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In order to further improve the accuracy of the video-based behavior recognition method, an effective behavior recognition method in the video session using convolutional neural network is proposed. Specifically, by adding the target detection phase before the behavior recognition algorithm, the body region in the video can be accurately extracted to reduce the interference of redundant and unnecessary background noises, and at the same time, the inappropriate images can be replaced, which has reached the role of balance background trade-off, and finally, the neural network can learn the human behavior information with emphasis. By adding fragmentation and stochastic sampling, the long-time time-domain modeling of the whole video session can be established, so that the model can obtain video-level expression ability. Finally, the improved loss function is used for behavior recognition to solve the problem of classification difficulty and possible sample imbalance. In addition, we conducted the hyperparametric experiment, the ablation experiment and the contrast experiment on different open source and benchmark datasets. Compared with other commonly used behavior recognition algorithms, the experimental results verify the effectiveness of the proposed method. In addition, the related deep learning-based methods used in behavior recognition are reviewed at the beginning of this paper, and the challenges in behavior recognition and future research directions are prospected at the end of this paper, which will undoubtedly play a double role in the work of later researchers. [ABSTRACT FROM AUTHOR]

Powered exoskeletons are typically task-specific, but to facilitate their wider adoption they should support a variety of tasks, which requires generalizeable controller designs. In this paper, we present two potential controllers for ankle exoskeletons based on soleus fascicles and Achilles tendon models. The methods use an estimate of the adenosine triphosphate hydrolysis rate of the soleus based on fascicle velocity. Models were evaluated using muscle dynamics from the literature, which were measured with ultrasound. We compare the simulated behavior of these methods against each other and to human-in-the-loop optimized torque profiles. Both methods generated distinct profiles for walking and running with speed variations. One of the approaches was more appropriate for walking, while the other approach estimated profiles similar to the literature for both walking and running. Human-in-the-loop methods require long optimizations to set parameters per individual for each specific task, the proposed methods can produce similar profiles, work across walking and running, and be implemented with body-worn sensors without requiring torque profile parameterization and optimization for every task. Future evaluations should examine how human behavior changes due to external assistance when using these control models. [ABSTRACT FROM AUTHOR]

Southwestern Iberia has played a key role in characterizing Late Pleistocene human ecodynamics. Among other aspects of human behavior, chert procurement and management studies in this region have received increasing attention in the past two decades, especially focusing on the sites showing repeated human occupation, such as the case of Vale Boi (Southern Portugal). However, these studies have been very limited in their geographical scope, and mostly focused on brief macroscopic descriptions of the raw materials. To further our knowledge of the relationship between regional availability of raw materials and its impact on human adaptations and mobility, a more detailed approach to characterizing geological sources is needed. This paper characterizes chert raw materials location, diversity, and availability in a geologically well-defined region of southern Portugal ‐ the Algarve. Through macroscopic and petrographic approaches, we provide a detailed characterization of geological chert sources to build a frame of reference for chert exploitation in the region. Our results show that there are four main chert formations in Algarve, and that despite the within-source variability, sufficient differences at macroscopic and petrographic levels are present to allow clear source attribution. These results provide a baseline for raw material studies in archaeological assemblages across southwestern Iberia, that will be essential to further characterize the dynamics of human behavior in some of the most important eco-cultural niches. [ABSTRACT FROM AUTHOR]

Although strategic coalition formation is traditionally modeled using cooperative game theory, behavioral game theorists have repeatedly shown that outcomes predicted by game theory are different from those generated by actual human behavior. To further explore these differences, in a cooperative game theory context, we experiment to compare the outcomes resulting from human participants' behavior to those generated by a cooperative game theory solution mechanism called the core partition. Our experiment uses an interactive simulation of a glove game, a particular type of cooperative game, to collect the participant's decision choices and their resultant outcomes. Two different glove games are considered, and the outputs from 62 trial games are analyzed. The experiment's outcomes show that core coalitions, which are coalitions in a core partition, are found in about 42% of games. Though this number may seem low, a trial's outcome is more complex than whether the human player finds a core coalition or not. Finding the core coalition depends on factors such as the other possible feasible solutions and the payoffs available from these solutions. These factors, and the complexity they generate, are discussed in the paper. [ABSTRACT FROM AUTHOR]

Topic modelling with innovative deep learning methods has gained interest for a wide range of applications that includes COVID-19. It can provide, psychological, social and cultural insights for understanding human behaviour in extreme events such as the COVID-19 pandemic. In this paper, we use prominent deep learning-based language models for COVID-19 topic modelling taking into account data from the emergence (Alpha) to the Omicron variant in India. Our results show that the topics extracted for the subsequent waves had certain overlapping themes such as governance, vaccination, and pandemic management while novel issues aroused in political, social and economic situations during the COVID-19 pandemic. We also find a strong correlation between the major topics with news media prevalent during the respective time period. Hence, our framework has the potential to capture major issues arising during different phases of the COVID-19 pandemic which can be extended to other countries and regions. [ABSTRACT FROM AUTHOR]

