Decision Mechanism Research Articles

Individualistic reward-seeking strategies that predict response to nicotine emerge among isogenic male mice living in a micro-society.

Individual animals differ in their traits and preferences, which shape their social interactions, survival, and susceptibility to disease, including addiction. Nicotine use is highly heterogenous and has been linked to the expression of personality traits. Although these relationships are well documented, we have limited understanding of the neurophysiological mechanisms that give rise to distinct behavioral profiles and their connection to nicotine susceptibility. To address this question, we conducted a study using a semi-natural and social environment called "Souris-City" to observe the long-term behavior of individual male mice. Souris-City provided both a communal living area and a separate test area where mice engaged in a reward-seeking task isolated from their peers. Mice developed individualistic reward-seeking strategies when choosing between water and sucrose in the test compartment, which, in turn, predicted how they adapted to the introduction of nicotine as a reinforcer. Moreover, the profiles mice developed while isolated in the test area correlated with their behavior within the social environment, linking decision-making strategies to the expression of behavioral traits. Neurophysiological markers of adaptability within the dopamine system were apparent upon nicotine challenge and were associated with specific profiles. Our findings suggest that environmental adaptations influence behavioral traits and sensitivity to nicotine by acting on dopaminergic reactivity in the face of nicotine exposure, potentially contributing to addiction susceptibility. These results further emphasize the importance of understanding interindividual variability in behavior to gain insight into the mechanisms of decision-making and addiction.

Decision-making and attention deficit hyperactivity disorder: neuroeconomic perspective

The decision-making process involves various cognitive procedures influenced by the interplay between cognition, motivation, and attention, forming a complex neural framework. Attention is a fundamental cognitive element within decision-making mechanisms, and one of the conditions affecting the attentional system is attention deficit hyperactivity disorder (ADHD). Decision-making impairments in ADHD have significant economic consequences, necessitating effective policies and interventions to address this critical issue. Research from computational models and neuroscience suggests how cognitive functions’ workings and problems affect decision-making and provide insights into the neural implications of decision-making. This article explores the intersection of decision-making, ADHD, and neuroeconomics, highlighting research gaps, potential contributions, and implications for future policies.

Bystanders or active participants? Mobilising meaningful participation in River Basin management: lessons from the Gauteng Province, South Africa

ABSTRACT This paper explores the concept of meaningful participation in river basin management, using the case study of the Hennops River in Gauteng Province, South Africa. Experiences of different stakeholders and communities regarding their involvement in the management of the Hennops River basin are analysed following engagement through interviews, workshops and clean-up initiatives. Several barriers and enablers for meaningful participation, such as lack of awareness, capacity, coordination, trust and incentives, are identified. Some recommendations are proposed for improving stakeholder and community participation, based on the lessons learned from the case study. This includes the formulation of a social fund to support small community-based projects such as clean-up campaigns. Projects should be reflective, keeping the community engaged and active. The paper argues that meaningful participation requires not only formal mechanisms of engagement and decision-making but also informal and practical actions that empower stakeholders and communities to take ownership and responsibility for their water resources and environment. For meaningful participation to take place, more needs to be done to shift communities away from a ‘bystander’ position to a more active role. A new approach to community engagement is therefore necessary to support stakeholder-driven initiatives and the raising of awareness in poorer communities.

Machine learning-based prediction of DNA G-quadruplex folding topology with G4ShapePredictor

Deoxyribonucleic acid (DNA) is able to form non-canonical four-stranded helical structures with diverse folding patterns known as G-quadruplexes (G4s). G4 topologies are classified based on their relative strand orientation following the 5’ to 3’ phosphate backbone polarity. Broadly, G4 topologies are either parallel (4+0), antiparallel (2+2), or hybrid (3+1). G4s play crucial roles in biological processes such as DNA repair, DNA replication, transcription and have thus emerged as biological targets in drug design. While computational models have been developed to predict G4 formation, there is currently no existing model capable of predicting G4 folding topology based on its nucleic acid sequence. Therefore, we introduce G4ShapePredictor (G4SP), an application featuring a collection of multi-classification machine learning models that are trained on a custom G4 dataset combining entries from existing literature and in-house circular dichroism experiments. G4ShapePredictor is designed to accurately predict G4 folding topologies in potassium (K+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {K}^+$$\\end{document}) buffer based on its primary sequence and is able to incorporate a threshold optimization strategy allowing users to maximise precision. Furthermore, we have identified three topological sequence motifs that suggest specific G4 folding topologies of (4+0), (2+2) or (3+1) when utilising the decision-making mechanisms of G4ShapePredictor.

