Complex Human Behavior Research Articles

A Practical Guide to Conversation Research: How to Study What People Say to Each Other

Conversation—a verbal interaction between two or more people—is a complex, pervasive, and consequential human behavior. Conversations have been studied across many academic disciplines. However, advances in recording and analysis techniques over the last decade have allowed researchers to more directly and precisely examine conversations in natural contexts and at a larger scale than ever before, and these advances open new paths to understand humanity and the social world. Existing reviews of text analysis and conversation research have focused on text generated by a single author (e.g., product reviews, news articles, and public speeches) and thus leave open questions about the unique challenges presented by interactive conversation data (i.e., dialogue). In this article, we suggest approaches to overcome common challenges in the workflow of conversation science, including recording and transcribing conversations, structuring data (to merge turn-level and speaker-level data sets), extracting and aggregating linguistic features, estimating effects, and sharing data. This practical guide is meant to shed light on current best practices and empower more researchers to study conversations more directly—to expand the community of conversation scholars and contribute to a greater cumulative scientific understanding of the social world.

SynthEco - A multi-layered digital ecosystem for analysing complex human behaviour in context

Introduction & BackgroundHuman behaviour is multi-faceted and complex, with different dimensions interacting and impacting each other and individuals operating in an environmental context. In order to understand this behaviour better, the combination of data from different sources is useful to uncover some of those interactions and complexities. We present a multi-layered digital ecosystem based on a data platform providing statistically representative synthetic population derived from census data at different geo-spatial granularity, which we call SynthEco. This platform is enriched with individual data stemming from cohorts and cross-sectional surveys and geo-scanning of different layers of socio-environmental actors and conditions to create a complex digital ecosystem.
 Objectives & ApproachThe objective of SynthEco is to allow for the analysis of behaviour, as well as health and wellbeing outcomes, through the integration of cohort and cross-sectional data into a geospatially anchored synthetic population embedded into environmental data which is forming the backdrop. We demonstrate the use of this platform on the example of Montreal, Canada. The synthetic population is first generated from census data using iterative proportional fitting, which allows for the creation of a population data set that is artificial yet statistically representative for a given geospatial granularity, such as a city. Each individual household is assigned a geospatial location, which allows for the consideration of their surrounding environment including enterprises or institutions such as schools, hospitals and the local food environment. Through fuzzy matching and statistical extrapolation, different cohort and cross-sectional survey data are then merged to individual records, in order to describe them in more detail. This includes health, as well as financial wellbeing or social environment descriptors.
 Relevance to Digital FootprintsThere are two important points made through the presented work in relation to Digital Footprints data: the first is the technical approach to merging multiple datasets describing different dimensions of interacting human characteristics and behaviour by anchoring them into a synthetic population through fuzzy record matching. The second is the consideration of a spatial dimension when describing human behaviour. This is especially important when describing behaviour within local environments, such as the interaction with local food outlets.
 ResultsRecent work in this context includes an analysis of the food environment in Montreal, Canada. It introduces a way of utilising the synthetic population to predict the healthfulness of their local environment in terms of healthy food outlets, as well as providing a platform for the analysis of food environment surveillance and intervention simulations. For this purpose, the healthfulness of different census tract regions in Montreal is calculated to identify food deserts, food swamps, as well as healthy areas as defined through the Modified Retail Food Environment Index. We test different machine learning approaches to then predict these healthfulness scores using census variables from the synthetic population in their respective census tract, achieving accuracy scores of around 0.53 to 0.60. This demonstrates that census data has some limited predictive power in explaining the healthiness of the local food environment, which could be especially relevant for situations in which no information on the retailers is available to local policy makers. Future work can extend this approach to also include further data describing the population, stemming from the integrated cohorts and survey data, which could improve the prediction accuracy or help in identifying areas of concern.
 Conclusions & ImplicationsThe presented SynthEco platform views individuals as agents nested within modular systems of systems, trying to capture both internal systems and processes as well as environmental ones within which individuals are operating. The platform thus enables the application of computational systems modelling for the analysis of individual human behaviour in contexts. As demonstrated through the example of using SynthEco in the context of healthier food environments, the approach is especially relevant to practitioners and policy makers interested in local intervention strategies and identifying areas for targeted policy related to different dimensions of health and wellbeing.