Trachoma, a neglected tropical disease (NTDs) caused by bacterium Chlamydia trachomatis, is a leading cause of infectious blindness. Efforts are underway to eliminate trachoma as a public health problem by using the "SAFE" strategy. While mathematical models are now standard tools used to support elimination efforts and there are a variety of models studying different aspects of trachoma transmission dynamics, the "F" component of the strategy corresponding to facial cleanliness has received very little attention so far. In this paper, we incorporate human behavior into a standard epidemiological model and develop a dynamical game during which individuals practice facial cleanliness based on their epidemiological status and perceived benefits and costs. We found that the number of infectious individuals generally increases with the difficulty to access a water source. However, this increase happens only during three transition periods and the prevalence stays constant otherwise. Consequently, improving access to water can help eliminate trachoma, but the improvement needs to be significant enough to cross at least one of the three transition thresholds; otherwise the improved access will have no noticeable effect. [ABSTRACT FROM AUTHOR]

Mental representations are of great importance for understanding human behaviour. The aim of this article is to present an innovative way to assess emotional scripts, which are a form of mental representations of emotional events, based on an analysis of narratives and their contents. Theoretical background on emotional schemas and scripts is provided along with information about types of related measures. Then, a rationale is presented for introducing an assessment of scripts related to specific emotions such as love, hate, and anxiety in a psychopathological context. This is followed by a perspective explaining the procedure of the relevant technique based on narrative data analysis. The technique has been successfully applied in two studies [I study (n– 200), II study (n– 280)]. A total of 1440 narratives about specific emotions have been analyzed to identify the indicators of scripts. The psychometric properties of the proposed technique have been established such as reliability, inter-rater agreement, and accuracy. The results show the value of the assessment of emotional script in individuals, particularly with high psychopathy and high trait anxiety. The contents of love and hate scripts are an illustration of cognitive distortions and deficits in the emotional information processing in individuals with psychopathy. The method enables the collection of informative data on romantic love, hate, and anxiety scripts which provides insight into how people may perceive and experience emotions and how they behave emotionally. Future research should focus on verification of the technique in other types of psychopathology and on the improvement of computer software dedicated to the narrative technique described in this paper. [ABSTRACT FROM AUTHOR]

The proliferation of Social Media and Open Web data has provided researchers with a unique opportunity to better understand human behavior at different levels. In this paper, we show how data from Open Street Map and Twitter could be analyzed and used to portray detailed Human Emotions at a city wide level in two cities, San Francisco and London. Neural Network classifiers for fine-grained emotions were developed, tested and used to detect emotions from tweets in the two cites. The detected emotions were then matched to key locations extracted from Open Street Map. Through an analysis of the resulting data set, we highlight the effect different days, locations and POI neighborhoods have on the expression of human emotions in the cities. [ABSTRACT FROM AUTHOR]

In many tasks, human behavior is far noisier than is optimal. Yet when asked to behave randomly, people are typically too predictable. We argue that these apparently contrasting observations have the same origin: the operation of a general-purpose local sampling algorithm for probabilistic inference. This account makes distinctive predictions regarding random sequence generation, not predicted by previous accounts—which suggests that randomness is produced by inhibition of habitual behavior, striving for unpredictability. We verify these predictions in two experiments: people show the same deviations from randomness when randomly generating from non-uniform or recently-learned distributions. In addition, our data show a novel signature behavior, that people's sequences have too few changes of trajectory, which argues against the specific local sampling algorithms that have been proposed in past work with other tasks. Using computational modeling, we show that local sampling where direction is maintained across trials best explains our data, which suggests it may be used in other tasks too. While local sampling has previously explained why people are unpredictable in standard cognitive tasks, here it also explains why human random sequences are not unpredictable enough. Author summary: When explicitly asked to be random, people are not random enough. Previous accounts of these random generation tasks have argued that people are effortfully trying not to be predictable. In many other tasks, however, people also show random behavior, even when it is unnecessary or outright disadvantageous. Here, we try to bridge this apparent gap. We hypothesize that the randomness people produce when trying to be random and the randomness that they display when trying to make the best choice has the same common mechanism: drawing mental samples to make judgments and decisions. In two experiments, we compare previous random generation accounts, which are task-specific in nature, to the more general account of mental sampling that has been used to explain how people behave in many other domains. We find that the flexibility of human random generation in our data is better explained by the mental sampling account. We also find a novel empirical signature of momentum in random generation, which points to a new kind of mental sampling algorithm. If mental sampling governs behavior in random generation tasks and elsewhere, then this task has great promise in helping to understand wider human behavior. [ABSTRACT FROM AUTHOR]

The structure of the internal representation of surrounding space, the so-called cognitive map, has long been debated. A Euclidean metric map is the most straight-forward hypothesis, but human navigation has been shown to systematically deviate from the Euclidean ground truth. Vector navigation based on non-metric models can better explain the observed behavior, but also discards useful geometric properties such as fast shortcut estimation and cue integration. Here, we propose another alternative, a Euclidean metric map that is systematically distorted to account for the observed behavior. The map is found by embedding the non-metric model, a labeled graph, into 2D Euclidean coordinates. We compared these two models using data from a human behavioral study where participants had to learn and navigate a non-Euclidean maze (i.e., with wormholes) and perform direct shortcuts between different locations. Even though the Euclidean embedding cannot correctly represent the non-Euclidean environment, both models predicted the data equally well. We argue that the embedding naturally arises from integrating the local position information into a metric framework, which makes the model more powerful and robust than the non-metric alternative. It may therefore be a better model for the human cognitive map. Author summary: How is the metric of space, i.e., knowledge about distances and angles between places, represented in the brain? Existing theories argue for either purely relational topological graphs without a metric, or consistent Euclidean maps where each place is assigned specific coordinates. The problem lies in the fact that human behavior systematically deviates from perfect metric maps, and theories need to account for these deviations. We propose an intermediate model that has both properties of non-metric graphs and metric maps, by embedding a graph labeled with local position information into metric space. In this "embedded graph", measurements of local metric information also affect the estimates of adjacent distances and turning angles. The result is a consolidated spatial representation which is still a graph, but whose local metric labels are globally optimized to match the available egomotion measurements. We show that the embedded graph is consistent with human behavior in a (virtual) non-Euclidean environment and argue that it is a natural consequence of the optimal integration of repeated local measurements over time. [ABSTRACT FROM AUTHOR]

