Land management intensity shapes ecosystem service provision, socio-ecological resilience and is central to sustainable transformation. Yet most land use models emphasise economic and biophysical drivers, while socio-psychological factors influencing land managers' decisions remain underrepresented despite increasing evidence that they shape land management choices. To address this gap, we develop a generic behavioural extension for agent-based land use models, guided by the Theory of Planned Behaviour as an overarching conceptual framework. The extension integrates environmental attitudes, descriptive social norms and behavioural inertia into land managers' decisions on land management intensity. To demonstrate applicability, the extension is coupled to an existing land use modelling framework and explored in stylised settings to isolate behavioural mechanisms. Results show that socio-psychological drivers can significantly alter land management intensity shares, landscape configuration, and ecosystem service provision. Nonlinear feedbacks between these drivers, spatial resource heterogeneity, and ecosystem service demand lead to emergent dynamics that are sometimes counter-intuitive and can diverge from the agent-level decision rules. Increasing the influence of social norms generates spatial clustering and higher landscape connectivity, while feedbacks between behavioural factors can lead to path dependence, lock-in effects, and the emergence of multiple stable regimes with sharp transitions. The proposed framework demonstrates how even low levels of behavioural diversity and social interactions can reshape system-level land use outcomes and provides a reusable modelling component for incorporating socio-psychological processes into land use simulations. The approach can be integrated into other agent-based land use models and parameterised empirically in future work.
In recent years, predicting Big Five personality traits from multimodal data has received significant attention in artificial intelligence (AI). However, existing computational models often fail to achieve satisfactory performance. Psychological research has shown a strong correlation between pose and personality traits, yet previous research has largely ignored pose data in computational models. To address this gap, we develop a novel multimodal dataset that incorporates full-body pose data. The dataset includes video recordings of 287 participants completing a virtual interview with 36 questions, along with self-reported Big Five personality scores as labels. To effectively utilize this multimodal data, we introduce the Psychology-Inspired Network (PINet), which consists of three key modules: Multimodal Feature Awareness (MFA), Multimodal Feature Interaction (MFI), and Psychology-Informed Modality Correlation Loss (PIMC Loss). The MFA module leverages the Vision Mamba Block to capture comprehensive visual features related to personality, while the MFI module efficiently fuses the multimodal features. The PIMC Loss, grounded in psychological theory, guides the model to emphasize different modalities for different personality dimensions. Experimental results show that the PINet outperforms several state-of-the-art baseline models. Furthermore, the three modules of PINet contribute almost equally to the model's overall performance. Incorporating pose data significantly enhances the model's performance, with the pose modality ranking mid-level in importance among the five modalities. These findings address the existing gap in personality-related datasets that lack full-body pose data and provide a new approach for improving the accuracy of personality prediction models, highlighting the importance of integrating psychological insights into AI frameworks.
Arbitrarily Applicable Relational Responding (AARR) is a cornerstone of human language and reasoning, referring to the learned ability to relate symbols in flexible, context-dependent ways. In this paper, we present a novel theoretical approach for modeling AARR within an artificial intelligence framework using the Non-Axiomatic Reasoning System (NARS). NARS is an adaptive reasoning system designed for learning under uncertainty. By integrating principles from Relational Frame Theory - the behavioral psychology account of AARR - with the reasoning mechanisms of NARS, we conceptually demonstrate how key properties of AARR (mutual entailment, combinatorial entailment, and transformation of stimulus functions) can emerge from the inference rules and memory structures of NARS. Two theoretical experiments illustrate this approach: one modeling stimulus equivalence and transfer of function, and another modeling complex relational networks involving opposition frames. In both cases, the system logically demonstrates the derivation of untrained relations and context-sensitive transformations of stimulus significance, mirroring established human cognitive phenomena. These results suggest that AARR - long considered uniquely human - can be conceptually captured by suitably designed AI systems, highlighting the value of integrating behavioral science insights into artificial general intelligence (AGI) research.
Saskia Denecke, Felix Strakeljahn, Antonia Bott
et al.
