How the Learning Path and the Very Structure of a Multifloored Environment Influence Human Spatial Memory

Few studies have explored how humans memorize landmarks in complex multifloored buildings. They have observed that participants memorize an environment either by floors or by vertical columns, influenced by the learning path. However, the influence of the building’s actual structure is not yet known. In order to investigate this influence, we conducted an experiment using an object-in-place protocol in a cylindrical building to contrast with previous experiments which used rectilinear environments. Two groups of 15 participants were taken on a tour with a first person perspective through a virtual cylindrical three-floored building. They followed either a route discovering floors one at a time, or a route discovering columns (by simulated lifts across floors). They then underwent a series of trials, in which they viewed a camera movement reproducing either a segment of the learning path (familiar trials), or performing a shortcut relative to the learning trajectory (novel trials). We observed that regardless of the learning path, participants better memorized the building by floors, and only participants who had discovered the building by columns also memorized it by columns. This expands on previous results obtained in a rectilinear building, where the learning path favoured the memory of its horizontal and vertical layout. Taken together, these results suggest that both learning mode and an environment’s structure influence the spatial memory of complex multifloored buildings.     

Multilayer cognitive architecture for UAV control

Extensive use of unmanned aerial vehicles (UAVs) in recent years has induced the rapid growth of research areas related to UAV production. Among these, the design of control systems capable of automating a wide range of UAV activities is one of the most actively explored and evolving. Currently, researchers and developers are interested in designing control systems that can be referred to as intelligent, e.g. the systems which are suited to solve such tasks as planning, goal prioritization, coalition formation, etc. and thus guarantee high levels of UAV autonomy. One of the principal problems in intelligent control system design is tying together various methods and models traditionally used in robotics and aimed at solving such tasks as dynamics modeling, control signal generation, location and mapping, path planning, etc. with the methods of behavior modeling and planning which are thoroughly studied in cognitive science. Our work is aimed at solving this problem. We propose layered architecture—STRL (strategic, tactical, reactive, layered)—of the control system that automates the behavior generation using a cognitive approach while taking into account complex dynamics and kinematics of the control object (UAV). We use a special type of knowledge representation—sign world model—that is based on the psychological activity theory to describe individual behavior planning and coalition formation processes. We also propose path planning methodology which serves as the mediator between the high-level cognitive activities and the reactive control signals generation. To generate these signals we use a state-dependent Riccati equation and specific method for solving it. We believe that utilization of the proposed architecture will broaden the spectrum of tasks which can be solved by the UAV’s coalition automatically, as well as raise the autonomy level of each individual member of that coalition.     

Moving Onwards: An Action Continuation Strategy in Finding the Way

In four studies, we examined people's strategies when deciding between multiple routes of equivalent length in way‐finding tasks. The results reveal the important role of continuing behavior when faced with a choice from multiple viable routes. After affirming the existence of asymmetric preferences for alternatives (Studies 1 and 2), we observed that variations of simple known‐environment mazes supported action continuation as prevailing process over alternative strategies such as preference for long initial path segments, paths with a least deviating angle, and a modified hill climbing strategy (Study 3). Moreover, asymmetric preferences disappeared with the absence of initial behavior to inform subsequent decision making (Study 4). Results are discussed within the context of decision making, navigation strategies, and everyday life path finding. Copyright © 2014 John Wiley & Sons, Ltd.     

Topological visual mapping in robotics

A key problem in robotics is the construction of a map from its environment. This map could be used in different tasks, like localization, recognition, obstacle avoidance, etc. Besides, the simultaneous location and mapping (SLAM) problem has had a lot of interest in the robotics community. This paper presents a new method for visual mapping, using topological instead of metric information. For that purpose, we propose prior image segmentation into regions in order to group the extracted invariant features in a graph so that each graph defines a single region of the image. Although others methods have been proposed for visual SLAM, our method is complete, in the sense that it makes all the process: it presents a new method for image matching; it defines a way to build the topological map; and it also defines a matching criterion for loop-closing. The matching process will take into account visual features and their structure using the graph transformation matching (GTM) algorithm, which allows us to process the matching and to remove out the outliers. Then, using this image comparison method, we propose an algorithm for constructing topological maps. During the experimentation phase, we will test the robustness of the method and its ability constructing topological maps. We have also introduced new hysteresis behavior in order to solve some problems found building the graph.     

