Complex Human Behavior Research Articles

Three principles for the utility of simple tasks that assess elemental processes in parsing heterogeneity.

As clinical psychological science and biological psychiatry push to assess, model, and integrate heterogeneity and individual differences, approaches leveraging computational modeling, translational methods, and dimensional approaches to psychopathology are increasingly useful in establishing brain-behavior relationships. The field is ultimately interested in complex human behavior, and disruptions in such behaviors can arise through many different pathways, leading to heterogeneity in etiology for seemingly similar presentations. Parsing this complexity may be enhanced using "simple" tasks-which we define as those assaying elemental processes that are the building blocks to complexity. Using eyeblink conditioning as one illustrative example, we propose that simple tasks assessing elemental processes can be leveraged by and enhance computational psychiatry and dimensional approaches in service of understanding heterogeneity in psychiatry, especially when these tasks meet three principles: (a) an extensively mapped circuit, (b) clear brain-behavior relationships, and (c) relevance to understanding etiological processes and/or treatment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Public transport across models and scales: A case study of the munich network

Abstract The use of public transport systems is a striking example of complex human behavior. Modeling, planning, and managing public transport is a major future challenge considering the drastically accelerated population growth in many urban areas. The desire to design sustainable cities that can cope with a dynamically increasing demand requires models for transport networks since we are not able to perform real-life experiments before constructing additional infrastructure. Yet, there is a fundamental challenge in the modeling process: we have to understand which basic principles apply to the design of transit networks. In this work, we are going to compare three scientific methods to understand human behavior in public transport modeling: agent-based models, centralized optimization-based models, and minimal physicsbased models. As a case study, we focus on the transport network in Munich, Germany. We show that there are certain universal macroscopic emergent features of public transport that arise regardless of the model chosen. In particular, we can obtain with minimal basic assumptions a common and robust distribution for the individual passenger in-vehicle time as well as for several other distributions. Yet, there are other more microscopic features that differ between the individual and centralized organization and/or that cannot be reproduced by a minimal non-local random-walk type model. Finally, we cross-validate our results with observed public transport data. In summary, our results provide a key understanding of the basic assumptions that have to underlie transport modeling for human behavior in future sustainable cities.

Bounded risk disposition explains Turing patterns and tipping points during spatial contagions.

Spatial contagions, such as pandemics, opinion polarization, infodemics and civil unrest, exhibit non-trivial spatio-temporal patterns and dynamics driven by complex human behaviours and population mobility. Here, we propose a concise generic framework to model different contagion types within a suitably defined contagion vulnerability space. This space comprises risk disposition, considered in terms of bounded risk aversion and adaptive responsiveness and a generalized susceptibility acquisition. We show that resultant geospatial contagion configurations follow intricate Turing patterns observed in reaction-diffusion systems. Pattern formation is shown to be highly sensitive to changes in underlying vulnerability parameters. The identified critical regimes (tipping points) imply that slight changes in susceptibility acquisition and perceived local risks can significantly alter the population flow and resultant contagion patterns. We examine several case studies using Australian datasets (COVID-19 pandemic; crime incidence; conflict exposure during COVID-19 protests; real estate businesses and residential building approvals) and demonstrate that these spatial contagions generated Turing patterns in accordance with the proposed model.

AN ANALYSIS OF BEHAVIOURAL FINANCE AND ITS INFLUENCE ON INVESTMENT DECISION

This research delves into the fascinating realm of Behavioural Finance, exploring its significant influence on investment decision-making among residents in India's National Capital Region (NCR). By drawing on insights from relevant studies, this work seeks to uncover the intricate link between human behaviour and financial decision-making within this demographic. The study begins by examining the unique financial behaviours of investors in the region, aiming to identify the key investor profiles that shape the investment environment. As a multidisciplinary field combining elements of psychology, sociology, and finance, Behavioural Finance provides the primary framework for this exploration. The research distinguishes between its micro and macro perspectives: the detailed analysis of individual behavioural traits of investors and the broader identification of inefficiencies in the market. Recognizing the pervasive impact of behavioural biases on investment behaviours and decisions, this study offers an in-depth review of diverse research within the Behavioural Finance domain. It sheds light on how human psychology and social dynamics interact with financial decision-making, highlighting the crucial role of Behavioural Finance in contemporary financial practices. In an era where financial markets are not merely governed by rationality but are also shaped by complex human behaviours, this research offers valuable insights into the dynamics of Behavioural Finance. By illuminating the distinct investment choices, the study aims to deepen our understanding of the factors influencing these decisions, ultimately equipping investors and financial professionals with more informed decision-making strategies.