Abstract Aetiological models of delusions propose a broad range of predictors. The extent to which these predictors explain variance in persecutory beliefs across the continuum requires systematic investigation. As part of a previous review, 51 aetiological models of delusions were identified in a systematic literature search using PubMed, Web of Science, and Science Direct databases. Omitting repetitions, 66 unique postulated predictors of delusions and persecutory delusions were extracted from these models, of which 55 met our inclusion criteria and were assessed in a cross-sectional online sample stratified by delusion severity (N = 336) using self-report and behavioural measures. Utilising machine learning (i.e., random forests with nested cross-validation), we investigated the extent to which the model-based predictors explain self-reported persecutory beliefs, identified the most relevant predictors, and investigated their specificity in explaining persecutory beliefs as opposed to delusional beliefs or psychopathological symptoms in general. The machine learning model explained 31% of the variance in persecutory beliefs, 47% of delusions in general, and 77% of general psychopathology. The ten predictors with the most influence on predicting persecutory beliefs included negative beliefs about mistrust, cognitive fusion, ostracism, threat anticipation, generalised negative other beliefs, trust, aberrant salience, hallucinations, stress, and emotion regulation difficulties. The limited explanatory power of the proposed predictors raises questions about the validity of existing models and suggests that crucial predictors specific to persecutory delusions may be missing. Our findings highlight the importance of investigating, refining, and cross-validating theoretical aetiological models to improve our understanding of the aetiology of delusions.
M. Graça Pereira, Martim Santos, Renata Magalhães
et al.
University students are at increased risk of developing burnout and psychological distress from high academic workloads and performance expectations. The purpose of this study is to analyze the relationship between psychological and lifestyle variables and academic burnout, as well as to identify burnout risk profiles in psychology students. This study used a cross-sectional design and included 274 Portuguese psychology students, the majority being undergraduates (72.6%). Participants were assessed on psychological well-being, psychological distress, difficulties in emotional regulation, type of diet, physical activity, sleep quality, and burnout. The results showed that psychological distress, difficulties in emotional regulation, and sleep quality were positively associated with burnout, while psychological well-being was negatively associated. Using machine learning algorithms, two distinct profiles were found: “Burnout Risk” and “No Risk”. A total of 62 participants were identified as belonging to the burnout risk profile, showing higher levels of distress, emotional regulation difficulties, poor psychological well-being and sleep quality, pro-inflammatory diet, and less physical activity. The accuracy of the three machine learning models—Random Forest, XGBoost, and Support Vector Machine—was 95.06%, 93.82%, and 97.53%, respectively. These results suggest the importance of health promotion within university settings, together with mental health strategies focused on adaptive psychological functioning, to prevent the risk of burnout.
19. yüzyılda Osmanlı Devleti, toplumsal ve siyasal düzenin istikrarını temin etmek amacıyla Ehl-i Sünnet inancı dışındaki çeşitli dini ve mezhebi gruplara yönelik politikalar geliştirmiştir. Bu çerçevede, Kızılbaş topluluklarına da belirli düzenlemeler ve tedbirler uygulanmıştır. Kızılbaş gruplar, dinî ayrışma ve İran etkisine bağlı bir siyasi tehdit olarak değerlendirilmiş ve özellikle II. Abdülhamid döneminde (1876-1909), Kızılbaşların inanç pratiklerini Ehl-i Sünnet inançlarına uygun hale getirmek amacıyla “tashih-i itikad” politikaları uygulanmıştır. Ancak bu politikalar, yerel direnişler ve devletin uygulama yetersizlikleri nedeniyle beklenen sonuçları verememiştir. Osmanlının mezhepsel farklılıklarla baş etme çabaları, dinî birlikteliği sağlama ve merkezi otoriteyi güçlendirme hedefleri çerçevesinde şekillense de sahadaki uygulamalar bu hedeflerle uyumlu sonuçlar doğurmamıştır. Bu araştırma, II. Abdülhamid döneminde Kızılbaşlara yönelik tashih-i itikad politikalarının ideolojik gerekçelerini, tarihsel arka planını ve toplumsal yansımalarını incelemeyi amaçlamaktadır. Bu