Elementary Behaviors Research Articles

Background Factors of Bicycle Driving Behavior and Helmet Wearing of Elementary and Junior High School Students

Background Factors of Bicycle Driving Behavior and Helmet Wearing of Elementary and Junior High School Students

Using Eye-Tracking Technology to Examine the Impact of Question Format on Reading Behavior in Elementary Students

Reading comprehension assessments often include multiple-choice (MC) questions, but some researchers doubt their validity in measuring comprehension. Consequently, new assessments may include more short-answer (SA) questions. The current study contributes to the research comparing MC and SA questions by evaluating the effects of anticipated question format on elementary students' reading behavior. Third- and fourth-grade participants were divided into the MC (n = 43) or SA condition (n = 44) and expected to answer questions consistent with their group assignment. Eye movements (EMs) were analyzed across the passage and on areas significant to its meaning. Correlational analyses between EMs and reading measures were conducted. Findings support modification of question format in reading assessments. Implications for school psychologists, teachers, and EM researchers are addressed.

Failure study of the woven composite material: 2.5 D carbon fabric/ resin epoxy

In this paper an experimental analysis of the failure of a single layer woven fabric composite 2.5 D G1151/ Resin Epoxy through a tensile tests at 0°, 45° and 90° is investigated. In addition a FE simulation of failure were elaborated through multiscale modeling method, micro then meso then macro scale. The microscale simulation was elaborated on ABAQUS standard simulation of a 3D unit cell of random fibers distribution of a single yarn. The meso scale simulation developed on MATLAB. The meso approach based to the extraction of the behavior of representative volume elementary (RVE) of the 2.5 D woven composite. The macroscale simulation was elaborated on ABAQUS standard simulation. With reference to the numerical and experimental study, the results shows a good agreement. The present investigation is an important preliminary study in process forming of single woven carbon 2.5 D composite.

On Reliable Neural Network Sensorimotor Control in Autonomous Vehicles

This paper deals with (deep) neural network implementations of sensorimotor control for automated driving. We show how to construct complex behaviors by re-using elementary neural network building blocks that can be trained and tested extensively; one of our goals is to mitigate the “black box” and verifiability issues that affect end-to-end trained networks. By structuring complex behaviors within a subsumption architecture, we retain the ability to learn (mostly at motor primitives level) with the ability to create complex behaviors by subsuming the (well-known) learned elementary perception-action loops. The learning process itself is simplified, since the agent needs only to learn elementary behaviors. At the same time, the structure imposed with the subsumption architecture ensures that the agent behaves in predictable ways (e.g., treating all obstacles uniformly). We demonstrate these ideas for longitudinal obstacle avoidance behavior, but the proposed approach can also be adapted to other situations.

Teachers' ratings of social skills and problem behaviors as concurrent predictors of students' bullying behavior

This study analyzed teacher ratings from the national standardization sample of the Social Skills Improvement System (SSIS) to examine the role of elementary and secondary students' social skills, problem behavior, and academic competence as they relate to bullying behavior. Regression analyses indicated slightly different concurrent predictors of bullying behavior for elementary (N = 544) and secondary (N = 200) students. Specifically, low levels of empathy skills and concurrent high levels of externalizing behaviors were relatively strong correlates with bullying behavior of both elementary and secondary school children. For secondary students, assertion skills exhibited at high frequency levels were also a significant predictor of bullying behavior. The resulting predictive models featured social behaviors that educators and other professionals can successfully improve with existing evidence-based intervention programs. A discussion of study limitations and future research concludes the report.

Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules.

Most elementary behaviors such as moving the arm to grasp an object or walking into the next room to explore a museum evolve on the time scale of seconds; in contrast, neuronal action potentials occur on the time scale of a few milliseconds. Learning rules of the brain must therefore bridge the gap between these two different time scales. Modern theories of synaptic plasticity have postulated that the co-activation of pre- and postsynaptic neurons sets a flag at the synapse, called an eligibility trace, that leads to a weight change only if an additional factor is present while the flag is set. This third factor, signaling reward, punishment, surprise, or novelty, could be implemented by the phasic activity of neuromodulators or specific neuronal inputs signaling special events. While the theoretical framework has been developed over the last decades, experimental evidence in support of eligibility traces on the time scale of seconds has been collected only during the last few years. Here we review, in the context of three-factor rules of synaptic plasticity, four key experiments that support the role of synaptic eligibility traces in combination with a third factor as a biological implementation of neoHebbian three-factor learning rules.