ATFA: Adversarial Time–Frequency Attention network for sensor-based multimodal human activity recognition

Human activity recognition (HAR) is a key component of mobile and ubiquitous computing. With the increasing number of sensors embedded in devices such as smartphones and smartwatches, models are enhanced to recognize more complex human behaviors. However, this also leads to increased data heterogeneity, caused by diverse user backgrounds, health conditions, and sensing environments, which makes it difficult to align the multimodal data distributions across individuals. This paper proposes a novel unsupervised domain adaptation framework via Adversarial Time-Frequency Attention (ATFA) to efficiently adapt models to new users. In particular, the proposed attention-based modality fusion module captures and fuses important modalities based on their context to reduce redundant information. Additionally, the network explores frequency domain features to improve performance in recognizing human activities. Extensive experiments are conducted on three publicly available HAR datasets to demonstrate the superiority of our proposed method compared to state-of-the-art baselines.

Decision and response monitoring during working memory are sequentially represented in the human insula

Emerging research supports a role of the insula in human cognition. Here, we used intracranial EEG to investigate the spatiotemporal dynamics in the insula during a verbal working memory (vWM) task. We found robust effects for theta, beta, and high frequency activity (HFA) during probe presentation requiring a decision. Theta band activity showed differential involvement across left and right insulae while sequential HFA modulations were observed along the anteroposterior axis. HFA in anterior insula tracked decision making and subsequent HFA was observed in posterior insula after the behavioral response. Our results provide electrophysiological evidence of engagement of different insula subregions in both decision-making and response monitoring during vWM and expand our knowledge of the role of the insula in complex human behavior.

Using a picture (or a thousand words) for supporting spatial knowledge of a complex virtual environment

External representations powerfully support and augment complex human behavior. When navigating, people often consult external representations to help them find the way to go, but do maps or verbal instructions improve spatial knowledge or support effective wayfinding? Here, we examine spatial knowledge with and without external representations in two studies where participants learn a complex virtual environment. In the first study, we asked participants to generate their own maps or verbal instructions, partway through learning. We found no evidence of improved spatial knowledge in a pointing task requiring participants to infer the direction between two targets, either on the same route or on different routes, and no differences between groups in accurately recreating a map of the target landmarks. However, as a methodological note, pointing was correlated with the accuracy of the maps that participants drew. In the second study, participants had access to an accurate map or set of verbal instructions that they could study while learning the layout of target landmarks. Again, we found no evidence of differentially improved spatial knowledge in the pointing task, although we did find that the map group could recreate a map of the target landmarks more accurately. However, overall improvement was high. There was evidence that the nature of improvement across all conditions was specific to initial navigation ability levels. Our findings add to a mixed literature on the role of external representations for navigation and suggest that more substantial intervention—more scaffolding, explicit training, enhanced visualization, perhaps with personalized sequencing—may be necessary to improve navigation ability.

PL-TARMI: A deep learning framework for pixel-level traffic crash risk map inference

A citywide traffic crash risk map is of great significance for preventing future traffic crashes. However, the fine-grained geographic traffic crash risk inference is still a challenging task, mainly due to the complex road network structure, human behavior and high data requirements. In this work, we propose a deep-learning framework PL-TARMI, which leverages easily accessible data to achieve accurate fine-grained traffic crash risk map inference. Specifically, we integrate the satellite image and road network image, combine with other accessible data (e.g., point of interest distribution, human mobility data, traffic data, etc.) as input, and finally obtain the pixel-level traffic crash risk map, which could provide more reasonable traffic crash prevention guidance with a lower cost. Extensive experiments on real-world datasets demonstrate the effectiveness of PL-TARMI.

