The effects of immobilization stress on electrodermal activity (EDA); skin conductance response magnitude and rate, skin conductance level and habituation number, and brain catecholamine levels; norepinephrine (NE) and dopamine (DA) were investigated in rats. Electrodermal activity was recorded using constant current method. Brain catecholamine levels were determined by a spectrophotophlorometric method. Electrodermal activity parameters (except skin conductance level) increased during immobilization. It was observed that, during immobilization stress, the alteration of norepinephrine and dopamine levels in rat brain was related to cerebral region and the duration of immobilization stress. It was concluded that these electrodermal activity alterations can be attributed to the changes in central norepinephrine metabolism induced by immobilization.
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http://dx.doi.org/10.3109/00207459209003281 | DOI Listing |
Traffic Inj Prev
January 2025
China Merchants Chongqing Communications Technology Research & Design Institute Co., Ltd, Chongqing, China.
Objective: This study aimed to analyze the influence of different tunnel reinforcement measures on drivers and to evaluate the associated driving safety risks.
Methods: Experimental data of driving behavior and physiological response were collected under different driving simulation scenarios, such as cover arch erection, corrugated steel, grouting, Steel strips, and fire; an evaluation index system was established based on electrocardiographic (ECG), electrodermal activity(EDA), standard deviation of speed (SDSP), Steering Entropy(SE), standard deviation of lateral position (SDLP) and other indices. The classical domain rank standard of each evaluation index was divided using K-Means algorithm, and a synthetic evaluation matter-element model was established to comprehensively evaluate and analyze the safety risks of each scenario.
Front Psychol
December 2024
Institute for Logic, Language and Computation, University of Amsterdam, Amsterdam, Netherlands.
The key function of storytelling is a meeting of hearts: a resonance in the recipient(s) of the story narrator's emotion toward the story events. This paper focuses on the role of gestures in engendering emotional resonance in conversational storytelling. The paper asks three questions: Does story narrators' gesture expressivity increase from story onset to climax offset (RQ #1)? Does gesture expressivity predict specific EDA responses in story participants (RQ #2)? How important is the contribution of gesture expressivity to emotional resonance compared to the contribution of other predictors of resonance (RQ #3)? 53 conversational stories were annotated for a large number of variables including Protagonist, Recency, Group composition, Group size, Sentiment, and co-occurrence with quotation.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Faculty of Information Science and Technology, Beijing University of Technology, Beijing 100124, China.
With the increasing complexity of urban roads and rising traffic flow, traffic safety has become a critical societal concern. Current research primarily addresses drivers' attention, reaction speed, and perceptual abilities, but comprehensive assessments of cognitive abilities in complex traffic environments are lacking. This study, grounded in cognitive science and neuropsychology, identifies and quantitatively evaluates ten cognitive components related to driving decision-making, execution, and psychological states by analyzing video footage of drivers' actions.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Nursing and Physiotherapy, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain.
Background: Motor imagery is the mental representation of a movement without physical execution. When motor imagery is performed to enhance motor learning and performance, participants must reach a temporal congruence between the imagined and actual movement execution. Identifying factors that can influence this capacity could enhance the effectiveness of motor imagery programs.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.
The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human-computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants.
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