Subthalamic nucleus neurons encode syllable sequence and phonetic characteristics during speech.

Speech is a complex behavior that can be used to study unique contributions of the basal ganglia to motor control in the human brain. Computational models suggest that the basal ganglia encode either the phonetic content or the sequence of speech elements. To explore this question, we investigated the relationship between phoneme and sequence features of a spoken syllable triplet and the firing rate of subthalamic nucleus (STN) neurons recorded during the implantation of deep brain stimulation (DBS) electrodes in individuals with Parkinson's disease. Patients repeated aloud a random sequence of three consonant-vowel (CV) syllables in response to audio cues. Single-unit extracellular potentials were sampled from the sensorimotor STN; a total of 227 unit recordings were obtained from the left STN of 25 subjects (4 females). Of these, 113 (50%) units showed significant task-related increased firing and 53 (23%) showed decreased firing (t test relative to inter-trial period baseline, P < 0.05). Linear regression analysis revealed that both populations of STN neurons encode phoneme and sequence features of produced speech. Maximal phoneme encoding occurred at the time of phoneme production, suggesting efference copy- or sensory-related processing, rather than speech motor planning (-50 ms and +175 ms relative to CV transition for consonant and vowel encoding, respectively). These findings demonstrate that involvement of the basal ganglia in speaking includes separate single unit representations of speech sequencing and phoneme selection in the STN.NEW & NOTEWORTHY Speech is a unique human behavior that requires dynamic execution of precisely timed and coordinated movements, resulting in intelligible vocalizations. Here, we demonstrate that activity of individual neurons in the subthalamic nucleus (STN) of the basal ganglia encode syllable sequence order and phoneme identity during a speech production task. These findings advance our understanding of neural substrates of human speech and shed light on potential involvement of the STN in complex human behaviors.

A Perspective Study of Infamous Classic Women in the Novels of Vaishnavi Patel’s ‘Kaikeyi’ and Kavita Kane’s ‘Karna’s Wife: the Outcast’s Queen’

Abstract Indian Mythology has many complex characters that reflect contemporary issues. Vaishnavi Patel and Kavita Kane, renowned women writers, have reinterpreted the epic tales of the Ramayana and the Mahabharata through a modern perspective. This paper examines human behaviour using a psychological approach in Patel’s “Kaikeyi”and Kavita Kane’s “Karna’s Wife: The Outcast’s Queen”. Patel’s“Kaikeyi”explores the protagonist’s optimistic traits, her childhood longings, and her feminist qualities. Traditionally depicted as a villain in the Ramayana, Kaikeyi is recognized as Rama’s stepmother who instigates his exile. However, before assuming this role, she experienced a difficult and challenging childhood in the Kingdom of Kekaya. The novel sheds light on the darker aspects of Kaikeyi’s past, providing a deeper understanding of her character. Kavita Kane’s interest lies in unveiling the complexities of classic women in her literary works. In “Karna’s Wife”, Uruvi, the protagonist, is portrayed as the marginalized wife of Karna, who belongs to the Sutapura lineage. Uruvi, a princess from the Kshatriya family of Pukeya kingdom is depicted as an optimistic character with great skills, knowledge, and mental strength.These two novels focus on the lives of classic women, their behavioural changes, and their insecurities in different situations. Women are inherently multifaceted and encounter various challenges throughout their lives. Different approaches are required to address these challenges. According to Sigmund Freud, the human psyche consists of three aspects that interact and influence each other to create complex human behaviour. Freud’s psychological theory can be used to analyse the characters of Kaikeyi and Uruvi in these novels. The study attempts to highlight the need for women to overcome feelings of insecurity, emotional dependence, to achieve self-realization, and build resilience, which are essential qualities in the contemporary era.