Are we preparing healthy & responsible adolescents? Exploratory qualitative study to understand the health and social issues of adolescent living in Karachi, Pakistan.

This study aimed to understand the lives of adolescents living in squatter settlements of Karachi, Pakistan regarding their assigned roles and responsibilities, health and social issues, their decision-making process, and mechanism and channels of information. An exploratory descriptive qualitative study design was employed. Purposive sampling techniques were used to carry out Focus Group Discussions (FGDs) with adolescents (n = 10, Participants = 190), adolescents' parents (n = 10, Participants = 180), and In-Depth Interviews of with key adolescent stakeholders (n = 20). Adolescent stakeholder mapping was conducted for enrolling participants. The data were analysed thematically using deductive and inductive approaches. Based on gender norms parents assign specific roles and responsibilities to adolescent girls and boys. Due to societal norms, communication gap exists between adolescents and their parents. The most popular information channels are social media platforms and friends. Adolescents reported being subjected to a variety of physical, sexual, mental, social, and environmental pressures. Lack of guidance from parents, inadequate knowledge and skills to deal with physical, sexual, mental, social and environmental hazards, and misuse of social media lead to risky decisions, injuries, and social instability. This study underlines the urgent need for targeted interventions for addressing gender issues and improving adolescents' decision-making and life skills. We recommend Behavior Change Communication interventions to dismantle gender stereotypes and support a balanced domestic environment for children's education and well-being, awareness raising among parents, teachers, and healthcare providers about adolescent risks emphasizing their role in youth guidance, and advocacy for youth-led forums to co-create educational content engaging parents, educators, and health professionals focusing on life skills. These strategies would turn demographic transitions into productive dividends.

Modeling and simulation of Fuzzy-rule based WBAN using multi-level fuzzy colored petri-nets and reinforcement learning

By developing IoTs (Internet of Things), a new concept is arising in WBANs (Wireless Body Area Networks), called IoBs (Internet of Bodies). WBANs are the safety-critical devices that use rule-based knowledge with fuzzy variables for monitoring patients. There are two issues with the knowledgebase: (1) appropriate knowledge representation and inference and (2) handling the situations for which there is no rule in the knowledgebase. In this paper, for the first issue we present MFCPN (Multi-level Fuzzy Colored Petri-nets) and for the second one we present a RL (Reinforcement Learning) mechanism for decision making based on the past device’s feedback to the environment. Through a few scenarios and two datasets, the proposed approach has been applied to pacemakers to show the approach's effectiveness. The comparison between decision making by our approach and two real datasets indicates the proposed approach has 79.3 % accuracy.

Breeding zebra finches prioritize reproductive bout over self-maintenance under food restriction.

Reproduction requires high amounts of energy, and challenging environments during breeding can force parents to prioritize their current reproductive bout over self-maintenance or vice versa. However, little is known about how common stressors, such as food restriction, can influence these trade-offs during breeding, and the physiological mechanisms for these trade-off decisions. In this study, adult zebra finches (Taeniopygia castanotis) were subjected to a control diet (ad libitum) or a 40% food restriction while raising nestlings and fledglings, and we measured body mass, furculum fat, plasma corticosterone (CORT) and blood glucose levels of the parents at the time of pairing, when their offspring fledged, and when their offspring reached nutritional independence. Offspring were also measured in body mass and growth rate from hatching until the end of the treatment period. Food-restricted parents had lower body mass when their offspring fledged and reached nutritional independence and higher baseline CORT at fledging compared to controls. Offspring did not differ in body mass or growth rate between treatment groups. However, there was no effect of food restriction on parents' furculum fat, baseline glucose, the adrenocortical response, or the glucose response. Furthermore, path analysis results suggest that alterations in baseline glucose is the primary driver of changes in body mass in parents and offspring brood mass. Taken together, these results suggest that food restriction during chick rearing in a short-lived passerine drives parents to prioritize their current reproductive bout over self-maintenance, and glucose could potentially be a mechanism for diverting energy toward parental effort.

