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In his article 'Origins of the Problem of Health-related Behaviours: A Genealogical Study', David Armstrong claims that the idea about' changeable behaviour is a relatively recent phenomenon'. In this paper it is argued that this conclusion is not valid. One concern is whether his sources sufficiently document the ideas. Another concern is whether David Armstrong overlooked the focus on health-promoting and health-damaging behaviours by including an insufficient number of words or terms in his search criteria. In this paper it is demonstrated that health-related behaviours have been a concern for doctors throughout history. It is not new as David Armstrong and proponents of the New Public Health claim. [ABSTRACT FROM AUTHOR]

Evolution’s ability to find innovative phenotypes is an important ingredient in the emergence of complexity in nature. A key factor in this capability is evolvability, or the propensity towards phenotypic variation. Numerous explanations for the origins of evolvability have been proposed, often differing in the role that they attribute to adaptive processes. To provide a new perspective on these explanations, experiments in this paper simulate evolution in gene regulatory networks, revealing that the type of evolvability in question significantly impacts the dynamics that follow. In particular, while adaptive processes result in evolvable individuals, processes that are either neutral or that explicitly encourage divergence result in evolvable populations. Furthermore, evolvability at the population level proves the most critical factor in the production of evolutionary innovations, suggesting that nonadaptive mechanisms are the most promising avenue for investigating and understanding evolvability. These results reconcile a large body of work across biology and inform attempts to reproduce evolvability in artificial settings. [ABSTRACT FROM PUBLISHER]

In recent years a number of public health, prevention and disease management strategies have emerged that depend on changing health-related behaviours. The definition of those behaviours, indeed of the very idea of behaviour, remains unchallenged in these initiatives, as behaviour is a taken-for-granted concept. Yet the idea of a changeable behaviour is a relatively recent phenomenon and the aim of this paper is to map its emergence and transformation over the last century. Its origins are shown to lie in the first half of the 20th century when it was derived from the ideas of conduct and movement. From mid-century onwards, it has been increasingly construed as being underpinned by a sense of agency and as a legitimate target for healthcare interventions. Finally, in the 21st century it has become stabilized as a core dimension of health and illness. [ABSTRACT FROM AUTHOR]

In 'Sacrifice and the Transformation of the Animal Body into a Scientific Object' Michael Lynch (1988) explores how the animal body is transformed into a scientific object in the laboratory. How did the laboratory become a (relatively) closed space in which scientists, the experts, were delegated the task of negotiating and transforming the interpretative sense of the animal - from sentient beings to analytic objects - as tools in a scientific machinery? By exploring a parliamentary controversy on experimental medicine at the turn of the 20th century I argue that this depended on a reworking of the status of the animal body, as well as the status of the laboratory. Crucial to this was social theory; specifically, utilitarian reasoning. Thus, what we need to study - this paper argues - is not simply the ways in which the practices of annual experimentation were met with opposition and critiques, but also how these practices came to be culturally and politically accepted, and what this implied for science - society relations. In analysing this controversy, the author attends to recent turn to politics in STS and argue for the significance of studying conventional political sites such as 'Parliament' and the role that social theory plays in renegotiating and remaking sites and objects. [ABSTRACT FROM AUTHOR]

The dynamical systems approach in the cognitive and behavioral sciences studies how systems made of many coupled components across brain, body, and environment self-organize to generate behavior. This approach has mostly focused on models of single actions and has not addressed how a dynamical system can engage in multiple different directed actions. In this paper, we introduce a family of artificial life models that demonstrate how dynamical agents can engage in multiple different actions and autonomously switch between them. These described agents engage in a food foraging task, and are driven by both internal, metabolic variables and external, sensory variables. The analysis of one of these agents demonstrates how different actions can arise through transient modes of sensorimotor coordination, in which a subset of the available sensors and effectors become engaged while others are ignored. Transitions between actions are analyzed and shown to correspond to rapid movements through the agent’s state space. In these transitions, some of the previously controlling sensors and effectors disengage, and new sets of sensors and effectors are engaged. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel controller for safe, efficient, and intuitive robot-to-human object handovers and a set of experiments to evaluate user responses to the robot’s handover behavior. The controller enables a robot to mimic human behavior by actively regulating the applied grip force according to the measured load force during a handover. We provide an implementation of the controller on a Willow Garage PR2 robot, demonstrating the feasibility of realizing our design on robots with basic sensor/actuator capabilities. A user study comparing four variations of our controller shows that our design yields both human-like and human-preferred object handovers. [ABSTRACT FROM AUTHOR]