Semi-supervised human action recognition via dual-stream cross-fusion and class-aware memory bank

Human action recognition, a crucial task for understanding complex human behavior from sensor data, faces significant challenges. This study specifically addresses two of these challenges: the complex interplay between joint positions and their velocities in action data, and improving model generalizability in the context of limited labeled data. To this end, we present the dual-stream cross-fusion and class-aware memory bank (DSCF-CAMB) framework, a novel semi-supervised approach. It employs a dual-stream cross-fusion (DSCF) mechanism to independently encode joint and velocity data streams, and through cross-fusion, enriches the action representation by exploring correlations between joint positions and their velocities. Meanwhile, to address the challenges posed by unlabeled data, we have developed a contrastive loss based class-aware memory bank (CAMB) that carefully selects high-confidence positive and negative sample embeddings and filters out potential noise-contributing samples. This strategy significantly improves the robustness and accuracy of the framework in action recognition. The experimental results show that our method outperforms the state-of-the-art methods by an average of 2.7% and 1.5% in top-1 accuracy on the renowned NTU RGB+D and NW-UCLA datasets, respectively, demonstrating the superiority of our method in human action recognition.

Human Behavior Analysis Using Machine Learning

The study and analysis of human behavior have been longstanding challenges in various fields such as psychology, sociology, and anthropology. With the advent of advanced technologies and the proliferation of data, machine learning has emerged as a powerful tool for deciphering and understanding complex human behaviors. This research aims to explore the application of machine learning techniques in the analysis of human behavior, leveraging diverse datasets and cutting-edge algorithms to gain insights into individual and collective actions. The research methodology involves the collection of multidimensional data sources, including but not limited to social media interactions, physiological measurements, and environmental factors. These datasets are preprocessed and integrated to create a comprehensive representation of human behavior. Machine learning models, ranging from traditional statistical methods to deep learning architectures, are then applied to uncover patterns, correlations, and predictive insights from the data. Machine learning algorithms can analyze patterns in behavior to identify potential signs of mental health issues, enabling early intervention and treatment. Tracking behavioral data can help in managing chronic conditions by providing insights into lifestyle choices, adherence to treatment plans, and overall well-being. Machine learning models can identify unusual patterns of behavior, which is crucial for security purposes. This can include recognizing suspicious activities in public spaces or flagging unusual behavior in online environments. The purpose of this study is to explore the challenges of multiple attribute decision-making when dealing with intuitionist fuzzy information. In this scenario, the attribute weights are not entirely known, and the attribute values are represented by intuitionist fuzzy numbers. To determine the attribute weights, an optimization model is constructed based on the traditional grey relational analysis (GRA) fundamental principles. The proposed method involves calculating the grey relation degree between each alternative and the positive-ideal solution and negative-ideal solution. This degree is then used to define a relative relational degree, which enables the ranking of all alternatives simultaneously with respect to both the positive-ideal solution (PIS) and negative-ideal solution (NIS). From the result 22 is ranked at first position and 45 is ranked at fifth position

Insights Unveiled: Harnessing AI to Explore Human Behaviour in Social Sciences

AI's evolution has expanded its potential to understand complex human behaviours, revealing new insights into human psychology and sociology. This essay explores AI's capabilities in detecting intricate behavioural patterns, its applications in social sciences, the challenges and ethical considerations associated with its use, and predictions for the future. AI's ability to uncover patterns invisible to the human mind offers an exciting frontier of exploration, particularly in understanding human behaviour. Human behaviour, characterized by its intricacy and influenced by sociocultural, economic, and psychological factors, has traditionally been studied through various methodologies. However, the unpredictability and dynamism of human behaviour often defy simple categorization or comprehension. AI has the potential to serve as a guide, decoding patterns beyond the reach of human cognition and traditional methodologies. AI can offer a new lens through which we view the rich tapestry of human experience, such as predicting social trends, understanding consumer behaviour, and exploring the underpinnings of political sentiment. Despite challenges such as feasibility, interpretability, and ethics, the potential impact of AI in understanding human behaviour is enormous. By shedding light on hidden patterns, we can foster a more nuanced understanding of our societies, inform policies, drive innovation, and potentially address some of the pressing challenges of our times.