In an effort to gauge the global pandemic's impact on social thoughts and behavior, it is important to answer the following questions: (1) What kinds of topics are individuals and groups vocalizing in relation to the pandemic? (2) Are there any noticeable topic trends and if so how do these topics change over time and in response to major events? In this paper, through the advanced Sequential Latent Dirichlet Allocation model, we identified twelve of the most popular topics present in a Twitter dataset collected over the period spanning April 3rd to April 13th, 2020 in the United States and discussed their growth and changes over time. These topics were both robust, in that they covered specific domains, not simply events, and dynamic, in that they were able to change over time in response to rising trends in our dataset. They spanned politics, healthcare, community, and the economy, and experienced macro-level growth over time, while also exhibiting micro-level changes in topic composition. Our approach differentiated itself in both scale and scope to study the emerging topics concerning COVID-19 at a scale that few works have been able to achieve. We contributed to the cross-sectional field of urban studies and big data. Whereas we are optimistic towards the future, we also understand that this is an unprecedented time that will have lasting impacts on individuals and society at large, impacting not only the economy or geo-politics, but human behavior and psychology. Therefore, in more ways than one, this research is just beginning to scratch the surface of what will be a concerted research effort into studying the history and repercussions of COVID-19. [ABSTRACT FROM AUTHOR]

COVID-19 is a global pandemic threatening the lives and livelihood of millions of people across the world. Due to its novelty and quick spread, scientists have had difficulty in creating accurate forecasts for this disease. In part, this is due to variation in human behavior and environmental factors that impact disease propagation. This is especially true for regionally specific predictive models due to either limited case histories or other unique factors characterizing the region. This paper employs both supervised and unsupervised methods to identify the critical county-level demographic, mobility, weather, medical capacity, and health related county-level factors for studying COVID-19 propagation prior to the widespread availability of a vaccine. We use this feature subspace to aggregate counties into meaningful clusters to support more refined disease analysis efforts. [ABSTRACT FROM AUTHOR]

Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19. This paper presents an approach that combines a well-established approach from transportation modelling that uses person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of different room sizes, air exchange rates, disease import, changed activity participation rates over time (coming from mobility data), masks, indoors vs. outdoors leisure activities, and of contact tracing. It is validated against the infection dynamics in Berlin (Germany). The model can be used to understand the contributions of different activity types to the infection dynamics over time. It predicts the effects of contact reductions, school closures/vacations, masks, or the effect of moving leisure activities from outdoors to indoors in fall, and is thus able to quantitatively predict the consequences of interventions. It is shown that these effects are best given as additive changes of the reproduction number R. The model also explains why contact reductions have decreasing marginal returns, i.e. the first 50% of contact reductions have considerably more effect than the second 50%. Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, or consequences of wearing masks in certain situations. The results can be used to inform political decisions. [ABSTRACT FROM AUTHOR]

Humans consider the parameters linked to movement goal during reaching to adjust their control strategy online. Indeed, rapid changes in target structure or disturbances interfering with their initial plan elicit rapid changes in behavior. Here, we hypothesize that these changes could result from the continuous use of a decision variable combining motor and cognitive components. We combine an optimal feedback controller with a real-time evaluation of the expected cost-to-go, which considers target- and movement-related costs, in a common theoretical framework. This model reproduces human behaviors in presence of changes in the target structure occurring during movement and of online decisions to flexibly change target following external perturbations. It also predicts that the time taken to decide to select a novel goal after a perturbation depends on the amplitude of the disturbance and on the rewards of the different options, which is a direct result of the continuous monitoring of the cost-to-go. We show that this result was present in our previously collected dataset. Together our developments point towards a continuous evaluation of the cost-to-go during reaching to update control online and make efficient decisions about movement goal. Author summary: The way humans perform reaching movements is compatible with models considering that they result from the minimization of a task-related cost function. However, these models typically assume a cost function that does not change within movement, which is incompatible with experimental findings highlighting humans' ability to adjust reaching control online and change target flexibly. We hypothesized that this ability relied on continuous evaluation of the cost-to-go, which integrates task- and body-related information. We show that this model can optimally select and adjust control during movement in a way that reproduces human behavior in a set of tasks involving change in cost function and change in goal target. Our model predicted that decision-time to change target must be postponed when limb displacements and alternative rewards are smaller, which was borne out in our previous experimental dataset. To conclude, our model explains dynamic updates in reach control and suggests the cost-to-go as decision variable linking decision-making and motor control. [ABSTRACT FROM AUTHOR]

