The ability of visual self-recognition in animals and infants is considered a hallmark of the domain-general cognitive representation of the self, which also underpins higher social ability. Cortical regions activated during self-face recognition in human adults have been accordingly expected to play the domain-general role in self-processing. However, there is no evidence of the involvement of this network in non-face domains. We compared cortical responses during face and name recognition of self, a friend, and an unfamiliar person, using functional magnetic resonance imaging (fMRI). Recognition of the self-face activated the right inferior frontal, precentral, supramarginal, and bilateral ventral occipitotemporal regions, consistent with previous findings, whereas these regions did not show self-specific activation during name recognition. During both face and name recognitions, increased activation for the friend and unfamiliar person than for the self was observed in the bilateral temporoparietal regions, and higher activation for the self and friend than for the unfamiliar person was observed in the medial cortical structures. These results suggest that the role of the self-specific networks during face recognition is not domain-general, but rather face-specific, and that the medial cortical structures, which are also implicated in self-referential processes, are not relevant to self-other distinction during face or name recognition. Instead, the reduced temporoparietal activation is a domain-general characteristic of the cortical response during self-recognition, which may reflect suppression of an automatic preparatory process for social interaction, possibly paralleling the disappearance of social behavior to the mirrored self-image at the emergence of self-recognition in animals and infants.
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http://dx.doi.org/10.1016/j.neuroimage.2008.03.054 | DOI Listing |
J Biomed Inform
January 2025
University of Manchester, United Kingdom.
Objective: Extracting named entities from clinical free-text presents unique challenges, particularly when dealing with discontinuous entities-mentions that are separated by unrelated words. Traditional NER methods often struggle to accurately identify these entities, prompting the development of specialised computational solutions. This paper systematically reviews and presents the methodologies developed for Discontinuous Named Entity Recognition in clinical texts, highlighting their effectiveness and the challenges they face.
View Article and Find Full Text PDFJ Integr Neurosci
January 2025
Department of Psychology, The Affiliated Hospital of Jiangnan University, 214151 Wuxi, Jiangsu, China.
Background: Deficits in emotion recognition have been shown to be closely related to social-cognitive functioning in schizophrenic. This study aimed to investigate the event-related potential (ERP) characteristics of social perception in schizophrenia patients and to explore the neural mechanisms underlying these abnormal cognitive processes related to social perception.
Methods: Participants included 33 schizophrenia patients and 35 healthy controls (HCs).
Sensors (Basel)
January 2025
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
With the rapid development of AI algorithms and computational power, object recognition based on deep learning frameworks has become a major research direction in computer vision. UAVs equipped with object detection systems are increasingly used in fields like smart transportation, disaster warning, and emergency rescue. However, due to factors such as the environment, lighting, altitude, and angle, UAV images face challenges like small object sizes, high object density, and significant background interference, making object detection tasks difficult.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Information Engineering, University of Padova, 35122 Padova, Italy.
Sleep posture is a key factor in assessing sleep quality, especially for individuals with Obstructive Sleep Apnea (OSA), where the sleeping position directly affects breathing patterns: the side position alleviates symptoms, while the supine position exacerbates them. Accurate detection of sleep posture is essential in assessing and improving sleep quality. Automatic sleep posture detection systems, both wearable and non-wearable, have been developed to assess sleep quality.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Automation, Beijing Institute of Technology, Beijing 100081, China.
Existing autonomous driving systems face challenges in accurately capturing drivers' cognitive states, often resulting in decisions misaligned with drivers' intentions. To address this limitation, this study introduces a pioneering human-centric spatial cognition detecting system based on drivers' electroencephalogram (EEG) signals. Unlike conventional EEG-based systems that focus on intention recognition or hazard perception, the proposed system can further extract drivers' spatial cognition across two dimensions: relative distance and relative orientation.
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