Coordinated Control of Aerial Robotic Manipulators: Theory and Experiments

This brief presents a three-layer control architecture for coordinated control of multiple unmanned aerial vehicle manipulators (UAVMs): the centralized top layer plans the end-effector desired trajectories of each UAVM; the middle layer, local to each vehicle, computes the corresponding motion references; the bottom layer is a low level dynamic motion controller, which tracks the motion references. At second layer, the overall mission is hierarchically decomposed in a set of elementary behaviors, which are combined together, via the null space-based behavioral approach, into more complex compound behaviors. For each UAVM, the suitable compound behavior to be executed is selected by a supervisor. The proposed framework has been tested through an experimental campaign.

When the simplest voluntary decisions appear patently suboptimal.

Rahnev & Denison (R&D) catalog numerous experiments in which performance deviates, often in subtle ways, from the theoretical ideal. We discuss an extreme case, an elementary behavior (reactive saccades to single targets) for which a simple contextual manipulation results in responses that are dramatically different from those expected based on reward maximization - and yet are highly informative and amenable to mechanistic examination.

Behavioral control of unmanned aerial vehicle manipulator systems

In this paper a behavioral control framework is developed to control an unmanned aerial vehicle-manipulator (UAVM) system, composed by a multirotor aerial vehicle equipped with a robotic arm. The goal is to ensure vehicle-arm coordination and manage complex multi-task missions, where different behaviors must be encompassed in a clear and meaningful way. In detail, a control scheme, based on the null space-based behavioral paradigm, is proposed to handle the coordination between the arm and vehicle motion. To this aim, a set of basic functionalities (elementary behaviors) are designed and combined in a given priority order, in order to attain more complex tasks (compound behaviors). A supervisor is in charge of switching between the compound behaviors according to the mission needs and the sensory feedback. The method is validated on a real testbed, consisting of a multirotor aircraft with an attached 6 Degree of Freedoms manipulator, developed within the EU-funded project ARCAS (Aerial Robotics Cooperative Assembly System). At the the best of authors' knowledge, this is the first time that an UAVM system is experimentally tested in the execution of complex multi-task missions. The results show that, by properly designing a set of compound behaviors and a supervisor, vehicle-arm coordination in complex missions can be effectively managed.

Modeling and simulation of pedestrian dynamical behavior based on a fuzzy logic approach

This study proposes a fuzzy logic approach to model and simulate pedestrian dynamical behaviors, which takes full advantage of human experience and knowledge and perceptual information obtained from interactions with surrounding environments. First, the radial-based method is adopted to represent the physical space. A pedestrian’s visual field, defined as a fan-shaped area with a certain visual distance and visual angle, is divided into five sectors. Then, the motion states of a pedestrian are determined by the integration of recommendations of local obstacle-avoiding behavior, regional path-searching behavior and global goal-seeking behavior with mutable weighting factors at three different scopes. These elementary behaviors and weighting’s assignment principle are modeled as fuzzy inference systems with the input information of a pedestrian’s perception toward surrounding environments. A pedestrian is guided to avoid the front obstacles and select the lowest negative energy path by local obstacle-avoiding behavior and regional path-searching behavior, respectively. The global goal-seeking behavior makes a pedestrian has a tendency of moving in direction of his/her goal regardless of external environments. The magnitudes of weighting factors are adjusted automatically to coordinate three elementary behaviors and resolve potential conflicts. At last, the effectiveness of the proposed model is validated by simulations of crowd evacuation, unidirectional and bidirectional pedestrian flows. The simulation results are analyzed from both qualitative and quantitative aspects, which indicate that the fuzzy logic based pedestrian model can get true reappearance of self-organization phenomena such as ‘arching and clogging’, ‘faster-is-slower effect’ and ‘lane formation’, and the fundamental diagrams are in matching with a large variety of empirical and experimental data. A further study finds that walking habits have negligible influence on the fundamental diagrams of bidirectional pedestrian flow at least for densities of ρ < 3p/m2.

