Quantum concepts in Psychology: Exploring the interplay of physics and the human psyche.

This paper delves into the innovative intersection of quantum mechanics and psychology, examining the potential of quantum principles to provide fresh insights into human emotions, cognition, and consciousness. Drawing parallels between quantum phenomena such as superposition, entanglement, tunneling, decoherence and their psychological counterparts, we present a quantum-psychological model that reimagines emotional states, cognitive breakthroughs, interpersonal relationships, and the nature of consciousness. The study uses computational models and simulations to explore this interdisciplinary fusion's implications and applications, highlighting its potential benefits and inherent challenges. While quantum concepts offer a rich metaphorical lens to view the intricacies of human experience, it is essential to approach this nascent framework with enthusiasm and skepticism. Rigorous empirical validation is paramount to realize its full potential in research and therapeutic contexts. This exploration stands as a promising thread in the tapestry of intellectual history, suggesting a deeper understanding of the human psyche through the lens of quantum mechanics.

Grandiose narcissism indirectly associates with lower psychopathology across five countries.

Using five independent non-clinical cross-cultural samples (total N = 3649; overall Mage = 29.31; 31% male and 69% female), this study explored the extent to which Dark Triad traits were indirectly associated with symptoms of psychopathology through mental toughness. Although Machiavellianism and psychopathy have not been studied extensively in this context, previous research (both cross-sectional and longitudinal) reports that grandiose narcissism increases mental toughness contributing indirectly to positive outcomes such as lower anxiety, stress, and depression. Accordingly, this study examined Machiavellianism, psychopathy, and narcissism in the context of mental toughness and psychopathology. A particular focus was placed on investigating negative relationships between grandiose narcissism and psychopathology. Participants completed self-report measures assessing the Dark Triad, mental toughness, and psychopathology. In all samples, grandiose narcissism exerted moderate negative, indirect associations with anxiety, stress, and depression through mental toughness. Relationships between Machiavellianism and psychopathy and psychopathology were generally weak and positive but varied across countries. Findings provided further cross-cultural support for a mediation model in which grandiose narcissism is related to higher mental toughness and lower psychopathology. Outcomes from this study indicate that exploration of the link between grandiose narcissism and resilience traits such as mental toughness can provide important conceptual insights into the adaptive properties of narcissism, and help to explain why grandiose narcissism is associated with a decrease in some psychopathological symptoms.

The Revised Assessment of Sadistic Personality (ASP-8): Evidence for Validity across Four Countries.

Subclinical sadism, characterized by infliction of cruelty, aggression, or humiliation on another for subjugation or pleasure, provides important information in the prediction of aversive behaviors that have implications for individuals' and society's well-being worldwide. Given sadism's universal relevance, it is imperative that researchers ensure valid and reliable trait measurement not only among English-speaking individuals, but also cross-nationally among countries in which sadism remains relatively understudied. The objective of the current research was to validate the revised version of the Assessment of Sadistic Personality (ASP-8) (Plouffe et al., 2017) across samples of Russian (n = 1087, Mage = 37.36, SD = 10.36), Greek (n = 1195, Mage = 35.64, SDage = 13.08), Serbian (n = 443, Mage = 28.10, SDage = 6.60), and British (n = 511, Mage = 28.50, SDage = 11.62) adults. Overall, results supported the reliability, dimensionality, and scalar/partial scalar measurement invariance of the ASP-8 across cross-national samples. Convergent and discriminant validity were mostly supported through correlations with general personality traits, the Dark Triad, emotional intelligence, mental toughness, depression, anxiety, stress, satisfaction with life, aggression, and attitudes toward social groups. Based on our findings, we recommend the use of the ASP-8 in future investigations of aversive traits.

Measurement Invariance of the Scale of Positive and Negative Experience Across 13 Countries.

The Scale of Positive and Negative Experience (SPANE) is widely used to measure emotional experiences, but not much is known about its cross-cultural utility. The present study evaluated the measurement invariance of the SPANE across adult samples (N = 12,635; age range = 18-85 years; 58.2% female) from 13 countries (China, Colombia, Germany, Greece, India, Italy, Japan, Poland, Portugal, Serbia, Spain, Turkey, and the United States). Configural and partial scalar invariance of the SPANE were supported. Three items capturing specific negative emotions (sad, afraid, and angry) were found to be culturally noninvariant. Our findings suggest that the SPANE's positive emotion terms and general negative emotion terms (e.g., negative and unpleasant) might be more suitable for cross-cultural studies on emotions and well-being, whereas caution is needed when comparing countries using the SPANE's specific negative emotion items.

Early COVID-19 quarantine: A machine learning approach to model what differentiated the top 25% well-being scorers.

This study focused on the interaction of demographics and well-being. Diener's subjective well-being (SWB) was successfully validated with Exploratory Graph Analysis and Confirmatory Factor Analysis to track well-being differences of the COVID-19 quarantined individuals. Six tree-based Machine Learning models were trained to classify top 25% SWB scorers during COVID-19 quarantine, after data-splitting (train 70%, test 30%). The model input variables were demographics, to avoid overlapping of inputs-outputs. A 10-fold cross-validation method (70%-30%) was then implemented in the training session to select the optimal Machine Learning model among the six tested. A CART classification was the optimal algorithm (Train-Accuracy = 0.77, Test-Accuracy = 0.75). A clean, three-node tree suggested that if someone spends time on perceived creative activities during the COVID-19 quarantine, under clearly described conditions, he/she had high probabilities to be a top subjective well-being scorer. The key importance of creative activities was subsequently cross-validated with three different model configurations: (1) a different tree-based model (Test-Accuracy =0.75); (2) a different operationalization of subjective well-being (Test-Accuracy =0.75) and (3) a different construct (depression; Test-Accuracy =0.73). This is an integrative approach to study individual differences in subjective well-being, bridging Exploratory Graph Analysis and Machine Learning in a single research cycle with multiples cross-validations.