Research on the discrimination and classification of multi-modal fault bearing based on multi-attribute decision-making mechanism

In order to solve the problem of low fault diagnosis rate of friction torque in upright placement, this paper proposes a fault diagnosis method based on multi-attribute decision-making mechanism to accurately evaluate the fault damage degree of bearing friction torque test bench. We detect defects by utilizing non-destructive, non-contact infrared thermal imaging technology to perform data acquisition and testing in six situations, including non-destructive, damaged inner ring, damaged outer ring, marble-defect, lack of lubrication, and damaged inner and outer ring bearings. Then, the infrared data of different bearings are transformed in a standardized form, and the decision data is mapped to the Pythagorean fuzzy information space. The correlation between the decision data are considering, the PA operator and the BM operator are used for data processing to achieve the accurate distinction of different faulty bearings. Finally, qualitative and quantitative methods are used to analyze the decision results, and the effectiveness of the method is explained. The variation of the overall singular value of each object in the decision result of the proposed method is less than 0.002, while the data fluctuation of WA, WG, HM and DHM related decision methods is 0.005, and there is a singular value greater than 0.02. In addition, sensitivity analysis provides a feasible scheme for parameter optimization and optimal parameter determination.

Research on an identification model for mine water inrush sources based on the HBA-CatBoost algorithm

Accurate and efficient identification of water inrush sources, as one of the three critical elements of mine water hazards, is crucial for mine water management. To identify the sources of mine water inrush effectively, a model named HBA-CatBoost is introduced. This model is established on the hybrid bat algorithm (HBA)-optimized category feature gradient boosting tree (CatBoost), and the shapley additive explanation (SHAP) method is employed to elucidate the model’s decision-making process. Given the prevalent occurrence of water hazards in coal seam roofs and floors in the northern Guizhou coalfield, coupled with the challenges in pinpointing water inrush sources in mines, the HBA-CatBoost model is tested at the Longfeng Coal Mine in northern Guizhou to validate its practicality. Comparative analysis with the HBA-RF, HBA-XGBoost, CatBoost, RF, and XGBoost models demonstrates that the hybrid bat algorithm significantly enhances the classification performance of the CatBoost model, resulting in improved convergence speed and classification accuracy. The HBA-CatBoost model outperforms the aforementioned models in terms of classification effectiveness, achieving accuracy, recall, precision, and F1 scores of 96.43%, 97.22%, 96.43%, and 96.61%, respectively. The SHAP method elucidates the decision mechanism of the optimal HBA-CatBoost model, highlighting the significance of sample features and bolstering the model’s credibility. These outcomes underscore the superior performance of the HBA-CatBoost model and its potential for effectively identifying water inrush sources in mines.

Industrial Process Fault Detection Based on IGA‐Combinatorial Model Decision Mechanism

ABSTRACTTo address the challenges of extracting features from complex industrial process data, the reliance of numerous fault detection methodologies on presupposed data distribution types, and the limited generalization capacity of fault detection, this manuscript introduces a sophisticated algorithm for industrial process fault detection. This algorithm harnesses the information gain adaptive (IGA) technique for feature selection and a synergistic model decision mechanism. Initially, the process involves the computation of information gain via decision trees, coupled with the determination of the value through cross‐validation. This strategy enables the adaptive selection of features, thereby facilitating data dimensionality reduction and effective feature extraction. The subsequent phase introduces a ternary statistical measure monitoring group for the detection of linear faults, while autoencoders and one‐class SVM methodologies are applied for the monitoring of nonlinear faults. The culmination of this approach is the development of an innovative weighted decision mechanism, designed to amalgamate the findings from both linear and nonlinear detection avenues, yielding more dependable detection results. The validation of this algorithm employs datasets from the water chillers process and Tennessee Eastman (TE) process, demonstrating the IGA‐combined model's superior performance over isolated linear or nonlinear detection algorithms in terms of detection accuracy and robustness. Notably, the efficacy of this method is not contingent upon specific assumptions regarding data distribution, rendering it a versatile and efficacious tool for the fault detection in industrial processes.

Neural and Computational Mechanisms of Motivation and Decision-making.

Motivation is often thought to enhance adaptive decision-making by biasing actions toward rewards and away from punishment. Emerging evidence, however, points to a more nuanced view whereby motivation can both enhance and impair different aspects of decision-making. Model-based approaches have gained prominence over the past decade for developing more precise mechanistic explanations for how incentives impact goal-directed behavior. In this Special Focus, we highlight three studies that demonstrate how computational frameworks help decompose decision processes into constituent cognitive components, as well as formalize when and how motivational factors (e.g., monetary rewards) influence specific cognitive processes, decision-making strategies, and self-report measures. Finally, I conclude with a provocative suggestion based on recent advances in the field: that organisms do not merely seek to maximize the expected value of extrinsic incentives. Instead, they may be optimizing decision-making to achieve a desired internal state (e.g., homeostasis, effort, affect). Future investigation into such internal processes will be a fruitful endeavor for unlocking the cognitive, computational, and neural mechanisms of motivated decision-making.