Drivers of food choice, meal pattern, and lifestyle habits among undergraduate students in Lead City University, Ibadan, Oyo State, Nigeria

Background and objective: Food choice is a complex human behavior, which is influenced by many interdependent factors. University students are exposed to unhealthy eating habits and poor dietary intake. This could be attributable to the independent food choices made. This study assessed the drivers of food choice, patterns, and lifestyle habits of undergraduate students at Lead City University Ibadan, Nigeria.Method: The study adopted a cross-sectional design and a multistage random sampling technique to select 200 respondents. Data on socio-demographic characteristics, eating habits, and dietary practices were obtained using a self-administered questionnaire.Results: The mean age of the students was 21 ± 2 years,46% of the students ate thrice per day, 36% skipped breakfast daily and 17% consumed alcohol. The main drivers of food choice among the students were sensory appeal in which taste (79%), pleasant texture (54%), and nice smell (54%) of food were considered very important. Concerning health and natural food content, the respondents considered good feeling (74%), weight control (48%), no additives (46%), low-fat content (42%), and low-calorie content (35%) as very important associated factors. Good value for money (53%), not expensive (42%), and easy availability (49%) were very important economic factors for driving food choices among respondents.Conclusion: Sensory appeal, health and natural food content, and price and purchase convenience were major drivers of food choice among respondents. It is recommended that regular nutrition education programs by the institution with emphasis on healthy dietary practices should be provided for the students.

Issues, Problems and Challenges of Enhancing Social Functioning among Children with Cognitive Disabilities in Tanzania: A Theoretical Review

The paper attempts to provide a theoretical and empirical perspective related to the issues, problems and challenges of enhancing social functioning among children with cognitive disability in Tanzania. Specifically, the study reviews three theories, namely empowerment, inclusive and ecological with a view of finding the challenges associated with the enhancement of children with cognitive disability. The study is a literature review based on survey from books, scholarly articles and other relevant sources seeking explanations on the enhancement of social functioning among children with cognitive disability. The reviewed literature were summarized, analysed, and synthesized. The findings reveal that, the theories used in social work practice have diverse and different foci of analysis and intervention. This is largely because addressing a social problem there are many variables and uncertainties in social work practice. Because of that, there is no one single objective “reality” in social theory and practice. Secondly, whereas theories such as the ecological describes the influence of different subsystems in the changing individual behaviour, other behaviours such as discrimination and social exclusion among people with disabilities do not seem to change. The study further reveals that the assumptions of the inclusive theory of developing positive social relationships (e.g. friendships and acquisition of social skills) have not been successful because of existence negative attitudes among societal members which are influenced by false beliefs about people with cognitive disability. The study concludes that, whereas the theories help social workers to understand complex human behaviour and social environment which influence their clients’ lives and the nature of social problems, there are no theories which help in understanding African realities. The study observes that, negative social environment and attitudinal change have a negative impact in facilitating positive behavioural change in as far as enhancing social functioning among children with cognitive disability. The study recommends that more research be conducted to address the existing prohibiting factors. It is further recommended that concerted be made among different actors including policy interventions to raise awareness of local communities.

Home health care routing and scheduling in densely populated communities considering complex human behaviours

This study focuses on the home health care routing problem (HHCRP) in the scenario of high population density areas where many elders live closely together. This study considers two main objectives. The first is to reduce travel and wait times for nurses or elders. The second concerns socially related objectives in scheduling problems, such as ‘quality of life’ and empowerment, by considering assumptions related to the acquaintanceship and mutual preferences of nurses and elders. This study models the effects of mutual preferences and acquaintanceship on service time in HHCRP. We use the Markov decision process and chance-constrained programming (CCP) to model the system to conserve the sequential service provision parameters and better represent the influence of stochastic service times. Because traditional deterministic algorithms cannot solve such a model, we apply a model-free reinforcement learning algorithm, Q-learning (QL), as well as the ant colony optimisation (ACO) algorithm. Thus, we tackle this problem by developing a model and algorithm to solve complex, large-scale systems. This study’s theoretical and practical contributions are verified by feedback from researchers and practitioners.