Adapting laboratory psycholinguistic methods to fieldwork contexts can be fraught with difficulties. However, successful implementation of such methods in the field enhances our ability to learn the true extent and limitations of human behavior. This paper reports two attempts to run word learning experiments with the small community of Nungon speakers in Towet village in the Saruwaged Mountains, Papua New Guinea. A first attempt involved running a cross-situational task in which word-object pairings were presented ambiguously in each trial, and an explicit word learning task in which pairings were presented explicitly, or unambiguously, in each trial. While this quickly garnered a respectable 34 participants over the course of a week, it yielded null results, with many participants appearing to show simple patterned responses at test. We interpreted the null result as possibly reflecting the unfamiliarity of both the task and the laptop-based presentation mode. In Experiment 2, we made several adjustments to the explicit word learning task in an attempt to provide clearer instructions, reduce cognitive load, and frame the study within a real-world context. During a second 11-day stay in the village, 34 participants completed this modified task and demonstrated clear evidence of word learning. With this success serving as a future guide for researchers, our experiences show that it may require multiple attempts, even by experienced fieldworkers familiar with the target community, to successfully adapt experiments to a field setting. [ABSTRACT FROM AUTHOR]

In energy systems' economic models, people's behaviour is often underestimated, and they are generally unaware of how habits impact energy efficiency. Improving efficiency is challenging, and recommendations alone may not be sufficient. Changing behaviour requires understanding the direct impact of needs and habits on energy efficiency. This research introduces a methodology that retrieves human expert knowledge from four key aspects of the current energy transition: everyday appliances, buildings, mobility, flexibility, and energy efficiency. The aim is to examine the causal relationship between energy consumption and human behaviour, gaining a deeper understanding of the links among the factors that drive final energy consumers to change habits through the adoption of energy-saving measures. Working in collaboration with expert panels, this study provides a methodology for extracting expert human knowledge based on a set of future energy transition scenarios aligned with the achievement of the Paris Agreement, a taxonomy of 32 factors that have a strong influence on households' investment decisions, and the results of a survey that characterises the European population through the 32-factor taxonomy and some socioeconomic conditions. In addition, the survey included a sample of the Latin American population to analyse how socioeconomic conditions (region, education, gender, etc.) influence the prioritisation of these factors. We discuss the high priority given to competence and autonomy over financial factors by inhabitants of the European Union residential sector. We provide an analysis of the factors through which other similar projects are focused and on which we converge. In addition, we contribute by presenting the hierarchy of priorities assigned by people. This highlights the importance for policymakers to take these aspects seriously when implementing energy policy interventions that go beyond purely financial measures and fiscal incentives. [ABSTRACT FROM AUTHOR]

Both the basic human values approach and the economic preference approach have been developed and applied to represent fundamental drivers of human behavior in various domains by measuring people's underlying preferences and motivations. Both of them have been used, however, in isolation from each other, the former primarily in social psychology studies and the latter mainly in economic studies. But how similar are they? Finding that they differ may suggest that combining them to explain human behavior might be beneficial. To the best of our knowledge, only a few studies have attempted to explore and empirically examine the theoretical and empirical link between variables in both approaches. The current study tries to fill this gap by examining relations between basic human values and major economic preferences. We examine the associations between the values of self-transcendence, self-enhancement, openness to change and conservation, and the economic preferences of risk aversion (or seeking), altruism, trust, and positive and negative reciprocity. We propose mechanisms as to how they may be associated with one another. For example, we expect an association between conservation and risk aversion as both are motivated by attributing importance to stability and the status quo, or between self-transcendence and altruism, as both are motivated by concern for others. For the empirical analysis we employed convenience samples collected in Poland and Germany. Results in both samples support our expectations: several values and economic preferences are linked in theoretically predictable ways, but only to a weak or moderate extent. We conclude that they are not mutually exclusive but may rather be complementary, and therefore likely both relevant for investigations into explaining behavior. [ABSTRACT FROM AUTHOR]

Understanding the influence of emotions on social interactions is important for a global understanding of the dynamics of human behavior. In this study, we investigated the interplay between emotions, spontaneous approach or avoidance tendencies, and the regulation of interpersonal distance. Fifty-seven healthy adults participated in a three-part experiment involving exposure to approaching or withdrawing emotional faces (neutral, happy, sad, fearful, disgusted, angry). The sequence began with an initial computerized stop-distance task, followed by a postural task in which participants' approach or avoidance tendencies were quantified via center of pressure (CoP-Y) displacements on a force platform, and concluded with a final computerized stop-distance task. Our findings revealed a gradient in postural responses, with the most forward CoP-Y displacements for neutral and happy faces, indicative of approach tendencies. These were followed by lesser forward displacements for sad and fearful faces, and most pronounced backward displacements for disgusted and angry faces, indicating avoidance. Furthermore, we observed modulations in participants' preferred interpersonal distance based on emotional cues, with neutral and happy faces associated with shorter distances, and disgusted and angry faces linked to larger distances. Despite these similar results, no direct correlation was found between CoP-Y and preferred interpersonal distance, underscoring a dissociation between spontaneous and voluntary social behaviors. These results contribute to a better understanding of how emotional expressions shape social interactions and underscore the importance of considering emotional cues, postural action tendencies, and interpersonal distance in facilitating successful social interactions. [ABSTRACT FROM AUTHOR]