Transformation of international security mechanisms in the context of the russian-ukrainian war

The article provides a comprehensive analysis of the transformations in international security mechanisms in the context of the Russia-Ukraine war, ongoing since 2014, with a particular focus on the activities of NATO and the United Nations. The author aims to examine the crisis in the effectiveness of traditional security structures, assess their ability to adapt to new geopolitical realities, and identify the need for the creation of new forms of international security. The research methodology is based on the analysis and synthesis of scientific sources, including works dedicated to the activities of international security organizations, as well as the application of a systematic approach to evaluate the effectiveness of existing security mechanisms. The article examines NATO and the UN's decision-making mechanisms and responses to the Russia-Ukraine war, analyzing their capacity to adequately address the new challenges arising from the armed conflict. The author employs comparative analysis methods to study the changes in the activities of these organizations and analyzes expert assessments to identify key changes in their operations. The study concludes that the Russia-Ukraine war has acted as a catalyst for changes in international security mechanisms, revealing the weaknesses and shortcomings of traditional approaches to global stability. This underscores the need to revise existing strategies, seek innovative approaches to ensuring international security, and potentially create new organizations or coalitions capable of responding more effectively to similar conflicts in the future. The findings of this research may be useful for policymakers, international security experts, and all those interested in the transformation of the global security system.

Heterogeneous uncertainty matters! Evidence based on firms' cost management decisions

The literature traditionally treats the uncertainty firms encounter as a whole, neglecting the inherent heterogeneity of uncertainty that prevails in the real world. To address this critical issue, this study employs natural language processing methods to decompose uncertainty into three distinct levels: firm-level, industry-level, and macro-level uncertainty. We then examine the impact of heterogeneous uncertainty on corporate decision-making from a cost management perspective. Our findings indicate that while uncertainty as a whole increases cost stickiness, this effect mainly stems from firm-level uncertainty rather than industry-level and macro-level uncertainty. These findings suggest that firms only respond to uncertainty when they perceive uncertainty at the firm level. Cross-sectional tests indicate that the impact of uncertainty on cost stickiness is more pronounced when firms face higher adjustment costs, lower financing constraints, and lower competitive pressures than otherwise. Additionally, uncertainty diminishes firms' cost elasticity, curtailing their production flexibility. Collectively, our results provide insights into comprehending the internal mechanisms of firms' decision-making in the face of uncertainty.

Quantifying perceived risk in driving: A Monte Carlo approach for obstacle avoidance

Objective This study aims to develop a model for quantifying perceived risk in obstacle avoidance, emphasizing how drivers’ perceived risk characteristics influence their driving decisions. The research addresses the lack of attention given to modeling risk from the perspective of drivers’ risk perceptions. Methods Monte Carlo methods are employed to account for the uncertainties and complexities of driving behavior, restoring the probabilistic nature of risk. The proposed method quantifies perceived risk by incorporating drivers’ fuzzy perceptions, enabling a quantitative evaluation during obstacle avoidance. A logit model is used to link perceived risk with driving decisions, identifying key factors influencing driver behavior in obstacle avoidance scenarios. Results Experimental data revealed significant variations in vehicle trajectories and speed distributions due to differences in drivers’ experience and proficiency. The perceived risk indicator (PRI) values for leftward bypasses were higher compared to rightward bypasses, and the receiver operating characteristic (ROC) curve confirmed the PRI’s strong predictive ability with an area under the curve (AUC) of 0.820. The logit model showed that both PRI and speed significantly influenced the probability of choosing a rightward bypass, achieving 90% accuracy. Building on the model, the study predicted and visualized the probability of vehicles turning right to avoid obstacles at different positions and speeds within 200 m of the obstacle. Conclusions The research offers a framework for traffic professionals to understand driver-perceived risk and decision-making mechanisms. This understanding is beneficial for improving traffic safety and highlights the importance of considering drivers’ risk perceptions in modeling driving behavior.

