Search Results (57)

Exploring the interactions between compulsive buying and dietary practices is crucial in terms of understanding these behaviors from a behavioral standpoint. This paper investigates the relationships between compulsive buying (CB) and non-compulsive buying (non-CB), focusing on the influence of dietary habits (TFEQ), body mass index (BMI), and emotional valence. The study involved a representative sample of 707 Polish adults and employed tools such as the Compulsive Buying Scale, the Three-Factor Eating Questionnaire (TFEQ), and the Emotional Appetite Questionnaire (EMAQ). The results revealed that compulsive buyers (CBs) had higher levels of cognitive restraint, emotional eating, and uncontrolled eating than non-compulsive buyers (non-CBs). Importantly, emotional valence—which includes both positive and negative emotions—significantly influenced dietary behaviors, illustrating the complex role emotions play in food consumption. Additionally, the results highlighted that the BMI significantly affects these relationships, suggesting different eating patterns across BMI categories. This study underscores the need for targeted interventions focusing on psychological and nutritional aspects to address these interconnected compulsive behaviors. Full article

Animal-assisted services (AAS) have been shown in multiple studies to improve a range of human psychological and physical health benefits. The aim of this pilot study is to investigate simultaneously two psycho-physiological indicators of the valence of interactions in the context of dog-assisted activities in children diagnosed with autism spectrum disorder. Ten children and four dogs experienced in AAS were involved, lasting 90 days, in weekly one-hour sessions. Before and after each session, saliva was taken in both dogs and children for determination of salivary oxytocin and cortisol levels. In addition, at the end of the program, a questionnaire was administered to both parents and dog handlers to assess the impact of AAS in children and dogs. Our results revealed no statistically significant change in cortisol and oxytocin levels in dogs enrolled throughout the sessions, while an increasing trend was noted for salivary oxytocin in 50% of the dogs and for salivary cortisol in all dogs at the end of the AAS, when compared to the pre-AAS. Salivary cortisol measurement in children with an autistic neurotype highlighted a statistically significant increase at the end of the AAS when compared to the pre-AAS, but this was not observed for oxytocin level evaluations. Regarding the perception of the children’s parents about the effects of the program, our data reported an improvement in sociability of the children in 100 percent of the cases. Furthermore, dog handlers reported an absence of signs of stress in their dogs during the sessions. Although the perceived effectiveness and quality of AAS has been demonstrated in the literature, the need to carefully select the dogs involved, considering their skills and needs, is critical to ensure their well-being in various therapeutic settings. Full article

Understanding the mindset of people who die by suicide remains a key research challenge. We map conceptual and emotional word–word co-occurrences in 139 genuine suicide notes and in reference word lists, an Emotional Recall Task, from 200 individuals grouped by high/low depression, anxiety, and stress levels on DASS-21. Positive words cover most of the suicide notes’ vocabulary; however, co-occurrences in suicide notes overlap mostly with those produced by individuals with low anxiety (Jaccard index of 0.42 for valence and 0.38 for arousal). We introduce a “words not said” method: It removes every word that corpus A shares with a comparison corpus B and then checks the emotions of “residual” words in $A-B$. With no leftover emotions, A and B are similar in expressing the same emotions. Simulations indicate this method can classify high/low levels of depression, anxiety and stress with 80% accuracy in a balanced task. After subtracting suicide note words, only the high-anxiety corpus displays no significant residual emotions. Our findings thus pin anxiety as a key latent feature of suicidal psychology and offer an interpretable language-based marker for suicide risk detection. Full article

We explore the feasibility of classifying perceived pain intensity—despite the stimulus being identical—using multimodal physiological signals and self-reported emotional ratings. A total of 112 healthy participants watched the same anger-inducing video, yet reported varying pain intensities (5, 6, or 7 on a 7-point scale). We recorded electrocardiogram, skin conductance (SC), respiration, photoplethysmogram results, and finger temperature, extracting 12 physiological features. Participants also rated their valence and arousal. Using a random forest model, we classified pain versus baseline and distinguished intensity levels. Compared to baseline, the painful stimulus altered heart rate variability, SC, respiration, and pulse transit time (PTT). Higher perceived pain correlated with more negative valence, higher arousal, and elevated SC, suggesting stronger sympathetic activation. The classification of baseline versus pain using SC and respiratory features reached an F1 score of 0.83. For intensity levels 6 versus 7, including PTT and skin conductance response along with valence achieved an F1 score of 0.73. These findings highlight distinct psychophysiological patterns that reflect perceived intensity under the same stimulus. SC features emerged as key biomarkers, while valence and arousal offered complementary insights, supporting the development of personalized, psychologically informed pain assessment systems. Full article