In the US, blood donors face a variety of restrictions that leave many people excluded entirely from the donor pool. This paper explores the specific circumstances and meanings surrounding the donor ban on Men-who-have-Sex-with-Men (MSM). The ban on MSM is one of the few existing donor guidelines to receive considerable criticism on grounds that it effectively prohibits any sexually active gay man from donating blood and thus discriminates against gays. Due in part to these questions of fairness, the Blood Products Advisory Committee (BPAC) of the Food and Drug Administration (FDA) met to reconsider the decades-old policy, first in 1997 and again in 2000. The FDA asked its advisory committee to address the efficacy and utility of the MSM ban in light of technological developments in blood-banking, epidemiological data on the spread of HIV, and mounting pressures from gay rights and blood-banking organizations to update the policy. Through a detailed reading of meeting and conference transcripts that took place between 1997 and 2000, I argue that 'MSM' became a contested definitional category during the FDA's reappraisal of the policy. During and between the Committee's discussions, presenters and experts debated the differences between sexual behavior and sexual identity in relation to HIV and, eventually, HHV-8, a virus known to cause Kaposi's sarcoma in immuno-suppressed individuals. I argue that the underlying flexibility in the meanings behind the term 'MSM' allowed Committee members, in the end, to retract their more nuanced discussions of human behavior and HIV and to uphold the contested policy. Finally, I suggest how the debates surrounding the MSM donor ban can help us to better understand the place of sexuality in discussions and claims of biopolitical citizenship in early 21st-century America. [ABSTRACT FROM AUTHOR]

Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. This paper describes how to extract a person's activities and significant places from traces of GPS data. The system uses hierarchically structured conditional random fields to generate a consistent model of a person's activities and places. In contrast to existing techniques, this approach takes the high-level context into account in order to detect the significant places of a person. Experiments show significant improvements over existing techniques. Furthermore, they indicate that the proposed system is able to robustly estimate a person `s activities using a model that is trained from data collected by other persons. [ABSTRACT FROM AUTHOR]

Today aging of society is becoming increasingly important. Many people want to live at home as long as possible. Very often this leads to accidents at home, which are unnoticed for a long time and therefore cause severe complications. We propose a cyber-physical system consisting of sensors for motion, light, temperature, and so on, which monitors the behavior of elderly people but is non-invasive in the sense that the persons are not observed by a camera and must not wear certain sensors on their body. Unusual behavior or accidents are registered in-time by a change point detection algorithm based on Markov chains, which does not store any data with the exception of the last datum, so that also the integrity of the elderly people is preserved. For verification of this algorithm, a cyber-physical test environment has been programmed which allows to simulate human common and uncommon behavior in house during day and night. This simulation environment is based on behavior trees and decision theory. The verification results suggest that the implemented cyber-physical system for change point and anomaly detection could be successfully used in the real environment of elderly people to give them help in-time when an accident occurs. [ABSTRACT FROM AUTHOR]

We address the problem of shaping deformable plastic materials using non-prehensile actions. Shaping plastic objects is challenging, because they are difficult to model and to track visually. We study this problem, by using kinetic sand, a plastic toy material that mimics the physical properties of wet sand. Inspired by a pilot study where humans shape kinetic sand, we define two types of actions: pushing the material from the sides and tapping from above. The chosen actions are executed with a robotic arm using image-based visual servoing. From the current and desired view of the material, we define states based on visual features such as the outer contour shape and the pixel luminosity values. These are mapped to actions, which are repeated iteratively to reduce the image error until convergence is reached. For pushing, we propose three methods for mapping the visual state to an action. These include heuristic methods and a neural network, trained from human actions. We show that it is possible to obtain simple shapes with the kinetic sand, without explicitly modeling the material. Our approach is limited in the types of shapes it can achieve. A richer set of action types and multi-step reasoning is needed to achieve more sophisticated shapes. [ABSTRACT FROM AUTHOR]

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand, and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots, and advanced surveillance systems. This article provides a survey of human motion trajectory prediction. We review, analyze, and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research. [ABSTRACT FROM AUTHOR]

We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies that can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system such as friction coefficients and an object's appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five. We also include a video of our results: https://youtu.be/jwSbzNHGflM. [ABSTRACT FROM AUTHOR]

