In this study, we used event-related potentials to examine how different dimensions of emotion-valence and arousal-influence different stages of word processing under different task demands. In two experiments, two groups of participants viewed the same single emotional and neutral words while carrying out different tasks. In both experiments, valence (pleasant, unpleasant, and neutral) was fully crossed with arousal (high and low). We found that the task made a substantial contribution to how valence and arousal modulated the late positive complex (LPC), which is thought to reflect sustained evaluative processing (particularly of emotional stimuli). When participants performed a semantic categorization task in which emotion was not directly relevant to task performance, the LPC showed a larger amplitude for high-arousal than for low-arousal words, but no effect of valence. In contrast, when participants performed an overt valence categorization task, the LPC showed a large effect of valence (with unpleasant words eliciting the largest positivity), but no effect of arousal. These data show not only that valence and arousal act independently to influence word processing, but that their relative contributions to prolonged evaluative neural processes are strongly influenced by the situational demands (and by individual differences, as revealed in a subsequent analysis of subjective judgments).
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http://dx.doi.org/10.3758/s13415-016-0402-y | DOI Listing |
J Neurosci Methods
January 2025
College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, China.
Background: Recognition of emotion changes is of great significance to a person's physical and mental health. At present, EEG-based emotion recognition methods are mainly focused on time or frequency domains, but rarely on spatial information. Therefore, the goal of this study is to improve the performance of emotion recognition by integrating frequency and spatial domain information under multi-frequency bands.
View Article and Find Full Text PDFFront Psychol
December 2024
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States.
Introduction: While the fact that visual stimuli synthesized by Artificial Neural Networks (ANN) may evoke emotional reactions is documented, the precise mechanisms that connect the strength and type of such reactions with the ways of how ANNs are used to synthesize visual stimuli are yet to be discovered. Understanding these mechanisms allows for designing methods that synthesize images attenuating or enhancing selected emotional states, which may provide unobtrusive and widely-applicable treatment of mental dysfunctions and disorders.
Methods: The Convolutional Neural Network (CNN), a type of ANN used in computer vision tasks which models the ways humans solve visual tasks, was applied to synthesize ("dream" or "hallucinate") images with no semantic content to maximize activations of neurons in precisely-selected layers in the CNN.
Sensors (Basel)
December 2024
Department of Information and Electronic Engineering, International Hellenic University, 57001 Thessaloniki, Greece.
Recent advances in emotion recognition through Artificial Intelligence (AI) have demonstrated potential applications in various fields (e.g., healthcare, advertising, and driving technology), with electroencephalogram (EEG)-based approaches demonstrating superior accuracy compared to facial or vocal methods due to their resistance to intentional manipulation.
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.
View Article and Find Full Text PDFBehav Sci (Basel)
December 2024
Department of Medicine and Surgery, University of Parma, Via Volturno, 39, 43125 Parma, PR, Italy.
Introduction: Post-Traumatic Stress Disorder (PTSD) is a highly prevalent disorder and a highly debilitating condition. Although current theories focused on depressed mood and intrusion as critical dimensions, the mechanism through which depression increases the risk of PTSD remains unclear. Research usually concentrates on the hyperactive negative valence system (NVS) (e.
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