Understanding Of Human Behavior Research Articles

Early evidence of extra-masticatory dental wear in a Neolithic community at Bestansur, Iraqi Kurdistan.

This paper presents the first evidence of extra-masticatory dental wear from Neolithic Bestansur, Iraqi Kurdistan (7700-7200 BC). Bestansur is a rare, recently excavated burial site of this period in the Zagros region, of Iraqi Kurdistan. A total of 585 teeth from 38 individuals were analyzed for features indicative of activities including oblique wear planes, notches, grooves, and chipping. Indications of extra-masticatory wear were found in 27 of 38 individuals, and 277 of 585 teeth (47%) available for study. The most frequent features were chipping and notches suggesting activities such as processing fibers by using the teeth as a "third hand." Evidence for these wear features was present in both males, females, and in children aged five and older. These aspects of childhood life-course and dentition are rarely investigated. The presence of dental wear features in the deciduous dentition can indicate an age range at which activities began in different groups and highlights the importance of including juvenile remains in such studies. The variety of forms of dental wear may relate to the mixed diet and activities of these people. This study adds to our understanding of human behaviors and socio-cultural aspects of life during this transitional period.

Shift Pose: A Lightweight Transformer-like Neural Network for Human Pose Estimation.

High-performing, real-time pose detection and tracking in real-time will enable computers to develop a finer-grained and more natural understanding of human behavior. However, the implementation of real-time human pose estimation remains a challenge. On the one hand, the performance of semantic keypoint tracking in live video footage requires high computational resources and large parameters, which limiting the accuracy of pose estimation. On the other hand, some transformer-based models were proposed recently with outstanding performance and much fewer parameters and FLOPs. However, the self-attention module in the transformer is not computationally friendly, which makes it difficult to apply these excellent models to real-time jobs. To overcome the above problems, we propose a transformer-like model, named ShiftPose, which is regression-based approach. The ShiftPose does not contain any self-attention module. Instead, we replace the self-attention module with a non-parameter operation called the shift operator. Meanwhile, we adapt the bridge–branch connection, instead of a fully-branched connection, such as HRNet, as our multi-resolution integration scheme. Specifically, the bottom half of our model adds the previous output, as well as the output from the top half of our model, corresponding to its resolution. Finally, the simple, yet promising, disentangled representation (SimDR) was used in our study to make the training process more stable. The experimental results on the MPII datasets were 86.4 PCKH, 29.1PCKH@0.1. On the COCO dataset, the results were 72.2 mAP and 91.5 AP50, 255 fps on GPU, with 10.2M parameters, and 1.6 GFLOPs. In addition, we tested our model for single-stage 3D human pose estimation and draw several useful and exploratory conclusions. The above results show good performance, and this paper provides a new method for high-performance, real-time attitude detection and tracking.

From the Editor Volume 22, Issue 2

By Paul H. Jacques. This issue explores topics that are, to the detriment of our understanding of human behavior, rarely seen in scholarly journals including LEGO Play, sexual harassment, and negative feedback.

From the Editor Volume 22, Issue 2

By Paul H. Jacques. This issue explores topics that are, to the detriment of our understanding of human behavior, rarely seen in scholarly journals including LEGO Play, sexual harassment, and negative feedback.

Overlaying human and mosquito behavioral data to estimate residual exposure to host-seeking mosquitoes and the protection of bednets in a malaria elimination setting where indoor residual spraying and nets were deployed together.