Humans excel at predictively synchronizing their behavior with external rhythms, as in dance or music performance. The neural processes underlying rhythmic inferences are debated: whether predictive perception relies on high-level generative models or whether it can readily be implemented locally by hard-coded intrinsic oscillators synchronizing to rhythmic input remains unclear and different underlying computational mechanisms have been proposed. Here we explore human perception for tone sequences with some temporal regularity at varying rates, but with considerable variability. Next, using a dynamical systems perspective, we successfully model the participants behavior using an adaptive frequency oscillator which adjusts its spontaneous frequency based on the rate of stimuli. This model better reflects human behavior than a canonical nonlinear oscillator and a predictive ramping model–both widely used for temporal estimation and prediction–and demonstrate that the classical distinction between absolute and relative computational mechanisms can be unified under this framework. In addition, we show that neural oscillators may constitute hard-coded physiological priors–in a Bayesian sense–that reduce temporal uncertainty and facilitate the predictive processing of noisy rhythms. Together, the results show that adaptive oscillators provide an elegant and biologically plausible means to subserve rhythmic inference, reconciling previously incompatible frameworks for temporal inferential processes. Author summary: Real-life sequence processing requires prediction in uncertain contexts. Temporal predictions are thought to be manifest by a dual timing mechanism: a relative timing mechanism measuring timing relative to an expected rhythm, and an absolute timing mechanism which measures absolute interval differences. We show that these mechanisms can be unified into a Bayesian Framework, reformulating the two components into the combination of prior and sensory measurement, and demonstrate that this framework can be implemented by an adaptive neural oscillator. [ABSTRACT FROM AUTHOR]

Brain models typically focus either on low-level biological detail or on qualitative behavioral effects. In contrast, we present a biologically-plausible spiking-neuron model of associative learning and recognition that accounts for both human behavior and low-level brain activity across the whole task. Based on cognitive theories and insights from machine-learning analyses of M/EEG data, the model proceeds through five processing stages: stimulus encoding, familiarity judgement, associative retrieval, decision making, and motor response. The results matched human response times and source-localized MEG data in occipital, temporal, prefrontal, and precentral brain regions; as well as a classic fMRI effect in prefrontal cortex. This required two main conceptual advances: a basal-ganglia-thalamus action-selection system that relies on brief thalamic pulses to change the functional connectivity of the cortex, and a new unsupervised learning rule that causes very strong pattern separation in the hippocampus. The resulting model shows how low-level brain activity can result in goal-directed cognitive behavior in humans. Author summary: Despite large research enterprises in the past decade, it is still unknown how our cognitive system is implemented by the immense complexity of the neurons in our brains. Here, we present a large-scale computational model of the human brain that explains how low-level neural activity can lead to functional, goal-directed behavior. Previous models typically either focused on low-level biological detail or on part-task, qualitative behavioral effects. In contrast, the current model is biologically plausible through the use of spiking neurons, and accounts both for human behavior and neuroimaging data across a whole task. The model's mechanisms are based on detailed machine-learning analyses of neuroimaging data, as well as on cognitive theories of associative recognition. [ABSTRACT FROM AUTHOR]

As information and communication technology advances rapidly, real-time live online broadcasting has emerged as a novel social media platform. In particular, live online broadcasts have gained widespread popularity among audiences. However, this process can cause environmental problems. When audiences imitate live content and perform similar field activities, it can have a negative effect on the environment. In this study, an extended theory of planned behavior (TPB) was used to explore how online live broadcasts relate to environmental damage from the perspective of human behavior. A total of 603 valid responses were collected from a questionnaire survey, and a regression analysis was conducted to verify the hypotheses. The findings showed that the TPB can be applied to account for the formation mechanism of behavioral intention of field activities caused by online live broadcasts. The mediating effect of imitation was verified using the above relationship. These findings are expected to provide a practical reference for the control of online live broadcast content and guidance on public environmental behavior. [ABSTRACT FROM AUTHOR]

Human behavior emerges from planning over elaborate decompositions of tasks into goals, subgoals, and low-level actions. How are these decompositions created and used? Here, we propose and evaluate a normative framework for task decomposition based on the simple idea that people decompose tasks to reduce the overall cost of planning while maintaining task performance. Analyzing 11,117 distinct graph-structured planning tasks, we find that our framework justifies several existing heuristics for task decomposition and makes predictions that can be distinguished from two alternative normative accounts. We report a behavioral study of task decomposition (N = 806) that uses 30 randomly sampled graphs, a larger and more diverse set than that of any previous behavioral study on this topic. We find that human responses are more consistent with our framework for task decomposition than alternative normative accounts and are most consistent with a heuristic—betweenness centrality—that is justified by our approach. Taken together, our results suggest the computational cost of planning is a key principle guiding the intelligent structuring of goal-directed behavior. Author summary: People routinely solve complex tasks by solving simpler subtasks—that is, they use a task decomposition. For example, to accomplish the task of cooking dinner, you might start by choosing a recipe—and in order to choose a recipe, you might start by opening a cookbook. But how do people identify task decompositions? A longstanding challenge for cognitive science has been to describe, explain, and predict human task decomposition strategies in terms of more fundamental computational principles. To address this challenge, we propose a model that formalizes how specific task decomposition strategies reflect rational trade-offs between the value of a solution and the cost of planning. Our account allows us to rationalize previously identified heuristic strategies, understand existing normative proposals within a unified theoretical framework, and explain human responses in a large-scale experiment. [ABSTRACT FROM AUTHOR]