We present a navigation planning framework for dynamic, multi-agent environments, where no explicit communication takes place among agents. Inspired by the collaborative nature of human navigation, our approach encodes the concept of coordination into an agent's decision making through an inference mechanism about collaborative strategies of collision avoidance. Each such strategy represents a distinct avoidance protocol, prescribing a distinct class of navigation behaviors to agents. We model such classes as equivalence classes of multi-agent path topology, using the formalism of topological braids. This formalism may naturally encode any arbitrarily complex, spatiotemporal, multi-agent behavior, in any environment with any number of agents into a compact representation of dual algebraic and geometric nature. This enables us to construct a probabilistic inference mechanism that predicts the collective strategy of avoidance among multiple agents, based on observation of agents' past behaviors. We incorporate this mechanism into an online planner that enables an agent to understand a multi-agent scene and determine an action that not only contributes progress towards its destination, but also reduction of the uncertainty of other agents regarding the agent's role in the emerging strategy of avoidance. This is achieved by picking actions that compromise between energy efficiency and compliance with everyone's inferred avoidance intentions. We evaluate our approach by comparing against a greedy baseline that only maximizes individual efficiency. Simulation results of statistical significance demonstrate that our planner results in a faster uncertainty decrease that facilitates the decision-making process of co-present agents. The algorithm's performance highlights the importance of topological reasoning in decentralized, multi-agent planning and appears promising for real-world applications in crowded human environments. [ABSTRACT FROM AUTHOR]

Synchronized behaviours are found in various species, among all taxa of live beings. Being synchronized with other individuals is defined by doing the same thing, at the same time and at the same place as others. It is observed within intraspecific groups and dyads. We aim to provide a synthetic overview of what is behavioural synchronization and focus on the adaptive value of such a phenomenon among individuals. Then, as it is observed that some stable groups or dyads consist of individuals from different species, we finally propose to investigate the existence of interspecific behavioural synchronization. [ABSTRACT FROM AUTHOR]

A technique for simultaneous planning and action based on dynamic field theory is presented. The model builds on previous work on representation of sequential behavior as attractors in dynamic neural fields. Here, we demonstrate how chains of competing attractors can be used to represent dynamic plans towards a goal state. The present work can be seen as an addition to a growing body of work that demonstrates the role of dynamic field theory as a bridge between low-level reactive approaches and high-level symbol processing mechanisms. The architecture is evaluated on a set of planning problems using a simulated e-puck robot, including analysis of the system’s behavior in response to noise and temporary blockages of the planned route. The system makes no explicit distinction between planning and execution phases, allowing continuous adaptation of the planned path. The proposed architecture exploits the dynamic field theory property of stability in relation to noise and changes in the environment. The neural dynamics are also exploited such that stay-or-switch action selection emerges where blockage of a planned path occurs; stay until the transient blockage is removed versus switch to an alternative route to the goal. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) in the context of artificial agents is typically used to produce behavioral responses as a function of the reward obtained by interaction with the environment. When the problem consists of learning the shortest path to a goal, it is common to use reward functions yielding a fixed value after each decision, for example a positive value if the target location has been attained and a negative value at each intermediate step. However, this fixed strategy may be overly simplistic for agents to adapt to dynamic environments, in which resources may vary from time to time. By contrast, there is significant evidence that most living beings internally modulate reward value as a function of their context to expand their range of adaptivity. Inspired by the potential of this operation, we present a review of its underlying processes and we introduce a simplified formalization for artificial agents. The performance of this formalism is tested by monitoring the adaptation of an agent endowed with a model of motivated actor–critic, embedded with our formalization of value and constrained by physiological stability, to environments with different resource distribution. Our main result shows that the manner in which reward is internally processed as a function of the agent’s motivational state, strongly influences adaptivity of the behavioral cycles generated and the agent’s physiological stability. [ABSTRACT FROM AUTHOR]

In this article, we present a hierarchical Bayesian framework for multimodal active perception, devised to be emergent, scalable and adaptive. This framework, while not strictly neuromimetic, finds its roots in the role of the dorsal perceptual pathway of the human brain. Its composing models build upon a common spatial configuration that is naturally fitting for the integration of readings from multiple sensors using a Bayesian approach devised in previous work. The framework presented in this article is shown to adequately model human-like active perception behaviours, namely by exhibiting the following desirable properties: high-level behaviour results from low-level interaction of simpler building blocks; seamless integration of additional inputs is allowed by the Bayesian Programming formalism; initial ‘genetic imprint’ of distribution parameters may be changed ‘on the fly’ through parameter manipulation, thus allowing for the implementation of goal-dependent behaviours (i.e. top-down influences). [ABSTRACT FROM PUBLISHER]