The impact of functional class and depressive symptomatology on rule-based insensitivity

Rule-governed behavior (RGB) is important for accounting for complex human behavior, as well as informing clinical practice. In line with Relational Frame Theory, RGB is divided into functional classes including pliance, which is rule-following under the control of apparent arbitrary social consequences, and tracking, which is rule-following under the control of apparent natural non-arbitrary consequences. In theory, pliance should produce lower levels of contingency sensitivity compared to tracking, but how this effect relates to nonclinical populations is unclear. This study (n = 134) tested the relationship between contingency sensitivity in pliance and tracking through a Matching-to-Sample task, while incorporating a measure of depressive symptomatology to test if a distinction in insensitivity would be seen in nondepressed participants. Results indicated that pliance was associated with lower levels of contingency sensitivity relative to tracking overall, and that depressive symptomatology did not influence this. Results are discussed in relation to prior inconsistencies in the experimental literature on RGB, and potential methods for operationalizing pliance in experimental settings.

Characterizing Large-Scale Human Circuit Development with In Vivo Neuroimaging.

Large-scale coordinated patterns of neural activity are crucial for the integration of information in the human brain and to enable complex and flexible human behavior across the life span. Through recent advances in noninvasive functional magnetic resonance imaging (fMRI) methods, it is now possible to study this activity and how it emerges in the living fetal brain across the second half of human gestation. This work has demonstrated that functional activity in the fetal brain has several features in keeping with highly organized networks of activity, which are undergoing a highly programmed and rapid sequence of development before birth, in which long-range connections emerge and core features of the mature functional connectome (such as hub regions and a gradient organization) are established. In this review, the findings of these studies are summarized, their relationship to the known changes in developmental neurobiology is considered, and considerations for future work in the context of limitations to the fMRI approach are presented.

Exploring the Association Between Textual Parameters and Psychological and Cognitive Factors.

Textual data analysis has become a popular method for examining complex human behavior in various fields, including psychology, psychiatry, sociology, computer science, data mining, forensic sciences, and communication studies. However, identifying the most relevant textual parameters for analyzing complex behavior is still a challenge. This paper aims to explore potential textual parameters that could be useful in analyzing behavior through complex textual data. Furthermore, we have examined the randomly generated text based on different textual parameters. To achieve this goal, we conducted a comprehensive review of the literature on textual data analysis and identified several potential topics that could be relevant, such as sentiment analysis, discourse analysis, lexical analysis, and syntactic analysis. We discuss the theoretical background and practical implications of each parameter and provide examples of how they have been used in previous research. Furthermore, we highlight the importance of considering the context in which these parameters are applied and the need for interdisciplinary collaboration to gain a deeper understanding of complex behavior through textual data analysis. Furthermore, we have provided Python code in the Supplementary Materials to facilitate a comprehensive analysis of such behaviors. In addition, to generate the text for analysis, we utilized ChatGPT 3.5 Turbo by requesting it to generate a random text of 1000 words divided into five paragraphs. Afterwards, we applied the provided Python code to analyze the randomly generated text. Overall, this paper provides a foundation for researchers to identify relevant textual parameters to analyze complex human behavior in their respective fields such as linguistics, sociology, psychiatry, and psychology.

Research on cultural diversity and sustainable land-use management assessment model

This study quantitatively evaluated the role of cultural diversity and psychological awareness in sustainable land-use management (SLUM). Firstly, an assessment model based on fuzzy analytic hierarchy process is established by combining fuzzy logic, expert knowledge and analytic hier-archy process. The model addresses the uncertainty of qualitative assessment and adapts to dy-namic environments and complex human behavior. The research process included a literature review and expert consultation to identify key factors influencing land use and assign weights to these factors through FAHP. Subsequently, FAHP determined that cultural diversity had the highest weight among all considerations, indicating the urgent need to respect and integrate local cultural diversity in land management. Psychological awareness follows closely, suggesting that land-use decisions are profoundly influenced not only by tangible economic and environmental conditions, but also by the psychological states, knowledge levels, value systems, and social perceptions of groups and individuals. These findings highlight the need to consider and integrate these intangible factors when developing land management strategies. This approach fosters broad social acceptance and participation, ultimately moving towards the long-term goal of sustainable land use. Finally, the study provides decision-makers with a tool for identifying subtle differences at the cultural and psychological levels that can help develop more nuanced and personalized management options for different land use strategies.