bağlamda, dönemin dinî ve siyasal kaygıları ile mezhepsel farklılıklar arasındaki ilişki, tarihî belgeler üzerinden analiz edilmiştir. Osmanlı Devleti’nin farklı inanç gruplarına yönelik din-siyaset politikalarının uygulanış biçimleri ve bu politikaların toplumsal etkileri ele alınarak, mezhepsel farklılıkların yönetimi konusundaki tarihsel deneyimler ortaya konmuştur. Osmanlı Devleti’nin farklı inanç gruplarıyla ilişkilerini anlamak açısından özgün bir katkı sunulmuştur. Kızılbaş gruplara yönelik politikaların mezhepsel ayrışmayı nasıl etkilediği ve dönemin toplumsal yapısı üzerindeki yansımaları, arşiv belgeleri ve tarihsel veriler ışığında değerlendirilmiştir. Osmanlı tarih yazımı İslam Mezhepleri Tarihi perspektifinden ele alınarak, bu döneme dair dinî ve siyasal politikalara vurgu yapılmıştır. Osmanlı arşiv belgeleri, resmî yazışmalar ve İslam Mezhepleri Tarihi ve diğer ilim dallarına ait araştırmalar temel alınarak değerlendirmeler yapılmıştır. Tarihî belge analizi ve nitel yöntemlerin bir araya getirilmesiyle, Kızılbaş gruplara yönelik tashih-i itikad politikaları, tarihî ve sosyolojik bağlamda değerlendirilmiştir. İslam Mezhepleri Tarihi yazıcılığının ilkeleri doğrultusunda, Kızılbaşların inanç yapıları, toplumsal konumları ve Osmanlı bürokrasisinin bu gruplara yaklaşımı analiz edilmiştir. Osmanlı Devleti, Kızılbaşlara yönelik politikalarında Ehl-i Sünnet inancını tahkim etmeyi amaçlamıştır. Eğitim reformları, cami-mescit inşası, din görevlilerinin istihdamı ve dinî risalelerin hazırlanması gibi adımlar bu hedeflerin gerçekleştirilmesi için temel araçlar olarak öne çıkmıştır. Ancak, dedelerin toplumsal etkisi, yerel direnç ve devletin uygulama yetersizlikleri bu politikaların başarısını sınırlamıştır. Eğitim kurumlarının yetersizliği, imam ve müderris eksikliği gibi yapısal sorunlar, tashih-i itikad çabalarının etkisini azaltmıştır. Ayrıca, Kızılbaş grupların tarihî, kültürel ve coğrafi farklılıklarının dikkate alınmaması, bu politikaların toplumsal kabul görmesini zorlaştırmıştır. Elde edilen bulgular, tashih-i itikad politikalarının yalnızca dinî bir çaba değil, aynı zamanda İran etkisi, misyoner faaliyetler ve mezhepsel ayrışma gibi siyasi tehditlere karşı bir strateji olarak uygulandığını göstermektedir. Bu politikaların sert müdahalelerle şekillendirilmesi, mezhep gerilimlerini azaltmak yerine artırmış, Kızılbaşların devlete karşı daha dirençli bir duruş sergilemesine yol açmıştır. Eğitimsizlik ve dinî bilgi eksikliği gibi nedenlerle şekillenen toplumsal sorunlar, mezhepsel ayrışmanın derinleşmesine katkı sağlamıştır. Sonuç olarak, Osmanlı Devleti’nin 19. yüzyıldaki tashih-i itikad politikaları, mezhepsel farklılıkları kontrol altına almayı ve toplumsal düzeni korumayı amaçlamış; ancak yerel dinamiklerin ve toplumsal gerçekliklerin göz ardı edilmesi, bu çabaların başarısını sınırlamıştır.
Given the rapid advancement of large-scale language models, artificial intelligence (AI) models, like ChatGPT, are playing an increasingly prominent role in human society. However, to ensure that artificial intelligence models benefit human society, we must first fully understand the similarities and differences between the human-like characteristics exhibited by artificial intelligence models and real humans, as well as the cultural stereotypes and biases that artificial intelligence models may exhibit in the process of interacting with humans. This study first measured ChatGPT in 84 dimensions of psychological characteristics, revealing differences between ChatGPT and human norms in most dimensions as well as in high-dimensional psychological representations. Additionally, through the measurement of ChatGPT in 13 dimensions of cultural values, it was revealed that ChatGPT's cultural value patterns are dissimilar to those of various countries/regions worldwide. Finally, an analysis of ChatGPT's performance in eight decision-making tasks involving interactions with humans from different countries/regions revealed that ChatGPT exhibits clear cultural stereotypes in most decision-making tasks and shows significant cultural bias in third-party punishment and ultimatum games. The findings indicate that, compared to humans, ChatGPT exhibits a distinct psychological profile and cultural value orientation, and it also shows cultural biases and stereotypes in interpersonal decision-making. Future research endeavors should emphasize enhanced technical oversight and augmented transparency in the database and algorithmic training procedures to foster more efficient cross-cultural communication and mitigate social disparities.
