Cultural influences on the concept of self is a very important topic for social cognitive neuroscientific exploration, as yet, little if anything is known about this topic at the neural level. The present study investigates this problem by looking at the Chinese culture's influence on the concept of self, in which the self includes mother. In Western cultures, self-referential processing leads to a memory performance advantage over other forms of semantic processing including mother-referential, other-referential and general semantic processing, and an advantage that is potentially localizable to the medial prefrontal cortex (MPFC). In Chinese culture, however, the behavioral study showed that mother-referential processing was comparable with self-referential processing in both memory performance and autonoetic awareness. The present study attempts to address whether similar neural correlates (e.g. MPFC) are acting to facilitate both types of referencing. Participants judged trait adjectives under three reference conditions of self, other and semantic processing in Experiment I, and a mother-reference condition replaced the other-reference condition in Experiment II. The results showed that when compared to other, self-referential processing yielded activations of MPFC and cingulate areas. However, when compared to mother, the activation of MPFC disappeared in self-referential processing, which suggests that mother and self may have a common brain region in the MPFC and that the Chinese idea of self includes mother.
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http://dx.doi.org/10.1007/s11427-004-5105-x | DOI Listing |
Nat Rev Neurosci
January 2025
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
The brain is always intrinsically active, using energy at high rates while cycling through global functional modes. Awake brain modes are tied to corresponding behavioural states. During goal-directed behaviour, the brain enters an action-mode of function.
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December 2024
Faculty of Human-Environment Studies, Kyushu University, Fukuoka, Japan.
The aim of the current study was to investigate visual scan patterns for the self-face in infants with the ability to recognize themselves with a photograph. 24-month-old infants (N = 32) were presented with faces including the self-face in the upright or inverted orientation. We also measured infants' ability to recognize oneself in a mirror and with a photograph.
View Article and Find Full Text PDFMajor Depressive Disorder (MDD) poses a significant public health challenge due to its high prevalence and the substantial burden it places on individuals and healthcare systems. Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) shows promise as a treatment for this disorder, although its mechanisms of action remain unclear. This study investigated whole-brain response patterns during rtfMRI-NF training to explain interindividual variability in clinical efficacy in MDD.
View Article and Find Full Text PDFBiol Psychiatry Cogn Neurosci Neuroimaging
December 2024
University of Arizona, SEMA Lab, Center for Consciousness Studies, Tuscon, AZ; Sanmai Technologies, PBC, Sunnyvale, CA.
Mindfulness has gained widespread recognition for its benefits to mental health, cognitive performance, and wellbeing. However, the multifaceted nature of mindfulness, encompassing elements like attentional focus, emotional regulation, and present-moment awareness, complicates its definition and measurement. A key component that may underlie its broad benefits is equanimity - the ability to maintain an open and non-reactive attitude toward all sensory experiences.
View Article and Find Full Text PDFBehav Res Methods
December 2024
CAP Team, Centre de Recherche en Neurosciences de Lyon - INSERM U1028 - CNRS UMR 5292 - UCBL - UJM, 95 Boulevard Pinel, 69675, Bron, France.
Artificial intelligence techniques offer promising avenues for exploring human body features from videos, yet no freely accessible tool has reliably provided holistic and fine-grained behavioral analyses to date. To address this, we developed a machine learning tool based on a two-level approach: a first lower-level processing using computer vision for extracting fine-grained and comprehensive behavioral features such as skeleton or facial points, gaze, and action units; a second level of machine learning classification coupled with explainability providing modularity, to determine which behavioral features are triggered by specific environments. To validate our tool, we filmed 16 participants across six conditions, varying according to the presence of a person ("Pers"), a sound ("Snd"), or silence ("Rest"), and according to emotional levels using self-referential ("Self") and control ("Ctrl") stimuli.
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