Characterizing persistent malaria transmission that occurs after the combined deployment of indoor residual spraying (IRS) and long-lasting insecticidal nets (LLINs) is critical to guide malaria control and elimination efforts. This requires a detailed understanding of both human and vector behaviors at the same temporal and spatial scale. Cross-sectional human behavior evaluations and mosquito collections were performed in parallel in Magude district, Mozambique. Net use and the exact time when participant moved into each of five environments (outdoor, indoor before bed, indoor in bed, indoor after getting up, and outdoor after getting up) were recorded for individuals from three different age groups and both sexes during a dry and a rainy season. Malaria mosquitoes were collected with CDC light traps in combination with collection bottle rotators. The percentage of residual exposure to host-seeking vectors that occurred in each environment was calculated for five local malaria vectors with different biting behaviors, and the actual (at observed levels of LLIN use) and potential (i.e. if all residents had used an LLIN) personal protection conferred by LLINs was estimated. Anopheles arabiensis was responsible for more than 74% of residents’ residual exposure to host-seeking vectors during the Magude project. The other four vector species (An. funestus s.s., An. parensis, An. squamosus and An. merus) were responsible for less than 10% each. The personal protection conferred by LLINs prevented only 39.2% of the exposure to host-seeking vectors that survived the implementation of both IRS and LLINs, and it differed significantly across seasons, vector species and age groups. At the observed levels of bednet use, 12.5% of all residual exposure to host-seeking vectors occurred outdoor during the evening, 21.9% indoor before going to bed, almost two thirds (64%) while people were in bed, 1.4% indoors after getting up and 0.2% outdoor after leaving the house. Almost a third of the residual exposure to host-seeking vectors (32.4%) occurred during the low transmission season. The residual bites of An. funestus s.s. and An. parensis outdoors and indoor before bedtime, of An. arabiensis indoors when people are in bed, and of An. squamosus both indoors and outdoors, are likely to have sustained malaria transmission throughout the Magude project. By increasing LLIN use, an additional 24.1% of exposure to the remaining hosts-seeking vectors could have been prevented. Since An. arabiensis, the most abundant vector, feeds primarily while people are in bed, increasing net use and net feeding inhibition (through e.g. community awareness activities and the selection of more effective LLINs) could significantly reduce the exposure to remaining host-seeking mosquitoes. Nonetheless, supplementary interventions aiming to reduce human-vector contact outdoors and/or indoors before people go to bed (e.g. through larval source management, window and eave screening, eave tubes, and spatial repellents) will be needed to reduce residual exposure to the outdoor and early biting An. funestus s.s. and An. parensis.

Auxiliary-LSTM based floor-level occupancy prediction using Wi-Fi access point logs

Smart city concepts have gained increased traction over the years. The advances in technology such as the Internet of things (IoT) networks and their large-scale implementation has facilitated data collection, which is used to obtain valuable insights towards managing, improving, and planning for services. One key component in this process is the understanding of human mobility behaviour. Traditional data collection methods such as surveys and GPS data have been extensively used to study human mobility. However, a key concern with such data is the protection of user privacy. This study aims to overcome those concerns using Wi-Fi access point logs and demonstrate their utility by creating building occupancy prediction models using advanced machine learning techniques. The floor level occupancy counts and auxiliary variable for a campus building are extracted from the Wi-Fi logs. They are used to develop specifications of Long-Short Term Memory network (LSTM), Auxiliary LSTM (Aux-LSTM), Autoregressive Integrated Moving Average (ARIMA), and Multi-layer Perceptron (MLP) models. The LSTM performed better than the other models and can efficiently capture peak values. Aux-LSTM was shown to increase the reliability in prediction and applicability in the context of facilities management. Results show the effectiveness of the Wi-Fi dataset in capturing trends, providing supplementary information, and highlight the ability of LSTM to adequately model time-series data.

BAYESIAN FUNCTIONAL REGISTRATION OF FMRI ACTIVATION MAPS.

Functional magnetic resonance imaging (fMRI) has provided invaluable insight into our understanding of human behavior. However, large inter-individual differences in both brain anatomy and functional localization after anatomical alignment remain a major limitation in conducting group analyses and performing population level inference. This paper addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subjects functional data to a common reference map. Our proposed Bayesian functional registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. It combines intensity-based and feature-based information into an integrated framework, and allows inference to be performed on the transformation via the posterior samples. We evaluate the method in a simulation study and apply it to data from a study of thermal pain. We find that the proposed approach provides increased sensitivity for group-level inference.

Understanding Human Thoughts, Behaviors, and Social Influence to Counter Racial Discrimination

In light of the rising societal tension and critical importance of systemic response, such as the Black Lives Matter movement and the Stop AAPI Hate movement, many have reexamined the role that discrimination plays on their perception of others. This paper recounts how racial discrimination is currently affecting society holistically and how in particular it affects African American Youth. The paper will then utilize current literature on the psychological aspect of discrimination in order to solidify a clearer understanding of human behaviors and thoughts with regard to racial discrimination. Lastly, this paper will illustrate how public institutions can extend their preexisting diversifying efforts using the key concepts of Moscovici’s minority influence theory in order to combat this systematic problem.