Consideration of influence of elementary teacher's personality behavior in creativity and innovation for the students of elementary schools in Semnan City

Aim: The aim of this research is to survey the private publications of elementary teachers in creating creativity and innovation for the students of elementary schools is Semnan Towns. Method of Research: The research utilized a descriptive – survey method, and to collect information from the survey methodology used cluster and probable method of sampling model is probability for teachers' and theirs student, in research used creativity model Torrance and five model personality factors Robert Mc Care and Paul Costain this research. Results: For data collection, a self-made scale was utilized it has been designed according to Likert scale. These questionnaires traditions confirmed by experts and the permanency are approved by Cronbach's alpha test 0.91 and 0.88 than The Pearson correlation test was used. The factor “neuroticism,” the teachers has the most connection with in students' flexibility extraversion and openness factors operating experience is more fluid relationship. Acclimation the most dutiful relationship with or are new initiatives. Conclusion: According to the results is recommended from increase creativity students on Teachers' Personality, on trainer's personality in the way of flexibility, innovation, freshness, and fluid have more accuracy.

Simultaneous planning and action: neural-dynamic sequencing of elementary behaviors in robot navigation

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.

A Programmer-Interpreter Neural Network Architecture for Prefrontal Cognitive Control.

There is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on behavior by biasing processing toward task-relevant information and by modulating response selection. This idea is typically framed in terms of top-down influences within a cortical control hierarchy, where prefrontal-basal ganglia loops gate multiple input-output channels, which in turn can activate or sequence motor primitives expressed in (pre-)motor cortices. Here we advance a new hypothesis, based on the notion of programmability and an interpreter-programmer computational scheme, on how the PFC can flexibly bias the selection of sensorimotor patterns depending on internal goal and task contexts. In this approach, multiple elementary behaviors representing motor primitives are expressed by a single multi-purpose neural network, which is seen as a reusable area of "recycled" neurons (interpreter). The PFC thus acts as a "programmer" that, without modifying the network connectivity, feeds the interpreter networks with specific input parameters encoding the programs (corresponding to network structures) to be interpreted by the (pre-)motor areas. Our architecture is validated in a standard test for executive function: the 1-2-AX task. Our results show that this computational framework provides a robust, scalable and flexible scheme that can be iterated at different hierarchical layers, supporting the realization of multiple goals. We discuss the plausibility of the "programmer-interpreter" scheme to explain the functioning of prefrontal-(pre)motor cortical hierarchies.

NARLE: Neurocognitive architecture for the autonomous task recognition, learning, and execution

Abstract Robots controlled by the state of the art cognitive architectures are still far behind animals in their capabilities to learn complex skills and autonomously adapt to unexpected circumstances. The neurocognitive architecture proposed in this paper addresses the problem of learning and execution of hierarchical behaviors and complex skills. Learning is addressed both on the level of individual elementary behaviors and goal-directed sequences of actions. The proposed architecture comprises a Dynamic Neural Fields (DNFs) implementation of the low-level elementary behaviors and a Functional System Network (FSN) tying these behaviors in goal-directed sequences. The DNF framework enables a continuous, dynamical representation of perceptual features and motor parameters, which may be directly coupled to the robot’s sensors and motors. Attractor states and instabilities of the DNFs account for segregation of cognitive states and mark behaviorally relevant events in the continuous flow of sensorimotor dynamics. The FSN, in its turn, comprises dynamical elements that can be arranged in a multilayered network by a learning process, in which new layers and elementary behaviors are added on demand. In our architecture, the FSN controls adaptation processes in the already acquired neural-dynamic elementary behaviors, as well as formation of new elementary behaviors. Combination of the DNF and FSN frameworks in a neurocognitive architecture NARLE enables pervasive learning both on the level of individual behaviors and goal-directed sequence, contributing to the progress towards more adaptive intelligent robotic systems, capable to learn new tasks and extend their behavioral repertoire in stochastic real-world environments.

Learning the Condition of Satisfaction of an Elementary Behavior in Dynamic Field Theory

AbstractIn order to proceed along an action sequence, an autonomous agent has to recognize that the intended final condition of the previous action has been achieved. In previous work, we have shown how a sequence of actions can be generated by an embodied agent using a neural-dynamic architecture for behavioral organization, in which each action has an intention and condition of satisfaction. These components are represented by dynamic neural fields, and are coupled to motors and sensors of the robotic agent.Here,we demonstratehowthemappings between intended actions and their resulting conditions may be learned, rather than pre-wired.We use reward-gated associative learning, in which, over many instances of externally validated goal achievement, the conditions that are expected to result with goal achievement are learned. After learning, the external reward is not needed to recognize that the expected outcome has been achieved. This method was implemented, using dynamic neural fields, and tested on a real-world E-Puck mobile robot and a simulated NAO humanoid robot.