The Shape of Relations to Come: Multidimensional Analyses of Complex Human Behavior

AbstractScience, understood to be the behavior of scientists, falls within the purview of behavior analysis. All scientists use scientific instruments to study a natural phenomenon, and for the behavior analyst, perhaps no tool is more important than the graph used to show changes in level, trend, and variability, and upon which behavior analysts make data-based decisions. Modern behaviorism as we know it dates back to the development of the cumulative recorder first developed in the 1930s. Though revolutionary to the science of behavior, two-dimensional graphs may be limited in application for analyzing complex human behavior. In the current article, we conceptualize verbal behavior as a multidimensional field of environmental relations, and introduce the use of multi-axial radar charts for its visual and quantitative analysis. From there, we survey the use of radar charts toward advancing a behavior-analytic understanding of human language and cognition. We demonstrate the use of radar charts for calculating simple shape descriptors as a quantitative measure of dynamic interactants, and show how they can be used to measure change over time.

Antimicrobial Stewardship Program at a Tertiary Care Hospital: A Road Less Travelled

The manuscript provides a practical overview of implementing an antimicrobial stewardship program from a very nascent stage to a well-established program in the complex setting of a tertiary care hospital. It gives the readers a road map to initiate or refine their journey utilizing many principles of quality improvement, common sense, and negotiating complex human behavior. It also showcases a good use of the scientific principles of quality improvement and change management. Starting small with well-defined surgical prophylaxis paved the way for the complex world of empirical prescription of antimicrobials later in this journey. Various strategies like prescription audit and feedback, handshake stewardship, antimicrobial time-out, and greater mindfulness towards antimicrobial prescription have been well highlighted. Regular point prevalence surveys provided us with actionable data for multiple interventions. Moreover, it highlights the well-defined process and outcome metrics that measured the various aspects of antimicrobial prescription and were instrumental in assessing the success or challenges in implementing the program. Compliance with surgical prophylaxis improved from 34 % to 71%, while compliance with de-escalation increased from 38% to 57%.

Problems and Solutions of Quantitative Analysis in Financial Market

This paper explores the role and challenges of quantitative analysis in financial markets. It provides an overview of the applications of quantitative analysis in the financial market, including risk management, portfolio optimization, asset pricing, algorithmic trading, and market prediction. The paper also identifies three main challenges in using quantitative analysis: limited data, lack of human intuition, and overreliance on data. Limited data can lead to inaccurate or biased predictions, and the lack of human intuition in quantitative models can limit their ability to capture complex human behavior and unexpected events. Overreliance on data is also a potential risk that may lead to false correlations. Solutions to these problems are proposed, including incorporating domain expertise, qualitative information, interpretability, transparency, and careful consideration of the data's context. Financial professionals can make more informed and effective decisions using quantitative analysis by addressing these challenges.

On Enmity, Terror and Fear

In this article, HARM presents a small excerpt from Ernst E. Boesch’s book “Von Kunst bis Terror - Über den Zwiespalt in der Kultur” [From art to terror - On the dichotomy in culture], published in 2005. The editors of HARM want to draw attention to a psychologist and social theorist who saw himself as a cultural psychologist and, as such, wanted to show how much more complex, ambivalent, opaque and unpredictable human thinking, feeling, behaviour, and actions are than mainstream psychology — which models and primarily quantifies and measures — would have us believe. This, according to one of his convictions which continues to shape modern cultural psychology, is because it recognises humans not primarily as natural creatures, but also as cultural beings who, as beings gifted with special language, are entangled in an indissoluble network of meaningful symbolic worlds and thus are capable of manifold processes of sense-making. In order to depict and reconstruct these entanglements and to understand their significance for human experience, attitudes and actions, Boesch argues that hermeneutic-interpretative procedures are required which, we are convinced, also contribute significantly to refining the depiction and analysis of phenomena that are relevant in the context of HARM. — Boesch’s text is preceded by a brief introduction to his life, his thinking and the selected text.