The spread of false and misleading information on social media is largely dependent on human action. Understanding the factors that lead social media users to amplify (or indeed intervene in) the spread of this content is an ongoing challenge. Prior research suggests that users are not only more likely to interact with misinformation that supports their ideology or their political beliefs, they may also feel it is more acceptable to spread. However, less is known about the influence of newer, issue-specific beliefs. Two online studies explored the relationship between the degree of belief-consistency of disinformation on users' moral judgements and intentions to spread disinformation further. Four disinformation narratives were presented: disinformation that supported or undermined the UK Government's handling of COVID-19, and disinformation that minimised or maximised the perceived risk of COVID-19. A novel scale for measuring intentions to contribute to the spread of social media content was also used in study 2. Participants reported greater likelihood of spreading false material that was consistent with their beliefs. More lenient moral judgements related to the degree of belief-consistency with disinformation, even when participants were aware the material was false or misleading. These moral judgements partially mediated the relationship between belief-consistency of content and intentions to spread it further on social media. While people are concerned about the spread of disinformation generally, they may evaluate belief-consistent disinformation differently from others in a way that permits them to spread it further. As social media platforms prioritise the ordering of feeds based on personal relevance, there is a risk that users could be being presented with disinformation that they are more tolerant of. [ABSTRACT FROM AUTHOR]

Using a reinforcement-learning algorithm, we model an agent-based simulation of a public goods game with endogenous punishment institutions. We propose an outcome-based model of social preferences that determines the agent's utility, contribution, and voting behavior during the learning procedure. Comparing our simulation to experimental evidence, we find that the model can replicate human behavior and we can explain the underlying motives of this behavior. We argue that our approach can be generalized to more complex simulations of human behavior. [ABSTRACT FROM AUTHOR]

Events profoundly influence human-environment interactions. Through repetition, some events manifest and amplify collective behavioral traits, which significantly affects landscapes and their use, meaning, and value. However, the majority of research on reaction to events focuses on case studies, based on spatial subsets of data. This makes it difficult to put observations into context and to isolate sources of noise or bias found in data. As a result, inclusion of perceived aesthetic values, for example, in cultural ecosystem services, as a means to protect and develop landscapes, remains problematic. In this work, we focus on human behavior worldwide by exploring global reactions to sunset and sunrise using two datasets collected from Instagram and Flickr. By focusing on the consistency and reproducibility of results across these datasets, our goal is to contribute to the development of more robust methods for identifying landscape preference using geo-social media data, while also exploring motivations for photographing these particular events. Based on a four facet context model, reactions to sunset and sunrise are explored for Where, Who, What, and When. We further compare reactions across different groups, with the aim of quantifying differences in behavior and information spread. Our results suggest that a balanced assessment of landscape preference across different regions and datasets is possible, which strengthens representativity and exploring the How and Why in particular event contexts. The process of analysis is fully documented, allowing transparent replication and adoption to other events or datasets. [ABSTRACT FROM AUTHOR]

Malaria vector control interventions in Sumba, Indonesia, have not been able to eliminate malaria. Human drivers of exposure to Anopheles bites were investigated as part of a larger clinical trial evaluating the impact of a spatial repellent product on malaria incidence. Human behavioral observations (HBOs) evaluating temporal and spatial presence, sleeping behaviors, and insecticide treated net (ITN) use, were collected parallel to entomological collections—indoor and outdoor human landing catches (HLCs), and house hold surveys. Data demonstrates that mosquito access to humans, enabled by structurally open houses, is evident by the similar entomological landing rates both inside and outside households. The presence of animals inside houses was associated with increased mosquito entry–however, the number of humans present inside houses was not related to increased mosquito landing. Analyzing mosquito landing rates with human behavior data enables the spatial and temporal estimation of exposure to Anopheles bites, accounting for intervention (ITN) presence and usage. Human behavior adjusted exposure to Anopheles bites was found to be highest in the early in the evening, but continued at lower levels throughout the night. Over the night, most exposure (53%) occurred when people were indoors and not under the protection of nets (asleep or awake) followed by exposure outside (44%). Characterized gaps in protection are outdoor exposure as well as exposure indoors–when awake, and when asleep and not using ITNs. Interestingly, in the primary trial, even though there was not a significant impact of the spatial repellent on vector biting rates by themselves (16%), when factoring in human behavior, there was approximately 28% less exposure in the intervention arm than in the placebo arm. The treated arm had less human behavior adjusted bites in all spaces evaluated though there was proportionally higher exposure indoors. This analysis points to the importance of using HBOs both towards understanding gaps in protection as well as how interventions are evaluated. To mitigate ongoing transmission, understanding context specific spatial and temporal exposure based on the interactions of vectors, humans and interventions would be vital for a directed evidence-based control or elimination strategy. [ABSTRACT FROM AUTHOR]

Excavations at Abu Hureyra, Syria, during the 1970s exposed a long sequence of occupation spanning the transition from hunting-and-gathering to agriculture. Dung spherulites preserved within curated flotation samples from Epipalaeolithic (ca. 13,300–11,400 calBP) and Neolithic (ca. 10,600–7,800 calBP) occupations are examined here alongside archaeological, archaeobotanical, and zooarchaeological data to consider animal management, fuel selection, and various uses of dung. Spherulites were present throughout the entire sequence in varying concentrations. Using a new method to quantify spherulites, exclusion criteria were developed to eliminate samples possibly contaminated with modern dung, strengthening observations of ancient human behavior. Darkened spherulites within an Epipalaeolithic 1B firepit (12,800–12,300 calBP) indicate burning between 500–700°C, documenting early use of dung fuel by hunter-gatherers as a supplement to wood, coeval with a dramatic shift to rectilinear architecture, increasing proportions of wild sheep and aurochsen, reduced emphasis on small game, and elevated dung concentrations immediately outside the 1B dwelling. Combined, these observations suggest that small numbers of live animals (possibly wild sheep) were tended on-site by Epipalaeolithic hunter-gatherers to supplement gazelle hunting, raising the question of whether early experiments in animal management emerged contemporaneously with, or pre-date, cultivation. Dung was used to prepare plaster floors during the Neolithic and continued to be burned as a supplemental fuel, indicating that spherulites were deposited via multiple human- and animal-related pathways. This has important implications for interpretations of archaeobotanical assemblages across the region. Spherulite concentrations dropped abruptly during Neolithic 2B (9,300–8,000 calBP) and 2C (8,000–7,800 calBP), when sheep/goat herding surpassed gazelle hunting, possibly corresponding with movement of animals away from the site as herd sizes increased. As hunter-gatherers at Abu Hureyra began interacting with wild taxa in different ways, they set in motion a remarkable transformation in the ways people interacted with animals, plants, and their environment. [ABSTRACT FROM AUTHOR]