Gerçek Zamanlı İnsan Davranışı Anlamaya Doğru: Optimal-Altı Bir Şekil Tanımlayıcı

In this study, two novel shape descriptors are proposed to be used in human behavior understanding problem. First is optimal shape descriptor, which has high performance but works very slow due to high algorithmic complexity. Second is suboptimal shape descriptor, performance of which is very close to optimal one, but works much more faster. Optimal means using minimum data to represent maximum knowledge. Algorithms are run on Weizmann dataset and results are shown both as figure and video link. Classification was performed using 12 statistical features extracted from the data sets' human silhouettes. An accuracy rating of 92 percent was obtained by using Euclidean distance in classification.

Degenerate boundaries for multiple-alternative decisions

Integration-to-threshold models of two-choice perceptual decision making have guided our understanding of human and animal behavior and neural processing. Although such models seem to extend naturally to multiple-choice decision making, consensus on a normative framework has yet to emerge, and hence the implications of threshold characteristics for multiple choices have only been partially explored. Here we consider sequential Bayesian inference and a conceptualisation of decision making as a particle diffusing in n-dimensions. We show by simulation that, within a parameterised subset of time-independent boundaries, the optimal decision boundaries comprise a degenerate family of nonlinear structures that jointly depend on the state of multiple accumulators and speed-accuracy trade-offs. This degeneracy is contrary to current 2-choice results where there is a single optimal threshold. Such boundaries support both stationary and collapsing thresholds as optimal strategies for decision-making, both of which result from stationary representations of nonlinear boundaries. Our findings point towards a normative theory of multiple-choice decision making, provide a characterisation of optimal decision thresholds under this framework, and inform the debate between stationary and dynamic decision boundaries for optimal decision making.

How to Increase Teacher Performance through Engagement and Work Efficacy

Work engagement, work efficacy and performance are key concepts in today’s human resources field, impacting both personal and organisational levels. However, not many studies investigate them in a core professional area: pre-university teachers. After measuring the work engagement, work efficacy and work performance variables of teachers, we identified the differences in teachers’ work engagement, work efficacy and work performance according to their seniority in education and the position of the high school in the top national rankings. Our paper’s focus is on exploring the relationship between work engagement and work performance among pre-university teachers from Romanian high schools. This relationship is analysed both directly and indirectly by including work efficacy as a mediating factor. A sample of 817 Romanian high school teachers participated in this study (questionnaire applied in April 2021). The results can lead to a better understanding of human resources behaviour, and, on this basis, to the formulating of human resource policies in the educational field.

World Dengue Day: A call for action.

Commemorating the 2021 ASEAN Dengue Day and advocacy for World Dengue Day, the International Society for Neglected Tropical Diseases (ISNTD) and Asian Dengue Voice and Action (ADVA) Group jointly hosted the ISNTD-ADVA World Dengue Day Forum–Cross Sector Synergies in June 2021. The forum aimed to achieve international and multisectoral coordination to consolidate global dengue control and prevention efforts, share best practices and resources, and improve global preparedness. The forum featured experts around the world who shared their insight, research experience, and strategies to tackle the growing threat of dengue. Over 2,000 healthcare care professionals, researchers, epidemiologists, and policy makers from 59 countries attended the forum, highlighting the urgency for integrated, multisectoral collaboration between health, environment, education, and policy to continue the march against dengue. Sustained vector control, environmental management, surveillance improved case management, continuous vaccine advocacy and research, capacity building, political commitment, and community engagement are crucial components of dengue control. A coordinated strategy based on science, transparency, timely and credible communication, and understanding of human behavior is needed to overcome vaccine hesitancy, a major health risk further magnified by the COVID-19 pandemic. The forum announced a strong call to action to establish World Dengue Day to improve global awareness, share best practices, and prioritize preparedness in the fight against dengue.