Vision-based activity recognition in children with autism-related behaviors

Advances in machine learning and contactless sensors have enabled the understanding complex human behaviors in a healthcare setting. In particular, several deep learning systems have been introduced to enable comprehensive analysis of neuro-developmental conditions such as Autism Spectrum Disorder (ASD). This condition affects children from their early developmental stages onwards, and diagnosis relies entirely on observing the child's behavior and detecting behavioral cues. However, the diagnosis process is time-consuming as it requires long-term behavior observation, and the scarce availability of specialists. We demonstrate the effect of a region-based computer vision system to help clinicians and parents analyze a child's behavior. For this purpose, we adopt and enhance a dataset for analyzing autism-related actions using videos of children captured in uncontrolled environments (e.g. videos collected with consumer-grade cameras, in varied environments). The data is pre-processed by detecting the target child in the video to reduce the impact of background noise. Motivated by the effectiveness of temporal convolutional models, we propose both light-weight and conventional models capable of extracting action features from video frames and classifying autism-related behaviors by analyzing the relationships between frames in a video. By extensively evaluating feature extraction and learning strategies, we demonstrate that the highest performance is attained through the use of an Inflated 3D Convnet and Multi-Stage Temporal Convolutional Network. Our model achieved a Weighted F1-score of 0.83 for the classification of the three autism-related actions. We also propose a light-weight solution by employing the ESNet backbone with the same action recognition model, achieving a competitive 0.71 Weighted F1-score, and enabling potential deployment on embedded systems. Experimental results demonstrate the ability of our proposed models to recognize autism-related actions from videos captured in an uncontrolled environment, and thus can assist clinicians in analyzing ASD.

Modeling of human behavior in norm and mental pathology

Active development of scientific technologies and the digital transformation of the healthcare service promise a serious breakthrough in understanding the norm and pathology, assessing the risks of diseases or specific mental conditions, predicting their course and possible consequences. In other words, there are hopes that in the nearest future science allows modeling and predicting of complex human behavior at least in some contexts. The article analyzes the progress in the field of human behavior modeling in medicine and, particularly, in psychiatry, for which the explanation of behavioral disorders is the most relevant. 1175 publications were reviewed and 74 of them were selected for further analysis of the exiting methods for human behavior modeling in norm and mental pathology. The article highlights the possibilities of human behavior modeling and its most promising prospects. The possibilities of such modeling at the present time are limited. Most part of the created models have no sufficient verification and are unsuitable for solving real practical problems. In addition, science progress is far from explaining complex variants of human behavior, and it is unclear if it is possible to model such behavior using computer architecture, which is significantly different from biological. Various behavioral models are considered, that allow to solve specific practical tasks in psychiatry and healthcare system, and therefore seem to be the most promising.

The rabbit-hole of conspiracy theories: An analysis from the perspective of the free energy principle

ABSTRACT I investigate the underlying cognitive mechanisms and socio-emotional factors behind conspiracy theory (CT) beliefs through the lens of the Free-Energy Principle (FEP). The FEP framework is employed to explain the emergence of CTs in the face of cumulative uncertainties and the influence of emotions on belief formation. The FEP account I propose concludes that considering emotional factors, distrust of established authorities, and the social environment, believing in CTs is a bounded rational choice for some individuals in certain contexts. This explains why CT believers are resistant to changing their views. Applying FEP to the complex human behavior of CT belief and propagation, this paper not only provides insights into the phenomenon but also enhances the theoretical credence of FEP itself.

Detecting Medication-Taking Gestures Using Machine Learning and Accelerometer Data Collected via Smartwatch Technology: Instrument Validation Study