Understanding and predicting athletes' mental states is crucial for optimizing sports performance. This study introduces a hybrid BERT-XGBoost model to analyze psychological factors such as emotions, anxiety, and stress, and predict their impact on performance. By combining BERT's bidirectional contextual learning with XGBoost's classification efficiency, the model achieves high accuracy (94%) in identifying psychological patterns from both structured and unstructured data, including self-reports and observational data tagged with categories like emotional balance and stress. The model also incorporates real-time monitoring and feedback mechanisms to provide personalized interventions based on athletes' psychological states. Designed to engage athletes intuitively, the system adapts its feedback dynamically to promote emotional well-being and performance enhancement. By analyzing emotional trajectories in real-time offers empathetic, proactive interactions. This approach optimizes performance outcomes and ensures continuous monitoring of mental health, improving human-computer interaction and providing an adaptive, user-centered model for psychological support in sports.
The integration of artificial intelligence across multiple domains has emphasized the importance of replicating human-like cognitive processes in AI. By incorporating emotional intelligence into AI agents, their emotional stability can be evaluated to enhance their resilience and dependability in critical decision-making tasks. In this work, we develop a methodology for modeling psychological disorders using Reinforcement Learning (RL) agents. We utilized Appraisal theory to train RL agents in a dynamic grid world environment with an Appraisal-Guided Proximal Policy Optimization (AG-PPO) algorithm. Additionally, we investigated numerous reward-shaping strategies to simulate psychological disorders and regulate the behavior of the agents. A comparison of various configurations of the modified PPO algorithm identified variants that simulate Anxiety disorder and Obsessive-Compulsive Disorder (OCD)-like behavior in agents. Furthermore, we compared standard PPO with AG-PPO and its configurations, highlighting the performance improvement in terms of generalization capabilities. Finally, we conducted an analysis of the agents' behavioral patterns in complex test environments to evaluate the associated symptoms corresponding to the psychological disorders. Overall, our work showcases the benefits of the appraisal-guided PPO algorithm over the standard PPO algorithm and the potential to simulate psychological disorders in a controlled artificial environment and evaluate them on RL agents.
In this article, we put into action a new conceptualization of self-talk using the methods of analysis developed in a previous article. To this end, we analyze the cue word “En Avant” (forward) that William, an Olympic mogul-skiing champion, says to himself when he does a double twisting backflip on the first jump of a mogul run. The description of the different properties of the cue word "Forward" led us to propose three effects: 1) the cue word brings out a language function within a motor activity, 2) the cue word supports the deployment of the different possible actions and carry out the choice of the right action, 3) the cue word allows live feedback on the action. The discussion in this article shows how the effects of the cue word do not exist per se, they exist through the dialectical relationship between the athlete and the word, and are the product of a development.
Abstract Background Patients undergoing assisted reproductive technology (ART) experience significant psychological distress due to infertility, with depression and anxiety being the most common manifestations. This study investigates the influence of family support and self-efficacy on the mental health of patients undergoing in vitro fertilization-embryo transfer (IVF-ET). The aim is to assess the direct and indirect effects of family function and self-efficacy on depression and anxiety in IVF-ET patients through pathway analyses, thereby providing novel insights for improving patients’ psychological well-being. Methods A cross-sectional study was conducted from March to July 2021, employing convenience sampling to recruit 291 participants from a tertiary care hospital’s reproductive medicine center. Data were collected using the Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS), General Self-Efficacy Scale (GSES), Family APGAR index (APGAR), and a Demographic Characteristics Form. Descriptive analysis, Pearson correlation analysis, and multiple linear regression analysis were performed. A Structural Equation Model (SEM) was utilized for pathway analysis to evaluate the direct and indirect influences of family function and self-efficacy on anxiety and depression. Results The scores for the SAS and SDS were 46.15 ± 7.35 and 51.71 ± 8.65, respectively. Multiple regression analysis indicated that family function, self-efficacy, and economic status significantly predicted anxiety and depression. Path analysis revealed that economic status directly (β=-0.447, -0.232) and indirectly (β=-0.066, -0.068) affected anxiety and depression, while family function both directly and indirectly affected depression (β=-0.323, -0.104), directly affected anxiety (β=-0.351), and self-efficacy directly influenced only depression (β=-0.509). Conclusion The findings underscore the pivotal role of a supportive family environment and self-efficacy in alleviating anxiety and depression among IVF-ET patients. The mediating role of family function between economic status and mental health highlights the importance of integrated support systems. Enhancing family function and self-efficacy as part of comprehensive care for individuals undergoing ART is crucial for promoting patient well-being.