B-107 Advancing Parasitology Laboratory Quality and Processes Through Artificial Intelligence (AI) and Automation

Abstract Background This research emphasizes the profound impact of intestinal parasitic infections on developing countries, particularly in regions such as sub-Saharan Africa, South and Central America, China, and East Asia. With over 1.5 billion people globally affected and 450 million facing serious illness and a mortality rate of 155,000 cases per year, the socio-economic hindrances posed by these infections necessitate innovative approaches for control and eradication. The integration of artificial intelligence (AI) and machine learning (ML) into parasitology, exemplified by the “Automated Diagnosis of Intestinal Parasites” (ADIP), from brazilian Portuguese “Diagnóstico Automatizado de Parasitas Intestinais” (DAPI) system, showcases a paradigm shift. This advanced system, combining simple and complex decision-making mechanisms, demonstrates promising results, achieving high agreement compared to TF-Test (three fecal test). Methods Data were extracted through reports from the laboratory information system (LIS) from January-December 2023. TF-test was replaced by ADIP system in October 2023. Data were analyzed using Microsoft Excel software, with a bibliographic survey in national and international repositories. Results The parasitology laboratory of AFIP implemented artificial intelligence (AI) with ADIP equipment associated with system LIS automation, automating the analysis of approximately 38,000 monthly stool samples. The ADIP classification, image analysis and subsequent algorithm allowed a reduced examination time, since approximately 90% of samples could be released automatically, optimizing the whole process. In addition, after a three-month period using only ADIP, positivity results increased from 7,63% (TF-test) to 9,53%. Our database is periodically reviewed by specialized professionals who validate positive results, ensuring the security of information obtained by artificial intelligence, bringing efficiency, safety, and quality to the laboratory processes. Conclusions Artificial intelligence (AI) in parasite identification and the automated results system promises a bright future. Collaboration between the scientific community and data scientists is crucial to enhance laboratory processes. Through these tools, it is possible to focus on continuous improvements, increased efficiency, and, most importantly, without sacrificing accuracy and result quality. With new technologies associated with the parasitology field, detailed records can be ensured, previously challenging to obtain, showcasing each step from result acquisition to issued report. Process improvement in parasitology brings optimizations, suggesting the potential for team reduction without compromising quality and improving the takt time.

The Impact of Consumer Trust on Purchase Intentions in China

This paper aims to explore the impact of consumer trust on purchase intentions in the Chinese market. Consumer trust plays a critical role in purchasing decisions, especially in the complex and rapidly changing environment of China. This study first defines the core concept of consumer trust and constructs a theoretical framework linking trust with purchase intentions. By analyzing the unique cultural, market, and policy context of China, the research examines how these factors influence consumer trust and its impact on purchasing decision mechanisms. The paper further employs empirical research methods to analyze data on the relationship between consumer trust and purchase intentions and offers recommendations for businesses and policymakers. The findings indicate that consumer trust significantly affects purchase intentions, and enhancing consumer trust can effectively increase positive purchasing tendencies. Finally, the paper summarizes the main findings, discusses their theoretical and practical implications, and outlines directions for future research.

A Pathway for Advance Care Planning Co-Developed by Service Users and Interdisciplinary Healthcare Professionals in Older Persons Mental Health

Abstract Background Advance Care Planning (ACP) has been recognised as an effective mechanism for decision-making, maintaining autonomy, and ensuring that end-of-life care is aligned with an individual’s will and preference. However, healthcare professional's (HCPs) lack of confidence and knowledge has been cited as a barrier to practice. Furthermore, there are currently no widely accepted guidelines for ACP in health service settings (Barnett et al., 2024[1]). A key aim of the second stage of a three-stage action research study was to co-design a pathway to support ACP practice in a Mental Health Service for Older Persons. Reference [1] Barnett, M.D., Bennett-Leleux, L.J. & Guillory, L.A. (2024) End-of-life treatment preferences and ACP among older adults, Death Studies, 48:2, 95-102. Methods The pathway was developed through cooperative inquiry (CI), a collaborative research approach. The CI group involved three service users (one with dementia family carer experience), and six interdisciplinary HCPs coming together as co-subjects and co-researchers. Five in-person CI meetings were convened. Results The CI group co-developed a person-centred pathway encompassing a tiered approach to ACP, which would be a source of guidance and support for service users, family carers and HCPs. Key components include training and education for HCPs to enhance confidence and skills; a Padlet online platform to host resources for service users and family carers; and facilitated interventions based on the individual needs of service users, including potentially additional accompanying documentation to capture mental health needs. An interdisciplinary approach to facilitating ACP engagement, with social work taking a lead role was also suggested. Conclusion The pathway was identified as an effective approach to embedding ACP practice in older persons mental health, through a tiered person-centred framework catering to the needs of both service users, and the training needs of staff. The pathway is proposed as potentially adaptable to other older persons services to support ACP engagement.