Commercial progress concerning biobased materials has been slow, with success depending on functionality and emotional responses. Emotional interaction research provides a novel way to shift perceptions of biobased materials. This study proposes a human-centered emotional design framework using biobased tea waste to explore how sensory properties (form, color, odor, surface roughness) shape emotional responses and contribute to sustainable wellbeing. We used a mixed-methods approach combining subjective evaluations (Self-Assessment Manikin scale) with physiological metrics (EEG, skin temperature, pupil dilation) from 24 participants. Results demonstrated that spherical forms and high surface roughness significantly enhanced emotional valence and arousal, while warm-toned yellow samples elicited 23% higher pleasure ratings than dark ones. Neurophysiological data revealed that positive emotions correlated with reduced alpha power in the parietal lobe (αPz, p = 0.03) and a 0.3 °C rise in skin temperature, whereas negative evaluations activated gamma oscillations in central brain regions (γCz, p = 0.02). Mapping these findings to human factors engineering principles, we developed actionable design strategies—such as texture-optimized surfaces and color–emotion pairings—that transform tea waste into emotionally resonant, sustainable products. This work advances emotional design’s role in fostering ecological sustainability and human wellbeing, demonstrating how human-centered engineering can align material functionality with psychological fulfillment. Full article

Many consumers today pursue health goals to adopt healthier behaviors, and interest in promoting healthy eating habits in gastronomy is growing. Empirical evidence demonstrates that sensory cues (e.g., food color, food shapes, and background music) influence healthy eating behavior. However, the theoretical understanding of how sensory cues shape healthy food choices remains unclear. Specifically, this study develops the sensory–healthy eating model, a theoretical framework that explains how and when sensory cues influence healthy eating behavior (e.g., food choices and intake). By integrating related theories and empirical findings across interdisciplinary fields, we identify which sensory cues shape healthy eating and the psychological processes through which they operate. The theoretical model proposes that (1) sensory cues evoke cognitive (higher evaluation, lower potency, lower activity) and/or affective responses (positive valence, lower arousal), (2) these responses shape the perceived healthiness of foods based on their characteristics and quantity, and (3) the influence of perceived food healthiness on healthy eating behavior is stronger for consumers with health goals or motives. Our model provides a valuable framework for researchers and practitioners in marketing, food science, and gastronomy to promote healthy eating behavior. Full article

Introduction: Virtual reality (VR) has garnered increasing attention in oncology due to its potential to enhance patient care by alleviating anxiety and emotional distress. The present work evaluates the hypothesis proposed by a recent theoretical model that engagement and the sense of presence are key mediators that impact the degree of beneficial effects that VR may have on the emotional well-being of breast cancer patients. Methods: This study draws on data from three previous studies comprising 156 breast cancer patients. The psychological variables of well-being studied included emotional dimensions measured before and after exposure to a virtual environment, as well as factors related to immersive qualities. Correlation and mediation analyses were conducted to explore relationships among said variables, namely, one’s tendency to be immersed in an activity, engagement, spatial presence, and emotional well-being (i.e., valence and arousal) of the patients. Results: Engagement plays a crucial mediating role between tendency of immersion, spatial presence, and positive emotional responses. Patients with a greater tendency toward immersion and higher engagement in the virtual environment showed significant emotional improvements. However, tendency of immersion and spatial presence alone did not directly lead to more positive emotional experiences; their influence was primarily exerted through engagement. Conclusions: Engagement emerges as a fundamental lever for maximizing the psychological benefits of VR in oncology. Clinical interventions using VR should prioritize optimizing engagement in immersive environments to improve patients’ emotional state throughout their cancer treatment journey. Full article

Artificial intelligence (AI) models can sense subjective affective states from facial images. Although recent psychological studies have indicated that dimensional affective states of valence and arousal are systematically associated with facial expressions, no AI models have been developed to estimate these affective states from facial images based on empirical data. We developed a recurrent neural network-based AI model to estimate subjective valence and arousal states from facial images. We trained our model using a database containing participant valence/arousal states and facial images. Leave-one-out cross-validation supported the validity of the model for predicting subjective valence and arousal states. We further validated the effectiveness of the model by analyzing a dataset containing participant valence/arousal ratings and facial videos. The model predicted second-by-second valence and arousal states, with prediction performance comparable to that of FaceReader, a commercial AI model that estimates dimensional affective states based on a different approach. We constructed a graphical user interface to show real-time affective valence and arousal states by analyzing facial video data. Our model is the first distributable AI model for sensing affective valence and arousal from facial images/videos to be developed based on an empirical database; we anticipate that it will have many practical uses, such as in mental health monitoring and marketing research. Full article