Work in AI ethics and fairness has made much progress in regulating LLMs to reflect certain values, such as fairness, truth, and diversity. However, it has taken the problem of how LLMs might 'mean' anything at all for granted. Without addressing this, it is not clear what imbuing LLMs with such values even means. In response, we provide a general theory of meaning that extends beyond humans. We use this theory to explicate the precise nature of LLMs as meaning-agents. We suggest that the LLM, by virtue of its position as a meaning-agent, already grasps the constructions of human society (e.g. morality, gender, and race) in concept. Consequently, under certain ethical frameworks, currently popular methods for model alignment are limited at best and counterproductive at worst. Moreover, unaligned models may help us better develop our moral and social philosophy.
The prevalent use of Large Language Models (LLMs) has necessitated studying their mental models, yielding noteworthy theoretical and practical implications. Current research has demonstrated that state-of-the-art LLMs, such as ChatGPT, exhibit certain theory of mind capabilities and possess relatively stable Big Five and/or MBTI personality traits. In addition, cognitive process features form an essential component of these mental models. Research in cultural psychology indicated significant differences in the cognitive processes of Eastern and Western people when processing information and making judgments. While Westerners predominantly exhibit analytical thinking that isolates things from their environment to analyze their nature independently, Easterners often showcase holistic thinking, emphasizing relationships and adopting a global viewpoint. In our research, we probed the cultural cognitive traits of ChatGPT. We employed two scales that directly measure the cognitive process: the Analysis-Holism Scale (AHS) and the Triadic Categorization Task (TCT). Additionally, we used two scales that investigate the value differences shaped by cultural thinking: the Dialectical Self Scale (DSS) and the Self-construal Scale (SCS). In cognitive process tests (AHS/TCT), ChatGPT consistently tends towards Eastern holistic thinking, but regarding value judgments (DSS/SCS), ChatGPT does not significantly lean towards the East or the West. We suggest that the result could be attributed to both the training paradigm and the training data in LLM development. We discuss the potential value of this finding for AI research and directions for future research.
Alessandro Del Ponte, Audrey De Dominicis, Paolo Canofari
Background: Here we investigate whether releasing COVID-19 vaccines in limited quantities and at limited times boosted Italy's vaccination campaign in 2021. This strategy exploits insights from psychology and consumer marketing. Methods: We built an original dataset covering 200 days of vaccination data in Italy, including 'open day' events. Open-day events (in short: open days) are instances where COVID-19 vaccines were released in limited quantities and only for a specific day at a specified location (usually, a large pavilion or a public building). Our dependent variables are the number of total and first doses administered in proportion to the eligible population. Our key independent variable is the presence of open-day events in a given region on a specific day. We analyzed the data using regression with fixed effects for time and region. The analysis was robust to alternative model specifications. Findings: We find that when an open day event was organized, in proportion to the eligible population, there was an average 0.39-0.44 percentage point increase in total doses administered and a 0.30-0.33 percentage point increase in first doses administered. These figures correspond to an average increase of 10,455-11,796 in total doses administered and 8,043-8,847 in the first doses administered. Interpretation: Releasing vaccines in limited quantities and at limited times by organizing open-day events was associated with an increase in COVID-19 vaccinations in most Italian regions. These results call for wider adoption of vaccination strategies based on the limited release of vaccines for other infectious diseases or future pandemics.
Recent research has focused on examining Large Language Models' (LLMs) characteristics from a psychological standpoint, acknowledging the necessity of understanding their behavioral characteristics. The administration of personality tests to LLMs has emerged as a noteworthy area in this context. However, the suitability of employing psychological scales, initially devised for humans, on LLMs is a matter of ongoing debate. Our study aims to determine the reliability of applying personality assessments to LLMs, explicitly investigating whether LLMs demonstrate consistent personality traits. Analysis of 2,500 settings per model, including GPT-3.5, GPT-4, Gemini-Pro, and LLaMA-3.1, reveals that various LLMs show consistency in responses to the Big Five Inventory, indicating a satisfactory level of reliability. Furthermore, our research explores the potential of GPT-3.5 to emulate diverse personalities and represent various groups-a capability increasingly sought after in social sciences for substituting human participants with LLMs to reduce costs. Our findings reveal that LLMs have the potential to represent different personalities with specific prompt instructions.