Humans have cognitive mechanisms that allow them to keep track of time, represent past events, and simulate the future, but these capacities have intrinsic constraints. Here, we explore the role of material culture as an extension of internal time representations through anthropological and archeological case studies, focusing on Upper Paleolithic material culture. We argue that calendars complement and extend internal time representations, because they enable humans to project past events into the future more accurately than is possible with episodic memory alone, making them one of the factors that significantly improved foraging success during the Upper Paleolithic. We discuss the implications of the epistemic use of material culture for our understanding of the causes of shifts in human behavior during the Upper Paleolithic. [ABSTRACT FROM PUBLISHER]

The ability to learn during continuous and on-line observation would be advantageous for humanoid robots, as it would enable them to learn during co-location and interaction in the human environment. However, when motions are being learned and clustered on-line, there is a trade-off between classification accuracy and the number of training examples, resulting in potential misclassifications both at the motion and hierarchy formation level. This article presents an approach enabling fast on-line incremental learning, combined with an incremental memory consolidation process correcting initial misclassifications and errors in organization, to improve the stability and accuracy of the learned motions, analogous to the memory consolidation process following motor learning observed in humans. Following initial organization, motions are randomly selected for reclassification, at both low and high levels of the hierarchy. If a better reclassification is found, the knowledge structure is reorganized to comply. The approach is validated during incremental acquisition of a motion database containing a variety of full body motions. [ABSTRACT FROM AUTHOR]

A fundamental assumption of cognitive science is that the individual is the correct unit of analysis for understanding human intelligence. I present evidence that this assumption may have limited utility, that the social networks containing the individuals are an important additional unit of analysis, and that this "network intelligence" is significantly mediated by non-linguistic processes. Across a broad range of situations these network effects typically predict 40% or more of the variation in human behavior. [ABSTRACT FROM AUTHOR]

Hair pulling has been reported in humans, six different non-human primate species, mice, guineapigs, rabbits, sheep and muskox, dogs and cats. This behaviour seems to occur only in subjects who are confined in an artificial environment. It has been classified as a mental disorder in humans, as a behavioural pathology in animals. The hair is not only pulled but also, in most species, ingested. Hair pulling can be both self-directed and partner-directed, contains elements of aggression, manifests more often in females than in males, is associated with psychogenic distress, and resists treatment. Research data collected from affected animals are probably not normative, hence scientifically unreliable. The preemptive correction of husbandry deficiencies causing long-term stress may prevent the development of this bizarre behaviour in healthy subjects. [ABSTRACT FROM AUTHOR]

Hans Selye discovered Stress in 1935 as a syndrome occurring in laboratory rats. In the modern world, Stress has become a universal explanation for human behaviour in industrial society. Selye's discovery arose out of widespread interest in the stability of bodily systems in 1930s' physiology; however, his findings were rejected by physiologists until the 1970s. This analysis is framed in terms of Latour's actor-network theories, and traces the translation of Stress from the animal laboratory into the narratives of modern life experience. This mapping reveals that translation was brought about by Selye's recruitment of a broadly based constituency outside of academic physiology, whose members each saw in Stress a validation of their pre-existing ideas of the relationship of the human mind and body in industrial civilization. While Selye was successful in realizing Stress as a scientific fact, he was unable to make his institute the obligatory passage point for Stress research. Selye's notion of a universal non-specific reaction has become accepted in almost all forms of human discourse about life and health, and physiologists in the 1990s use Stress as a unifying concept to understand the interaction of organic life with the environment. However, this modern use of Stress contains none of the physiological postulates of Selye's original findings. [ABSTRACT FROM AUTHOR]

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The article focuses on the role of mechanical metaphors in studying mind and human behavior. The hypothesis that psychological theories tend to arise by displacement of concepts from the physical domain to the mental domain has frequently been advanced, but rarely documented or examined closely. Mechanical metaphors drawn from the high technology of the day have always been the primary source for this particular mode of coming to grips with the invisible insubstantial mind. The article focuses on how the strategy of displacement is first formulated clearly in the aftermath of the Ionian enlightenment and how it was continued to be applied throughout successive technological revolutions to the present day. The crucial role played by physicians and in particular, by neurologists in propagating technological metaphors of human behavior and brain function is stressed. The machines of mind have provided a very general framework which allowed mental processes to be studied if they were physical processes, but which does not lead to reductionism.

The author reflects on the Shambaugh Conference on biobehavioral studies of politics at the University of Iowa in November 1980. He states that in one important respect, the conference fell victim to the character of its times. The author believes that if political behavior research is to become a coherent endeavor, biobehavioral theory which is tied to perspectives on functioning human communities is crucial.