Grassroots learning through indigenous co-design for a Kvmemongen in Coastal Lake Budi, Chile

ABSTRACT Given calls to decolonise engagement with Indigenous communities, this article explores how allied researchers can participate in self-determined learning with Indigenous Peoples. Drawing on over a decade of experience within an action-research collective in a Mapuche context, the authors suggest that allied researchers can accompany Indigenous-led co-design in a manner that not only strengthens genuine Indigenous participation but also fosters mutual and collective learning from within the co-creative processes themselves. Lake Budi, a biocultural hotspot in the Pacific coast of Northern Patagonia, Chile, is a coastal wetland habitat for hundreds of endemic and migratory species and the ancestral homeland for the Mapuche-Lafkenche (∼15,000) who, through grassroots learning, are determining practical steps towards restoring their territory and its self-governance for kvmemongen. This Mapuche concept refers broadly to enacting forms of living well together, humans and non-humans. As allied participants in a Mapuche-led codesign collective since 2013, in this paper, we focus on exploring key “moments of mutual learning” within this longer-than-usual co-design process. Each of these moments involved collective learning that required interaction and feedback loops across diverse areas of expertise, made possible over longer and flexible rhythms and periods. Tools, protocols, and methods gradually take shape in such a process through mutual learning opportunities provided by relationship building, cultural immersion, community-led protocols, decision-making, and evaluation mechanisms. This work suggests a new understanding of the involvement of allied researchers in Indigenous-led co-design as an emerging and increasingly relevant form of grassroots mutual learning toward climate resilience.

Phylogenomic instructed target analysis reveals ELAV complex binding to multiple optimally spaced U-rich motifs.

ELAV/Hu RNA-binding proteins are gene-specific regulators of alternative pre-mRNA processing. ELAV/Hu family proteins bind to short AU-rich motifs which are abundant in pre-mRNA, making it unclear how they achieve gene specificity. ELAV/Hu proteins multimerize, but how multimerization contributes to decode degenerate sequence environments remains uncertain. Here, we show that ELAV forms a saturable complex on extended RNA. Through phylogenomic instructed target analysis we identify the core binding motif U5N2U3, which is repeated in an extended binding site. Optimally spaced short U5N2U3 binding motifs are key for high-affinity binding in this minimal binding element. Binding strength correlates with ELAV-regulated alternative poly(A) site choice, which is physiologically relevant through regulation of the major ELAV target ewg in determining synapse numbers. We further identify a stem-loop secondary structure in the ewg binding site unwound upon ELAV binding at three distal U motifs. Base-pairing of U motifs prevents ELAV binding, but N6-methyladenosine (m6A) has little effect. Further, stem-loops are enriched in ELAV-regulated poly(A) sites. Additionally, ELAV can nucleate preferentially from 3' to 5'. Hence, we identify a decisive mechanism for ELAV complex formation, addressing a fundamental gap in understanding how ELAV/Hu family proteins decode degenerate sequence spaces for gene-specific mRNA processing.

Аксиологический аспект воли в научном познании

Introduction. The article presents the axiological aspect of will as a phenomenon of a person's regulation of his behavior and scientific activity, in particular. The study of the mechanisms of volitional decisions in scientific knowledge is necessary, first of all, because the will is responsible for the formation of goals and the implementation of efforts to realize them. The purpose of philosophical research is to identify the ontological sources of the phenomenon of will and their axiological analysis in scientific activity leading to creative judgments. Theoretical analysis. The axiological approach allows us to explore the possibilities of the mind, as opposed to impulsive desires, in conditions of making volitional decisions both in science and other forms of knowledge. The ontological basis of will is the presence of an obstacle, overcoming which becomes the fulfillment of a goal. In scientific activity, the will prevents the manifestation of emotional excitement, acting as a determinant of scientific research activity. An axiological analysis of will as a value and a source of positive emotions demonstrates the achievement of a goal by satisfying the need to overcome an obstacle that has a correlation with the category of singularity. Conclusion. A value analysis of the phenomenon of will shows the possibilities of human thinking for the most productive scientific activity, expressed in a combination of the optimal level of emotional stress and strong will.