As the burden of misconduct in medical research is increasingly recognised, questions have been raised about how best to address this problem. Whilst there are existing mechanisms for the investigation and management of misconduct in medical literature, they are inadequate to deal with the magnitude of the problem. Journal editors and publishers play an essential role in protecting the veracity of the medical literature. Whilst ethical guidance for journal editors and publishers is important, it is not as readily enforceable as legal obligations might be. This article questions the legal obligations that might exist for journal editors and publishing companies with respect to ensuring the veracity of the published literature. Ultimately, there is no enforceable legal obligation in Australia, the United Kingdom, or the United States. In light of this, more robust mechanisms are needed to deliver greater confidence and transparency in the investigative process, the management of concerns or findings of misconduct and the need to cleanse the literature. We show that the law disincentivises journals and publishers from ensuring truth in their publications. There are harmful consequences for medical care and public confidence in the medical profession and health care system when the foundations of medical science are questionable.
Scott Cheng-Hsin Yang, Tomas Folke, Patrick Shafto
The goal of explainable Artificial Intelligence (XAI) is to generate human-interpretable explanations, but there are no computationally precise theories of how humans interpret AI generated explanations. The lack of theory means that validation of XAI must be done empirically, on a case-by-case basis, which prevents systematic theory-building in XAI. We propose a psychological theory of how humans draw conclusions from saliency maps, the most common form of XAI explanation, which for the first time allows for precise prediction of explainee inference conditioned on explanation. Our theory posits that absent explanation humans expect the AI to make similar decisions to themselves, and that they interpret an explanation by comparison to the explanations they themselves would give. Comparison is formalized via Shepard's universal law of generalization in a similarity space, a classic theory from cognitive science. A pre-registered user study on AI image classifications with saliency map explanations demonstrate that our theory quantitatively matches participants' predictions of the AI.
According to the conflict monitoring hypothesis, conflict monitoring and inhibitory control in cognitive control mainly cause activity in the anterior cingulate cortex (ACC) and control-related prefrontal cortex (PFC) in many cognitive tasks. However, the role of brain regions in the default mode network (DMN) in cognitive control during category induction tasks is unclear. To test the role of the ACC, PFC, and subregions of the DMN elicited by cognitive control during category induction, a modified category induction task was performed using simultaneous fMRI scanning. The results showed that the left middle frontal gyrus (BA9) and bilateral dorsal ACC/medial frontal gyrus (BA8/32) were sensitive to whether conflict information (with/without) appears, but not to the level of conflict. In addition, the bilateral ventral ACC (BA32), especially the right vACC, a part of the DMN, showed significant deactivation with an increase in cognitive effort depending on working memory. These findings not only offer further evidence for the important role of the dorsolateral PFC and dorsal ACC in cognitive control during categorization but also support the functional distinction of the dorsal/ventral ACC in the category induction task.
The aim of the article was to establish the nature of the relationship between the types of resourcefulness of a personality based on empirical data.
Methods. In the empirical study, the psychological survey methods were used, as well as mathematical and statistical methods of correlation, classification, discriminant, multifactorial, significative, comparative analysis. The empirical study is implemented in the Nelson's model, which makes it possible to describe the phenomenon under study under given conditions.
Research results. The indicators of comparability based on the results of the multivariate test of signification and comparative analysis using the Schef- fe's test justified are: value of oneself, freedom, responsibility. It should be noted that the empirical argumentation of hardiness as an indicator of comparability and a vector for positioning resource types is weak. Personality resourcefulness is different from other types resourcefulness in terms of the smallest share of representation in the volume of generalized resourcefulness and in the secondary importance of semantic significance. Resource richness is the least, and psychological resource is the most operationalized of the type from resourcefulness. Psychological capital is the most clearly expressed type of resourcefulness.
Conclusions. In the manifestation of the types of resourcefulness of the personality, the experience of overcoming difficult life situations is revealed, at the same time, the main thing is the experience of independent choice according to conscience, the freedom to take advantage of the opportunity to choose and responsibility for its consequences. Therefore, we conclude that the positioning of the types of psychological resourcefulness in the coordinates of "against-and- owing to" is carried out, to a large extent, owing to the individual's reliance on the ethical choice. Empirical comparison of types of resourcefulness according to reasonable indicators allows us to determine the nature of their relationship as a constellation - an ordered matrix of interrelated valuable issues. The applied significance of the positioning of types of resourcefulness lies in the opening possibility of predicting a change in the type of resourcefulness of a personality when choosing freedom and responsibility, as well as maintaining of him internal dialogue with conscience.