How Organizational Neuroscience and Self-Determination Theory Explain Neuro-Leadership

Organizational neuroscience (ON) explores new brain research alternatives and contributes to HR field for further understanding of human behaviour and decision-making process. Also, ON studies provide opportunities to extend leadership approach that is one the most interesting research area of organizational behaviour through latest discoveries on neural activation pathway during decision process. However, without taking into consideration of psychological theories behind the leadership philosophy, ON would not be enough to explain and improve leadership behavioural patterns. At this point, Self-determination theory (SDT) will present detail explanation of intrinsic motivational factors that are the main success factors of leaders. Neuro-leadership use SDT factors that are autonomy, relatedness and competence to go one step further on upcoming leadership theories. Neuro-leadership will be a tie between psychological theories and organizational neuroscience. This article illustrate how neuroscience, psychology and leadership approaches can be combined to address how to enhance leadership skills in business environment and the new way of shaping leadership behavioral pattern. In addition to Neuro-leadership approach of SCRAF model, emotional regulation ability should become a recent research area for leadership studies. Contemporary leadership development training should comprise sessions for reappraising environmental stimulus on positive way while dealing with managerial problems on the business settings.

Understanding Urban Area Attractiveness Based on Private Car Trajectory Data Using a Deep Learning Approach

With the fast development of urbanization and motorization, an increasing number of people choose to buy private cars to fulfill their daily travel needs. In particular, many people from various positions of the city drive their cars to specified areas, and then they will stop and stay for a certain period of time, leading to a spatiotemporal evolution of urban area attractiveness (AA). In this paper, we aim at understanding urban AA based on analyses of private car trajectory datasets. Specifically, by extracting point-of-stop (PoS) data from the private car trajectories, we design the variational Bayesian Gaussian mixture models (VBGMM) to deduce the probability density distribution of PoSs and connect it to the variation of AA. We establish a deep learning model based on long short-term memory (LSTM) to capture the evolution of the AA. Furthermore, we integrate dropout in the LSTM method to address challenging issues such as overfitting and time-consuming training of complex neural networks in the AA prediction. We conduct experiments by using real-world private car trajectory data to evaluate the performance of the proposed method. The results validate that our proposed method outperforms existing ones in terms of various metrics. To the authors’ knowledge, our work is the first one to utilize private car trajectory data to study urban area attractiveness, thereby facilitating a new perspective regarding an understanding of human travel behavior and the evolution of urban mobility.

Classification of three-dimensional integral stimuli: Accounting for a replication and extension of Nosofsky and Palmeri (1996) with a dual discrimination invariance model.

The quest for determining the degree of learning difficulty associated with different types of categories has been instrumental in our understanding of human categorization behavior and, more broadly, human generalization. For instance, we now know that the topological nature of the dimensions (e.g., whether these are integral or separable) that define the family of categories generated with three binary dimensions yield two different learning difficulty orderings. In our study, for the first time, we replicated one such classic ordering involving integral dimensions and explored the impact that the choice of dimensional values, along with the nature of the stimulus presentation, had on learning difficulty during the learning phase of our four experiments. Two standard orderings were observed in our investigation which were consistent with the hypothesis that multilevel discrimination plays a key role in concept learning. We accounted for our empirical results by making this role explicit at three levels, the feature, object, and category structure levels, using a dual discrimination invariance model (DDIM) derived from the core invariance law of generalized invariance structure theory (GIST). The model involves a "discrimination switch" via what is referred to in GIST as the "tau discrimination threshold" where two distinct extreme degrees of discrimination yield two fundamental learning difficulty orderings able to account for results from our experiments without free parameters. We then show how the DDIM is not only a more accurate and general predictor than the best alternatives but also provides a plausible and tenable cognitive mechanism for understanding these behaviors. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Generalized Euclidean Measure to Estimate Distances on Multilayer Networks

Estimating the distance covered by a spreading event on a network can lead to a better understanding of epidemics, economic growth, and human behavior. There are many methods solving this problem—which has been called Node Vector Distance (NVD)—for single layer networks. However, many phenomena are better represented by multilayer networks: networks in which nodes can connect in qualitatively different ways. In this article, we extend the literature by proposing an algorithm solving NVD for multilayer networks. We do so by adapting the Mahalanobis distance, incorporating the graph’s topology via the pseudoinverse of its Laplacian. Since this is a proper generalization of the Euclidean distance in a complex space defined by the topology of the graph, and that it works on multilayer networks, we call our measure the Multi Layer Generalized Euclidean (MLGE). In our experiments, we show that MLGE is intuitive, theoretically simpler than the alternatives, performs well in recovering infection parameters, and it is useful in specific case studies. MLGE requires solving a special case of the effective resistance on the graph, which has a high time complexity. However, this needs to be done only once per network. In the experiments, we show that MLGE can cache its most computationally heavy parts, allowing it to solve hundreds of NVD problems on the same network with little to no additional runtime. MLGE is provided as a free open source tool, along with the data and the code necessary to replicate our results.