“Quiet Strength and Bold Energy” Unraveling Psychological Dynamics of Friendship among Mismatched Personalities

Among all human relationships, friendship is the most significant one and plays a profound role in our life. The core aim of the study was to explore the fascinating picture of human connection of interpersonal closeness among the friends having mismatched personalities; and further, how these introvert-extrovert friends mold each other to reach the harmony, quality and flexibility in their friendship. The study utilized exploratory qualitative research to examine the intricate nature of uneven human interaction. Sample was recruited on the basis of personality type screening and acquaintance pairs. Screening was done on a sample of 7 mismatched pairs of friends through 11 items Myers Briggs Type Indicator from which only 5 pairs reached at the cut off score. Further, the sample was confined to 3 pairs since data was reached at saturation point. The obtained data was transcribed and codes were extracted using content analysis (Humble, 2015; Lapan et al., 2012). The results revealed 5 themes majorly; interpersonal interactions, emotional approach, altruism, friendship envy, and rejection sensitivity. Current study concluded that Introverts and Extroverts make great friends, with the key to their friendship lying in mutual understanding and genuine affection for each other. The current study benefits in understanding complex human behavior in different relationships.

B - 72 Neuropsychological Problem-Solving Styles on the Tower of London Drexel.

The Tower of LondonDX (2nd Edition TOLDX, Culbertson & Zillmer, 2005) provides a standardized measure of executive planning and problem solving. To enhance the measure's clinical and empirical utility, four contrasting Executive Planning Styles (EPS), were identified based on two different, but moderately related (i.e., rho = -0.37), TOLDX variables, Time to First Move and Total Move Score. The TOLDX normative data (Technical Manual) for participants ranging in age from 7 to 80years (n = 990) were utilized for study. The participants were drawn from sites in the United States and Canada. An individual's assignment to an EPS was based on initiation time and performance scores either one standard deviation above or below the mean for that individual's normative group. Using these criteria, four contrasting EPS were identified. The four Executive Efficiency constructs are labeled as: Q1 Efficient-Fast, exhibiting rapid, advanced planning skills; Q2 Efficient-Slow, suggesting careful, deliberate, and strategic planning; Q3 Inefficient-Fast, indicating pervasive impulsivity, nonstrategic planning, and an increased likelihood of poor rule-governed behavior; and Q4 Inefficient-Slow, suggesting limitations in executive planning, trial-and-error problem-solving, and possible cognitive limitations. The purpose of this analysis was to further differentiate the executive planning process of an established measure of executive function by combing two constructs (initiation time and performance) into four Executive Planning Styles. Executive planning is a dimension of complex human behavior, and the four-quadrant conceptualization of Executive Efficiency expands our understanding of this very important neuropsychological construct.

Childhood-Onset Fluency Disorder with marked ingressive speech: a prospective case study

This prospective case study aimed to examine the effectiveness of a comprehensive treatment approach employed for a 14-year-old boy, EK, with Childhood-Onset Fluency Disorder (COFD), particularly manifested by excessive pulmonic ingressive speech and phonation as well as profound secondary behaviors that resemble those attested in individuals with Tic Syndrome (TS). A single-case, experimental-longitudinal design was used, with a hypothesis adopting a therapeutic approach, which integrated strategies used to manage several behaviors in TS, would reduce dysfluency and associated secondary behaviors and that gains would be maintained. Perceptual and instrumental data were obtained. The findings offered evidence for the effectiveness of the therapeutic program, which could be attributed to the possible similarities in the underlying pathogenesis of stuttering and TS – glitches in the neurophysiological system that give rise or contribute to the two disorders. The results further back up comprehensive assessment and therapeutic approach facilitating better understanding and management of complex human behaviors like stuttering.