs, DWTs, and SVMs, enhances the accuracy of feature extraction and classification.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Guangzhou Cadre and Talent Health Management Center, Guangzhou, China.
Background: Large language models have shown remarkable efficacy in various medical research and clinical applications. However, their skills in medical image recognition and subsequent report generation or question answering (QA) remain limited.
Objective: We aim to finetune a multimodal, transformer-based model for generating medical reports from slit lamp images and develop a QA system using Llama2.
Sci Rep
January 2025
Robotics Group, Department of Mechanic Engineering, Computer and Aerospace Sciences, University of León, 24006, León, Spain.
Symbolic anchoring is an important topic in robotics, as it enables robots to obtain symbolic knowledge from the perceptual information acquired through their sensors and maintain the link between that knowledge and the sensory data. In cognitive-based robots, this process of transforming sub-symbolic data generated by sensors to obtain and maintain symbolic knowledge is still an open problem. To address this issue, this paper presents SAILOR, a framework for symbolic anchoring integrated into ROS 2.
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