Accomplishing Tasks in Reaching the Goal of Robot Formation

The article presents a hierarchical steering system for developing mobile robots and fulfils the established goal in unknown surroundings. The task was carried out with reference to a cooperative combination of elementary behaviour patterns such as finding destination and avoiding obstacles, and was generated using the Kohonen neural network. The paper has assumed formation shape and considered the right of steering other robots in the formation process. Finally, the proposed solution has been verified.

PENGARUH PARTISIPASI SISWA DALAM EKSTRAKURIKULER PRAMUKA TERHADAP PERILAKU PROSOSIAL SISWA SEKOLAH DASAR

This research is aimed to get empirical data about the influence of student’s participation in scout toward student’s 3rd grade elementary school prosocial behavior. The research was conducted at SDN 09 and 11 Lubang Buaya, East Jakarta on May until June 2013. This research used Expost Facto method. The sample in this research is amount 40 students of 3rd grade in SDN 09 and 11 Lubang Buaya, East Jakarta, there are 20 students as the experiment group and 20 students as the control group. The sample of this research was taken by using cluster random sampling. Data collection technique in this research used questioner and observation. The technique for data analysis used Liliefors test and Fisher test. The technique for hypothesis used t-test. The t-test result showed tcount = 30,1 &gt; ttable = 1,686, that means H0 was rejected and Ha was accepted. Thus, the conclusion is the student’s prosocial behavior in experiment group is higher than the student’s prosocial behavior in control group, that showed the student’s participation in scout has significant effect on student’s 3rd grade elementary school prosocial behavior in Lubang Buaya, East Jakarta.

Regulation of experience-dependent bidirectional chemotaxis by a neural circuit switch in Caenorhabditis elegans.

The nematode Caenorhabditis elegans changes its chemotaxis to NaCl depending on previous experience. At the behavioral level, this chemotactic plasticity is generated by reversing the elementary behaviors for chemotaxis, klinotaxis, and klinokinesis. Here, we report that bidirectional klinotaxis is achieved by the proper use of at least two different neural subcircuits. We simulated an NaCl concentration change by activating an NaCl-sensitive chemosensory neuron in phase with head swing and successfully induced klinotaxis-like curving. The curving direction reversed depending on preconditioning, which was consistent with klinotaxis plasticity under a real concentration gradient. Cell-specific ablation and activation of downstream interneurons revealed that ASER-evoked curving toward lower concentration was mediated by AIY interneurons, whereas curving to the opposite direction was not. These results suggest that the experience-dependent bidirectionality of klinotaxis is generated by a switch between different neural subcircuits downstream of the chemosensory neuron.

Bond-slip analysis via a cohesive-zone model simulating damage, friction and interlocking

A recently proposed cohesive-zone model which effectively combines damage, friction and mechanical interlocking has been revisited and further validated by numerically simulating the pull-out test, from a concrete block, of a ribbed steel bar in the post-yield deformation range. The simulated response is in good agreement with experimental measurements of the bond slip characteristics in the post-yield range of deformed bars reported in the literature. This study highlights the main features of the model: with physically justified and relatively simple arguments, and within the sound framework of thermodynamics with internal variables, the model effectively separates the three main sources of energy dissipation, i.e. loss of adhesion, friction along flat interfaces and mechanical interlocking. This study provides further evidence that the proposed approach allows easier and physically clearer procedures for the determination of the model parameters of such three elementary mechanical behaviours, and makes possible their interpretation and measurement as separate material property, as a viable alternative to lumping these parameters into single values of the fracture energy. In particular, the proposed approach allows to consider a single value of the adhesion energy for modes I and II.

CAVIS: a Control software Architecture for cooperative multi-unmanned aerial VehIcle-manipulator Systems

In this paper a Control software Architecture for Cooperative multiple unmanned aerial VehIcle-manipulator Systems (CAVIS) is presented. The core of the architecture is a set of software components, communicating each other through a set of defined messages. To handle multiple control objectives simultaneously, a library of elementary behaviors is defined; then, multiple elementary behaviors are combined, in a given priority order, into tasks (compound behaviors); to this aim the Null-Space-based Behavioral (NSB) approach has been adopted. An application example, involving a cooperative transportation of a bar by two aerial vehicle-manipulator systems, is developed to assess the performance of the proposed architecture.