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.e., delta, theta, alpha, beta, and gamma, from EEG signals, followed by the acquisition of multi-entropy features, including Spectral Entropy (PSDE), Singular Spectrum Entropy (SSE), Sample Entropy (SE), Fuzzy Entropy (FE), Approximation Entropy (AE), and Permutation Entropy (PE). Then, such entropies are fused into a matrix to represent complex and dynamic characteristics of EEG, denoted as the Brain Rhythm Entropy Matrix (BREM). Next, Dynamic Time Warping (DTW), Mutual Information (MI), the Spearman Correlation Coefficient (SCC), and the Jaccard Similarity Coefficient (JSC) are applied to measure the similarity between the unknown testing BREM data and positive/negative emotional samples for classification. Experiments were conducted using the DEAP dataset, aiming to find a suitable scheme regarding similarity measures, time windows, and input numbers of channel data. The results reveal that DTW yields the best performance in similarity measures with a 5 s window. In addition, the single-channel input mode outperforms the single-region mode. The proposed method achieves 84.62% and 82.48% accuracy in arousal and valence classification tasks, respectively, indicating its effectiveness in reducing data dimensionality and computational complexity while maintaining an accuracy of over 80%. Such performances are remarkable when considering limited data resources as a concern, which opens possibilities for an innovative entropy fusion method that can help to design portable EEG-based emotion-aware devices for daily usage. Full article

Despite their diverse assumptions, clinical psychology approaches share the goal of mental health promotion. The literature highlights their usefulness, but also some issues related to their effectiveness, such as their difficulties in monitoring psychological change. The elective strategy for activating and managing psychological change is the clinical question. But how do different types of questions foster psychological change? This work tries to answer this issue by studying therapist–patient interactions with a ML model for text analysis. The goal was to investigate how psychological change occurs thanks to different types of questions, and to see if the ML model recognized this difference in analyzing patients’ answers to therapists’ clinical questions. The experimental dataset of 14,567 texts was divided based on two different question purposes, splitting answers in two categories: those elicited by questions asking patients to start describing their clinical situation, or those from asking them to detail how they evaluate their situation and mental health condition. The hypothesis that these categories are distinguishable by the model was confirmed by the results, which corroborate the different valences of the questions. These results foreshadow the possibility to train ML and AI models to suggest clinical questions to therapists based on patients’ answers, allowing the increase of clinicians’ knowledge, techniques, and skills. Full article

Canine-assisted interactions (CAIs) have been explored to offer therapeutic benefits to human participants in various contexts, from addressing cancer-related fatigue to treating post-traumatic stress disorder. Despite their widespread adoption, there are still unresolved questions regarding the outcomes for both humans and animals involved in these interactions. Previous attempts to address these questions have suffered from core methodological weaknesses, especially due to absence of tools for an efficient objective evaluation and lack of focus on the canine perspective. In this article, we present a first-of-its-kind system and study to collect simultaneous and continuous physiological data from both of the CAI interactants. Motivated by our extensive field reviews and stakeholder feedback, this comprehensive wearable system is composed of custom-designed and commercially available sensor devices. We performed a repeated-measures pilot study, to combine data collected via this system with a novel dyadic behavioral coding method and short- and long-term surveys. We evaluated these multimodal data streams independently, and we further correlated the psychological, physiological, and behavioral metrics to better elucidate the outcomes and dynamics of CAIs. Confirming previous field results, human electrodermal activity is the measure most strongly distinguished between the dyads’ non-interaction and interaction periods. Valence, arousal, and the positive affect of the human participant significantly increased during interaction with the canine participant. Also, we observed in our pilot study that (a) the canine heart rate was more dynamic than the human’s during interactions, (b) the surveys proved to be the best indicator of the subjects’ affective state, and (c) the behavior coding approaches best tracked the bond quality between the interacting dyads. Notably, we found that most of the interaction sessions were characterized by extended neutral periods with some positive and negative peaks, where the bonded pairs might display decreased behavioral synchrony. We also present three new representations of the internal and overall dynamics of CAIs for adoption by the broader field. Lastly, this paper discusses ongoing options for further dyadic analysis, interspecies emotion prediction, integration of contextually relevant environmental data, and standardization of human–animal interaction equipment and analytical approaches. Altogether, this work takes a significant step forward on a promising path to our better understanding of how CAIs improve well-being and how interspecies psychophysiological states can be appropriately measured. Full article