Medication adherence is a global public health challenge, as only approximately 50% of people adhere to their medication regimens. Medication reminders have shown promising results in terms of promoting medication adherence. However, practical mechanisms to determine whether a medication has been taken or not, once people are reminded, remain elusive. Emerging smartwatch technology may more objectively, unobtrusively, and automatically detect medication taking than currently available methods. This study aimed to examine the feasibility of detecting natural medication-taking gestures using smartwatches. A convenience sample (N=28) was recruited using the snowball sampling method. During data collection, each participant recorded at least 5 protocol-guided (scripted) medication-taking events and at least 10 natural instances of medication-taking events per day for 5 days. Using a smartwatch, the accelerometer data were recorded for each session at a sampling rate of 25 Hz. The raw recordings were scrutinized by a team member to validate the accuracy of the self-reports. The validated data were used to train an artificial neural network (ANN) to detect a medication-taking event. The training and testing data included previously recorded accelerometer data from smoking, eating, and jogging activities in addition to the medication-taking data recorded in this study. The accuracy of the model to identify medication taking was evaluated by comparing the ANN's output with the actual output. Most (n=20, 71%) of the 28 study participants were college students and aged 20 to 56 years. Most individuals were Asian (n=12, 43%) or White (n=12, 43%), single (n=24, 86%), and right-hand dominant (n=23, 82%). In total, 2800 medication-taking gestures (n=1400, 50% natural plus n=1400, 50% scripted gestures) were used to train the network. During the testing session, 560 natural medication-taking events that were not previously presented to the ANN were used to assess the network. The accuracy, precision, and recall were calculated to confirm the performance of the network. The trained ANN exhibited an average true-positive and true-negative performance of 96.5% and 94.5%, respectively. The network exhibited <5% error in the incorrect classification of medication-taking gestures. Smartwatch technology may provide an accurate, nonintrusive means of monitoring complex human behaviors such as natural medication-taking gestures. Future research is warranted to evaluate the efficacy of using modern sensing devices and machine learning algorithms to monitor medication-taking behavior and improve medication adherence.

Learning signatures of decision making from many individuals playing the same game.

Human behavior is incredibly complex and the factors that drive decision making-from instinct, to strategy, to biases between individuals-often vary over multiple timescales. In this paper, we design a predictive framework that learns representations to encode an individual's 'behavioral style', i.e. long-term behavioral trends, while simultaneously predicting future actions and choices. The model explicitly separates representations into three latent spaces: the recent past space, the short-term space, and the long-term space where we hope to capture individual differences. To simultaneously extract both global and local variables from complex human behavior, our method combines a multi-scale temporal convolutional network with latent prediction tasks, where we encourage embeddings across the entire sequence, as well as subsets of the sequence, to be mapped to similar points in the latent space. We develop and apply our method to a large-scale behavioral dataset from 1,000 humans playing a 3-armed bandit task, and analyze what our model's resulting embeddings reveal about the human decision making process. In addition to predicting future choices, we show that our model can learn rich representations of human behavior over multiple timescales and provide signatures of differences in individuals.

Understanding common human driving semantics for autonomous vehicles

Autonomous vehicles will share roads with human-driven vehicles until the transition to fully autonomous transport systems is complete. The critical challenge of improving mutual understanding between both vehicle types cannot be addressed only by feeding extensive driving data into data-driven models but by enabling autonomous vehicles to understand and apply common driving behaviors analogous to human drivers. Therefore, we designed and conducted two electroencephalography experiments for comparing the cerebral activities of human linguistics and driving understanding. The results showed that driving activates hierarchical neural functions in the auditory cortex, which is analogous to abstraction in linguistic understanding. Subsequently, we proposed a neural-informed, semantics-driven framework to understand common human driving behavior in a brain-inspired manner. This study highlights the pathway of fusing neuroscience into complex human behavior understanding tasks and provides a computational neural model to understand human driving behaviors, which will enable autonomous vehicles to perceive and think like human drivers.

Spatiotemporally distributed frontotemporal networks for sentence reading.

Reading a sentence entails integrating the meanings of individual words to infer more complex, higher-order meaning. This highly rapid and complex human behavior is known to engage the inferior frontal gyrus (IFG) and middle temporal gyrus (MTG) in the language-dominant hemisphere, yet whether there are distinct contributions of these regions to sentence reading is still unclear. To probe these neural spatiotemporal dynamics, we used direct intracranial recordings to measure neural activity while reading sentences, meaning-deficient Jabberwocky sentences, and lists of words or pseudowords. We isolated two functionally and spatiotemporally distinct frontotemporal networks, each sensitive to distinct aspects of word and sentence composition. The first distributed network engages the IFG and MTG, with IFG activity preceding MTG. Activity in this network ramps up over the duration of a sentence and is reduced or absent during Jabberwocky and word lists, implying its role in the derivation of sentence-level meaning. The second network engages the superior temporal gyrus and the IFG, with temporal responses leading those in frontal lobe, and shows greater activation for each word in a list than those in sentences, suggesting that sentential context enables greater efficiency in the lexical and/or phonological processing of individual words. These adjacent, yet spatiotemporally dissociable neural mechanisms for word- and sentence-level processes shed light on the richly layered semantic networks that enable us to fluently read. These results imply distributed, dynamic computation across the frontotemporal language network rather than a clear dichotomy between the contributions of frontal and temporal structures.

