Objective: While in general arousal increases with positive or negative valence (a so-called V-shaped relation), there are large differences among individuals in how these two fundamental dimensions of affect are related in people's experience. In two studies, we examined two possible sources of this variation: personality and culture.
Method: In Study 1, participants (Belgian university students) recalled a recent event that was characterized by high or low valence or arousal and reported on their feelings and their personality in terms of the Five-Factor Model. In Study 2, participants from Canada, China/Hong Kong, Japan, Korea, and Spain reported on their feelings in a thin slice of time and on their personality.
Results: In Study 1, we replicated the V-shape as characterizing the relation between valence and arousal, and identified personality correlates of experiencing particular valence-arousal combinations. In Study 2, we documented how the V-shaped relation varied as a function of Western versus Eastern cultural background and personality.
Conclusions: The results showed that the steepness of the V-shaped relation between valence and arousal increases with Extraversion within cultures, and with a West-East distinction between cultures. Implications for the personality-emotion link and research on cultural differences in affect are discussed.
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http://dx.doi.org/10.1111/jopy.12258 | DOI Listing |
Psychophysiology
January 2025
Department of Psychology, University of Georgia, Athens, Georgia, USA.
Emotional experiences involve dynamic multisensory perception, yet most EEG research uses unimodal stimuli such as naturalistic scene photographs. Recent research suggests that realistic emotional videos reliably reduce the amplitude of a steady-state visual evoked potential (ssVEP) elicited by a flickering border. Here, we examine the extent to which this video-ssVEP measure compares with the well-established Late Positive Potential (LPP) that is reliably larger for emotional relative to neutral scenes.
View Article and Find Full Text PDFBiol Psychol
January 2025
Department of Psychology, Institute for Mind and Brain, University of South Carolina, Columbia, SC 29201, USA. Electronic address:
We examined differences in physiological responses to aversive and non-aversive naturalistic audiovisual stimuli and their auditory and visual components within the same experiment. We recorded five physiological measures that have been shown to be sensitive to affect: electrocardiogram, electromyography (EMG) for zygomaticus major and corrugator supercilii muscles, electrodermal activity (EDA), and skin temperature. Valence and arousal ratings confirmed that aversive stimuli were more negative in valence and higher in arousal than non-aversive stimuli.
View Article and Find Full Text PDFMem Cognit
January 2025
École de Psychologie, Université de Moncton, Moncton, NB, E1A 3E9, Canada.
In short-term ordered recall tasks, phonological similarity impedes item and order recall, while semantic similarity benefits item recall with a weak or null effect on order recall. Ishiguro and Saito recently suggested that these contradictory findings were due to an inadequate assessment of semantic similarity. They proposed a novel measure of semantic similarity based on the distance between items in a three-dimensional space composed of the semantic dimensions of valence, arousal, and dominance.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Emotion recognition is an advanced technology for understanding human behavior and psychological states, with extensive applications for mental health monitoring, human-computer interaction, and affective computing. Based on electroencephalography (EEG), the biomedical signals naturally generated by the brain, this work proposes a resource-efficient multi-entropy fusion method for classifying emotional states. First, Discrete Wavelet Transform (DWT) is applied to extract five brain rhythms, i.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Psychology, City College, City University of New York, New York, NY 10031.
Looking at the world often involves not just seeing things, but feeling things. Modern feedforward machine vision systems that learn to perceive the world in the absence of active physiology, deliberative thought, or any form of feedback that resembles human affective experience offer tools to demystify the relationship between seeing and feeling, and to assess how much of visually evoked affective experiences may be a straightforward function of representation learning over natural image statistics. In this work, we deploy a diverse sample of 180 state-of-the-art deep neural network models trained only on canonical computer vision tasks to predict human ratings of arousal, valence, and beauty for images from multiple categories (objects, faces, landscapes, art) across two datasets.
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