In order to accurately parse the visual scene into distinct surfaces, it is essential to determine whether a local luminance edge is caused by a boundary between two surfaces or a shadow cast across a single surface. Previous studies have demonstrated that local chromatic cues may help to distinguish edges caused by shadows from those caused by surface boundaries, but the information potentially available in local achromatic cues like contrast, texture, and penumbral blur remains poorly understood. In this study, we develop and analyze a large database of hand-labeled achromatic shadow edges to better understand what image properties distinguish them from occlusion edges. We find that both the highest contrast as well as the lowest contrast edges are more likely to be occlusions than shadows, extending previous observations based on a more limited image set. We also find that contrast cues alone can reliably distinguish the two edge categories with nearly 70% accuracy at 40x40 resolution. Logistic regression on a Gabor Filter bank (GFB) modeling a population of V1 simple cells separates the categories with nearly 80% accuracy, and furthermore exhibits tuning to penumbral blur. A Filter-Rectify Filter (FRF) style neural network extending the GFB model performed at better than 80% accuracy, and exhibited blur tuning and greater sensitivity to texture differences. We compare human performance on our edge classification task to that of the FRF and GFB models, finding the best human observers attaining the same performance as the machine classifiers. Several analyses demonstrate both classifiers exhibit significant positive correlation with human behavior, although we find a slightly better agreement on an image-by-image basis between human performance and the FRF model than the GFB model, suggesting an important role for texture. Author summary: Distinguishing edges caused by changes in illumination from edges caused by surface boundaries is an essential computation for accurately parsing the visual scene. Previous psychophysical investigations examining the utility of various locally available cues to classify edges as shadows or surface boundaries have primarily focused on color, as surface boundaries often give rise to more significant change in color than shadows. However, even in grayscale images we can readily distinguish shadows from surface boundaries, suggesting an important role for achromatic cues in addition to color. We demonstrate using statistical analysis of natural shadow and surface boundary edges that locally available achromatic cues can be exploited by machine classifiers to reliably distinguish these two edge categories. These classifiers exhibit sensitivity to blur and local texture differences, and exhibit reasonably good agreement with humans classifying edges as shadows or surface boundaries. As trichromatic vision is relatively rare in the animal kingdom, our work suggests how organisms lacking rich color vision can still exploit other cues to avoid mistaking illumination changes for surface changes. [ABSTRACT FROM AUTHOR]

Inter-personal violence (whether intra- or inter-group) is a pervasive yet highly variable human behavior. Evolutionary anthropologists suggest that the abundance and distribution of resources play an important role in influencing differences in rates of violence, with implications for how resource conditions structure adaptive payoffs. Here, we assess whether differences in large-scale ecological conditions explain variability in levels of inter-personal human violence. Model results reveal a significant relationship between resource conditions and violence that is mediated by subsistence economy. Specifically, we find that interpersonal violence is highest: (1) among foragers and mixed forager/farmers (horticulturalists) in productive, homogeneous environments, and (2) among agriculturalists in unproductive, heterogeneous environments. We argue that the trend reversal between foragers and agriculturalists represents differing competitive pathways to enhanced reproductive success. These alternative pathways may be driven by features of subsistence (i.e., surplus, storage, mobility, privatization), in which foragers use violence to directly acquire fitness-linked social payoffs (i.e., status, mating opportunities, alliances), and agriculturalists use violence to acquire material resources that can be transformed into social payoffs. We suggest that as societies transition from immediate return economies (e.g., foragers) to delayed return economies (e.g., agriculturalists) material resources become an increasingly important adaptive payoff for inter-personal, especially inter-group, violence. [ABSTRACT FROM AUTHOR]