Equivalence Class Formation in Adults with Severe Behavioral Problems

Stimulus equivalence is a behavioral phenomenon that has been related to complex human behavior (e.g., remembering, cognitive functioning, and symbolic behavior). As a rule, people diagnosed with severe mental disorders (e.g., schizophrenia, bipolar disorder) that exhibit delusional and hallucinatory behavior, and disorganized speech have shown cognitive impairment (e.g., processing speed, reasoning/problem solving). Not enough research has analyzed the stimulus equivalence performance in this population. This study aims to investigate the stimulus equivalence performance in adults diagnosed with severe mental disorders. In particular, this study analyzes the many-to-one (MTO) and one-to-many (OTM) training structures effects, and the simultaneous (SIM) and the simple-to-complex (STC) training and testing protocol effects on equivalence class formation in this population. To achieve it, we analyzed the behavior of 18 participants diagnosed with severe mental disorders in three different conditions (Condition 1 OTM/SIM; 2 MTO/SIM; and 3 MTO/STC). Behavior consistent with stimulus equivalence was found in 11 out of 13 participants who had finished the study (5 participants decided to leave before completing the tasks). STC yielded better results than the SIM protocol. No differences were found between MTO and OTM training structures. Implications and suggestions for further research have been discussed.

Exploring the limits to our understanding of whether fish feel pain.

Exploring the limits to our understanding of whether fish feel pain.

On quality and complexity: non-conformance failures, management perspectives and learning outcomes on a highways megaproject

PurposeThe construction industry continues to struggle to deliver a right first time culture, seeking a panacea for improvement whilst maintaining project milestones. Complex construction projects demand stringent programmes, however, (un)foreseen changes, political influences and human behaviours all have significant impacts on delivering schemes without error. Previous studies have questioned the ability of the construction industry to successfully learn from errors. A major barrier has been the sharing of sensitive data from failed outcomes. Hence, this paper investigates non-conformance on an existing scheme and suggests avenues for improvement.Design/methodology/approachA mixed-method approach was adopted whereby 1,260 non-conformance reports (NCRs) from a highways megaproject were interrogated using root cause analysis (RCA) techniques to uncover the most frequent and costly areas. This was followed by a survey to industry professionals within a tier 1 principal contractor to gain insight into their perceptions of non-conformance and rework on construction projects.FindingsUsing Pareto analysis, the authors find that materials management, workmanship (poor quality execution) and supervision issues are the most frequently occurring and costly root causes of non-conformance on a major highways scheme. Furthermore, the authors link corresponding viewpoints of two project professional groups to the posed, achieving a high degree of consensus for the areas requiring development. Lastly, the authors suggest avenues for improvement via lessons learnt. These include greater emphasis on quality culture via a strong leadership mandate, enhanced vetting of workforce competence and improving the way materials are managed by embracing technology to drive efficiency.Originality/valueThis paper interrogates a current highways scheme using a uniquely rich, sensitive dataset to determine how the construction sector may improve efforts to achieving right first time outcomes.

Theory of brain complexity and marital behaviors: The application of complexity science and neuroscience to explain the complexities of marital behaviors.

The extant theories on the quality and stability of marital relationships have some difficulties in explaining some of the complexities of marital behaviors. The present article is an initial attempt to explain the complexities of marital behaviors based on the science of complexity and neuroscience. This article proposes a new theoretical framework relying on this simple argument that marital behaviors, as one of the most complex human behaviors, are the product of one's brain's complex adaptive system (CAS). Hence, to understand the complexities of marital behaviors, a movement toward familiarity with the brain's CAS involved in marital behaviors needs to be started. The article presents the theory of brain complexity and marital behaviors (BCM) and outlines its assumptions, concepts, and propositions. Then, BCM is compared with the extant theories on happy and stable marriage, and finally, it was concluded by discussing the testability and the potential application of the theory. This article might inspire interdisciplinary studies of marital relationships, complex systems, and neuroscience and may have considerable practical implications.