Fatigue and Arousal Modulations Revealed by Saccade and Pupil Dynamics.

Saccadic eye movements are directed to the objects of interests and enable high-resolution visual images in the exploration of the visual world. There is a trial-to-trial variation in saccade dynamics even in a simple task, possibly attributed to arousal fluctuations. Previous studies have showed that an increase of fatigue level over time, also known as time-on-task, can be revealed by saccade peak velocity. In addition, pupil size, controlled by the autonomic nervous system, has long been used as an arousal index. However, limited research has been done with regards to the relation between pupil size and saccade behavior in the context of trial-to-trial variation. To investigate fatigue and arousal effects on saccadic and pupillary responses, we used bright and emotional stimuli to evoke pupillary responses in tasks requiring reactive and voluntary saccade generation. Decreased voluntary saccade peak velocities, reduced tonic pupil size and phasic pupillary responses were observed as time-on-task increased. Moreover, tonic pupil size affected saccade latency and dynamics, with steeper saccade main sequence slope as tonic pupil size increased. In summary, saccade dynamics and tonic pupil size were sensitive to fatigue and arousal level, together providing valuable information for the understanding of human behavior.

Behaviour as relational process: linking theory to practice

ABSTRACT Behaviour supports in schools and educational psychology practices have been largely influenced by the view of human behaviour as individual action. Individual action-focused practices are associated with identifying, labelling and separating individual young people. This paper argues for a relational process-oriented understanding of human behaviour that can lead to more dynamic practices that are responsive to complexity and diversity. This paper explores how a relational lens in practice could shift the conversation from severe behaviour challenges to behaviour as a relational process and generate more collaborative, inclusive and culturally responsive school practices. A growing interest in relationally oriented practices is evident in the increasing use of culturally responsive, restorative, narrative, and solution-focused practices in schools. What seems to be missing is a conceptual shift that would allow relational orientation in current practices to be better recognised and strategically developed.

The Metaverse as a Virtual Form of Smart Cities: Opportunities and Challenges for Environmental, Economic, and Social Sustainability in Urban Futures

Data infrastructures, economic processes, and governance models of digital platforms are increasingly pervading urban sectors and spheres of urban life. This phenomenon is known as platformization, which has in turn given rise to the phenomena of platform society, where platforms have permeated the core of urban societies. A recent manifestation of platformization is the Metaverse, a global platform project launched by Meta (formerly Facebook) as a globally operating platform company. The Metaverse represents an idea of a hypothetical “parallel virtual world” that incarnate ways of living and working in virtual cities as an alternative to smart cities of the future. Indeed, with emerging innovative technologies—such as Artificial Intelligence, Big Data, the IoT, and Digital Twins—providing rich datasets and advanced computational understandings of human behavior, the Metaverse has the potential to redefine city designing activities and service provisioning towards increasing urban efficiencies, accountabilities, and quality performance. However, there still remain ethical, human, social, and cultural concerns as to the Metaverse’s influence upon the quality of human social interactions and its prospective scope in reconstructing the quality of urban life. This paper undertakes an upper-level literature review of the area of the Metaverse from a broader perspective. Further, it maps the emerging products and services of the Metaverse, and explores their potential contributions to smart cities with respect to their virtual incarnation, with a particular focus on the environmental, economic, and social goals of sustainability. This study may help urban policy makers to better understand the opportunities and implications of the Metaverse upon tech-mediated practices and applied urban agendas, as well as assess the positives and negatives of this techno-urban vision. This paper also offers thoughts regarding the argument that the Metaverse has disruptive and substantive effects on forms of reconstructing reality in an increasingly platformized urban society. This will hopefully stimulate prospective research and further critical perspectives on the topic.

Geographic information science in the era of geospatial big data: A cyberspace perspective

Geographic information science in the era of geospatial big data: A cyberspace perspective