While much effort has gone into building predictive models of the COVID-19 pandemic, some have argued that early exponential growth combined with the stochastic nature of epidemics make the long-term prediction of contagion trajectories impossible. We conduct two complementary studies to assess model features supporting better long-term predictions. First, we leverage the diverse models contributing to the CDC repository of COVID-19 USA death projections to identify factors associated with prediction accuracy across different projection horizons. We find that better long-term predictions correlate with: (1) capturing the physics of transmission (instead of using black-box models); (2) projecting human behavioral reactions to an evolving pandemic; and (3) resetting state variables to account for randomness not captured in the model before starting projection. Second, we introduce a very simple model, SEIRb, that incorporates these features, and few other nuances, offers informative predictions for as far as 20-weeks ahead, with accuracy comparable with the best models in the CDC set. Key to the long-term predictive power of multi-wave COVID-19 trajectories is capturing behavioral responses endogenously: balancing feedbacks where the perceived risk of death continuously changes transmission rates through the adoption and relaxation of various Non-Pharmaceutical Interventions (NPIs). Author summary: Long-term projections of COVID-19 trajectory have been used to inform various policies and decisions such as planning intensive care capacity, selecting clinical trial locations, and deciding on economic policy packages. However, these types of long-term forecasts are challenging as epidemics are complex: they include reinforcing contagion mechanisms that create exponential growth, are moderated by randomness in environmental and social determinants of transmission, and are subject to endogenous human responses to evolving risk perceptions. In this study we take a step towards systematically examining the modeling choices that regulate COVID-19 forecasting accuracy in two complementary studies. First, we leverage the diverse models contributing to the CDC repository of COVID-19 USA death projections to identify factors associated with prediction accuracy across different projection horizons. Second, we design a very simple forecasting model that only incorporates the key features identified in the first study, and show that the long-term prediction accuracy of this model is comparable with the best models in the CDC set. We conclude that forecasting models responding to future epidemics would benefit from starting small: first incorporating key mechanistic features, important behavioral feedbacks, and simple state-resetting approaches and then expanding to capture other features. Our study shows that the key to the long-term predictive power of epidemic models is an endogenous representation of human behavior in interaction with the evolving epidemic. [ABSTRACT FROM AUTHOR]

The advancement in technology especially in the field of artificial intelligence has opened up novel and robust ways to reanalyze the many aspects of human emotional behavior. One of such behavioral studies is the cultural impact on the expression and perception of human emotions. In-group advantage makes it easy for the people of the same cultural group to perceive each other's emotions accurately. The goal of this research is to re-investigate human behavior regarding expression and perception of emotions in speech. The theoretical basis of this research is grounded on the dialect theory of emotions. For the purpose of this study, six datasets of audio speeches have been considered. The participants of these datasets belong to six different cultural areas. A fully automated, machine learning-based framework i.e. Support Vector Machine (SVM) is used to carry out this study. The overall emotion perception for all six cultural groups supports in-group advantage, whereas emotion wise analysis partially supports the In-group advantage. [ABSTRACT FROM AUTHOR]

Identifying mechanisms able to sustain costly cooperation among self-interested agents is a central problem across social and biological sciences. One possible solution is peer punishment: when agents have an opportunity to sanction defectors, classical behavioral experiments suggest that cooperation can take root. Overlooked from standard experimental designs, however, is the fact that real-world human punishment—the administration of justice—is intrinsically noisy. Here we show that stochastic punishment falls short of sustaining cooperation in the repeated public good game. As punishment noise increases, we find that contributions decrease and punishment efforts intensify, resulting in a 45% drop in gains compared to a noiseless control. Moreover, we observe that uncertainty causes a rise in antisocial punishment, a mutually harmful behavior previously associated with societies with a weak rule of law. Our approach brings to light challenges to cooperation that cannot be explained by economic rationality and strengthens the case for further investigations of the effect of noise—and not just bias—on human behavior. [ABSTRACT FROM AUTHOR]

Toxoplasmosis gondii exposure has been linked to increased impulsivity and risky behaviors, which has implications for eating behavior. Impulsivity and risk tolerance is known to be related with worse diets and a higher chance of obesity. There is little known, however, about the independent link between Toxoplasma gondii (T. gondii) exposure and diet-related outcomes. Using linear and quantile regression, we estimated the relationship between T. gondii exposure and BMI, total energy intake (kcal), and diet quality as measured by the Health Eating Index-2015 (HEI) among 9,853 adults from the 2009–2014 National Health and Nutrition Examination Survey. Previous studies have shown different behavioral responses to T. gondii infection among males and females, and socioeconomic factors are also likely to be important as both T. gondii and poor diet are more prevalent among U.S. populations in poverty. We therefore measured the associations between T. gondii and diet-related outcomes separately for men and women and for respondents in poverty. Among females <200% of the federal poverty level Toxoplasmosis gondii exposure was associated with a higher BMI by 2.0 units (95% CI [0.22, 3.83]) at median BMI and a lower HEI by 5.05 units (95% CI [-7.87, -2.24]) at the 25th percentile of HEI. Stronger associations were found at higher levels of BMI and worse diet quality among females. No associations were found among males. Through a detailed investigation of mechanisms, we were able to rule out T. gondii exposure from cat ownership, differing amounts of meat, and drinking water source as potential confounding factors; environmental exposure to T. gondii as well as changes in human behavior due to parasitic infection remain primary mechanisms. Author summary: Toxoplasmosis gondii (T. gondii) is a parasite that infects over 10 percent of the US population. T. gondii infection can cause serious health problems for some people, but most infections remain undiagnosed and subclinical. When an individual is infected, T. gondii can chronically reside in muscle and central nervous system (including brain) tissue. Previous studies have found that individuals with prior exposure to T. gondii may engage in more risky and impulsive behaviors, and risk tolerance and impulsivity may be related with individual's diet. Our study examines whether individuals with T. gondii exposure have higher body mass index (BMI) and worse diets. We further discuss and test for alternative explanations that prevent us from establishing a causal relationship between T. gondii and BMI/diet. Overall, our results show that T. gondii exposure is related with higher BMI and worse diets among lower-income females in the US. Our results uncover a novel correlate of BMI and diets, and suggest the importance of investigating the broader public health impacts of chronic T. gondii infection. [ABSTRACT FROM AUTHOR]