Personality psychology

Background. Although quite a few classifications of coping strategies have been proposed, with different premises, much less is known about the methods of interpretation and how people using different types of coping perceive their life difficulties.

Objective. To develop a verifiable algorithm for classifying perceived difficulties. The proposed classification was developed deductively, using “approach–avoidance” as the basis for cognitive activity aimed at taking on (approaching) a difficult situation or escaping from it, avoiding a solution to the problem. The classification comprises 1) driven, 2) maximum, 3) optimal, 4) ambivalent, and 5) evasive types of perception of difficult life tasks (DLTs). Types 1, 2, and 3 correspond to approaching a difficult situation, and 5 to avoiding it. Type 4 involves a combination of approach and avoidance.

Design. The type is determined by an expert psychologist in a complex way, based on a combination of 1) the respondent’s profile according to the “Types of Orientations in Difficult Situations” questionnaire (TODS) and 2) features that are significant for the type as shown in qualitative data – descriptions of DLTs (answers to open questions). Machine learning methods and A.S. Podkolzin’s computer modeling of logical processes are used to develop the algorithm. The sample comprised 611 adult participants (M_age = 25; SD = 5.8; 427 women).

Results. Using machine-learning algorithms, various options were tested for separation into classes; the best results were obtained with a combination of markup and questionnaire features and sequential separation of classes. Using computer modeling of logical processes, decisive classification rules were tested, based on the psychologist’s description of the features of the type of perception. The classification accuracy using these rules of the final algorithm is 77.17% of cases.

Conclusion. An algorithm was obtained that allows step-by-step tracing of the process by which a classification problem is solved by the psychologist. We propose a new model for studying situational perception using a mixed research design and computer-modeling methods.

Background. Advanced AI models such as the large language model GPT-4 demonstrate sophisticated intellectual capabilities, sometimes exceeding human intellectual performance. However, the emotional competency of these models, along with their underlying mechanisms, has not been sufficiently evaluated.

Objective. Our research aimed to explore different emotional intelligence domains in GPT-4 according to the Mayer–Salovey–Caruso model. We also tried to find out whether GPT-4's answer accuracy is consistent with its explanation of the answer.

Design. The Russian version of the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT) sections was used in this research, with questions asked as text prompts in separate, independent ChatGPT chats three times each.

Results. High scores were achieved by the GPT-4 Large Language Model on the Understanding Emotions scale (with scores of 117, 124, and 128 across the three runs) and the Strategic Emotional Intelligence scale (with scores of 118, 121, and 122). Average scores were obtained on the Managing Emotions scale (103, 108, and 110 points). However, the Using Emotions to Facilitate Thought scale yielded low and less reliable scores (85, 86, and 88 points). Four types of explanations for the answer choices were identified: Meaningless sentences; Relation declaration; Implicit logic; and Explicit logic. Correct answers were accompanied by all types of explanations, whereas incorrect answers were only followed by Meaningless sentences or Explicit logic. This distribution aligns with observed patterns in children when they explore and elucidate mental states.

Conclusion. GPT-4 is capable of emotion identification and managing emotions, but it lacks deep reflexive analysis of emotional experience and the motivational aspect of emotions.

Background. Previous studies have assumed that a materialistic value orientation is correlates with personality traits such as honesty, neuroticism, and agreeableness. Less is known about the relationship between features of a materialistic orientation such as acquisition centrality, acquisition as the pursuit of happiness, and possession-defined success, and the Dark Triad traits. This article presents a study on the relationship between materialism, the Dark Triad traits (Machiavellianism, narcissism, and psychopathy), and money management. 

Objective. The study aimed to investigate whether groups exhibiting various combinations of materialism and Dark Triad traits have disparities in financial control and accountability, which serve as indicators of money management.

Design. Questionnaire-based surveys were conducted online, with a total of 442 undergraduate students age 18 to 25 participating. The participants filled out the Short Dark Triad measure, the Material Values Scale, and the Money Management Scale, in addition to providing their demographics.

Results. Four combinations of materialistic and Dark Triad traits were revealed (Bright and Dark Materialists and Non-materialists). Bright and Dark Materialists were more self-centered and manipulative than Non-materialists. Strong materialism, paired with the lowest degree of Dark Triad characteristics, resulted in the worst financial management. On the contrary, individuals with low materialistic tendencies in addition to high Dark Triad traits tended to have better ability to managing their finances.

Conclusion. It is possible to assume that materialism is a strategy for obtaining riches, positions, and reputation at the cost of others in the case of "dark" personalities. Nevertheless, those with low levels of materialism and low Dark Triad characteristics showed better abilities to handle their finances in terms of control and responsibility.