The capacity to identify risks during the pre-contract phase is crucial for effective contract management. Among the various factors that can influence this ability, emotions play a significant role in determining the risk recognition capabilities of individuals. This study aims to investigate the complex interplay between emotional states, personality traits, learning styles, and risk recognition during contract review with physiological and psychological responses. Firstly, a theoretical framework that delineates the relationship among the demographic and emotional factors and risk recognition performance during contract review is proposed. Secondly, an experiment is conducted to record the physiological and psychological responses. Finally, a hierarchical regression model is employed. The results indicated that emotional valence and arousal significantly influence risk recognition performance (p < 0.001), while individual factors such as personality traits and risk propensity indirectly affect risk recognition performance through the mediating role of emotions (p < 0.01). The research contributes to the existing literature by elucidating the indirect pathways through which individual characteristics influence risk perception. These insights can inform sustainable risk management strategies, helping organizations and individuals make more informed and effective decisions in contract management. Full article

Emotion recognition is a valuable technique to monitor the emotional well-being of human beings. It is found that around 60% of people suffer from different psychological conditions like depression, anxiety, and other mental issues. Mental health studies explore how different emotional expressions are linked to specific psychological conditions. Recognizing these patterns and identifying their emotions is complex in human beings since it varies from each individual. Emotion represents the state of mind in response to a particular situation. These emotions, that are collected using EEG electrodes, need detailed emotional analysis to contribute to clinical analysis and personalized health monitoring. Most of the research works are based on valence and arousal (VA) resulting in two, three, and four emotional classes based on their combinations. The main objective of this paper is to include dominance along with valence and arousal (VAD) resulting in the classification of 16 classes of emotional states and thereby improving the number of emotions to be identified. This paper also considers a 2-class emotion, 4-class emotion, and 16-class emotion classification problem, applies different models, and discusses the evaluation methodology in order to select the best one. Among the six machine learning models, KNN proved to be the best model with the classification accuracy of 95.8% for 2-class, 91.78% for 4-class and 89.26% for 16-class. Performance metrics like Precision, ROC, Recall, F1-Score, and Accuracy are evaluated. Additionally, statistical analysis has been performed using Friedman Chi-square test to validate the results. Full article

The global population has been significantly affected by the pandemic in terms of physical and mental health. According to transactional theory, individuals have undergone an adaptation process influenced by cognitive control abilities. Emotional responses to COVID-19-related stimuli may interfere with top-down attentional processes, thereby hindering adaptation. This study aimed to investigate the impact of COVID-19-related stimuli on attentional processing and to determine whether psychological factors could modulate these effects. A sample of 96 healthy undergraduate students participated in an emotional Stroop task in which they were presented with a series of stimuli, including both neutral and negative COVID-19-related as well as non-COVID-19 stimuli. COVID-19-related PTSD, as an index of distress (PTSS), and trait anxiety were evaluated. Results showed that participants were more accurate in identifying COVID-19-related stimuli compared to non-COVID-19 stimuli. Being female and having higher retrospective PTSS scores related to COVID-19 were predictive of faster reaction times for both neutral and negative COVID-19-related stimuli. This heightened attentional bias toward COVID-19-related stimuli suggests that individuals may be more sensitive to stimuli associated with the pandemic. The results suggest that the association between COVID-19 stimuli and attentional biases extends beyond emotional valence, being retrospectively influenced by mental health, suggesting potential pathways to future mental health challenges. Full article

This crossover randomized controlled trial examined the acute psychological effects of a bout of moderate-intensity continuous aerobic exercise (MICE) and a bout of high-intensity functional exercise (HIFE), relative to a no-exercise sedentary control (SED), in participants (N = 21; 15 f; 24.7 ± 9.3 years) with subsyndromal post-traumatic stress disorder (PTSD). Affective state (Energy, Tiredness, Tension, Calmness) was assessed before (Pre), immediately after (Post 0), 20-min after (Post 20), and 40-min after (Post 40) each condition. Affective valence was assessed during each condition, and exercise enjoyment was assessed at Post 0. Enjoyment was significantly greater following HIFE and MICE relative to SED. Energy was significantly increased Post 0 HIFE and MICE but decreased Post 0 SED. Tension was reduced following all conditions and was significantly lower at Post 40 relative to Pre for HIFE, MICE, and SED. Tiredness was significantly reduced at Post 40 relative to Pre following MICE only, while Calmness was significantly lower at Post 40 relative to Pre following MICE and SED. Overall, both exercise conditions were enjoyed to a greater extent than the control, but MICE may provide greater psychological benefits with respect to Calmness and Tiredness. This study is among the first to assess acute changes in affective states relative to various exercise modes in individuals living with subsyndromal PTSD. Full article