时间领导对员工助人行为的影响:工作激情和主动型人格的作用

基于情感事件理论和人-境互动视角,应用bootstrap法根据260份有效问卷的数据执行路径分析,探讨时间领导对员工助人行为的影响机制。结果发现:时间领导正向预测助人行为;时间领导影响助人行为部分是通过和谐式激情与强迫式激情的并行多重中介作用来实现的,且和谐式激情的个别中介效应较大;由于主动型人格的调节作用,时间领导影响助人行为的直接效应和总效应在员工主动型人格较低时更强。     

Supervisory mentoring and employee affective commitment and turnover: The critical role of contextual factors

Past research has largely ignored the role of contextual factors in the relationships between supervisory mentoring and individual and organizational outcomes. In order to fill this void, we investigate how job scope and career and development opportunities, two critical contextual factors, moderate the supervisory mentoring-affective commitment-turnover links. Integrating social exchange theory with insights from situational approaches to leadership, we hypothesized that (a) job scope would interact with supervisory mentoring in predicting affective commitment and (b) career and development opportunities would interact with affective commitment in predicting turnover such that the conditional effects of supervisory mentoring on turnover would be stronger at high levels of these contextual moderators. Results of a study conducted with a sample of 228 business alumni, using 15-month voluntary turnover as outcome, supported our predictions. We discuss the implications of these findings for mentoring research and practice.     

Encouraging employees to report unethical conduct internally: It takes a village

Via three studies of varying methodologies designed to complement and build upon each other, we examine how supervisory ethical leadership is associated with employees’ reporting unethical conduct within the organization (i.e., internal whistle-blowing). We also examine whether the positive effect of supervisory ethical leadership is enhanced by another important social influence: coworkers’ ethical behavior. As predicted, we found that employees’ internal whistle-blowing depends on an ethical tone being set by complementary social influence sources at multiple organizational levels (both supervisory and coworker levels), leading us to conclude that “it takes a village” to support internal whistle-blowing. Also, this interactive effect was found to be mediated by a fear of retaliation in two studies but not by perceptions of futility. We conclude by identifying theoretical and practical implications of our research.     

他人定向变量影响青少年孤独感和社交焦虑:人际能力作中介

利用自我意识量表中的公众自我意识分量表和社交焦虑分量表,人际能力量表和青少年孤独感量表调查465名高中生,探索社会观点采择、公众自我意识和同伴交往中的人际能力对青少年情绪适应的影响。结果表明:青少年社会观点采择和公众自我意识直接正向预测其人际能力和社交焦虑;人际能力分别负向预测青少年的孤独感和社交焦虑;人际能力在青少年的社会观点采择和孤独感之间,以及在公众自我意识和孤独感之间分别起完全中介作用;人际能力在青少年的社会观点采择和社交焦虑之间,以及在公众自我意识和社交焦虑之间分别起部分中介作用。进一步分析表明,社会观点采择以及公众自我意识对不同情境下的人际能力的影响存在显著差异。讨论了从社会观点采择和公众自我意识出发,提高青少年人际能力,对缓解其孤独感和社交焦虑的重要意义。     

Process and Content in a Therapy Group for Bipolar Outpatients

Although the literature suggests that homogeneous therapy groups are useful for bipolar outpatients, little is known about the process in such groups. Using the GCQ-S and a content analysis of discussion topics, we evaluated group process during the first 31 sessions of an outpatient bipolar group. Compared with normative samples, our group scored significantly higher in the Engaged dimension and significantly lower in the Avoiding and Conflict dimensions and in the anxiety/tension scale. Furthermore, 81% of the discussion topics were related to the three group goals, and an additional 18% dealt with general group issues such as orienting new members and attendance. The results from this pilot study suggest that bipolar patients can discuss relevant issues in a homogeneous group environment that is cohesive, open, and safe.     

Divergence Behaviour of the Successive Geometric Mean Method of Pairwise Comparison Matrix Generation for a Multiple Stage,Multiple Objective Optimization Problem

This paper focuses on the divergence behaviour of the successive geometric mean (SGM) method used to generate pairwise comparison matrices while solving a multiple stage, multiple objective (MSMO) optimization problem. The SGM method can be used in the matrix generation phase of our three‐phase methodology to obtain pairwise comparison matrix at each stage of an MSMO optimization problem, which can be subsequently used to obtain the weight vector at the corresponding stage. The weight vectors across the stages can be used to convert an MSMO problem into a multiple stage, single objective (MSSO) problem, which can be solved using dynamic programming‐based approaches. To obtain a practical set of non‐dominated solutions (also referred to as Pareto optimal solutions) to the MSMO optimization problem, it is important to use a solution approach that has the potential to allow for a better exploration of the Pareto optimal solution space. To accomplish a more exhaustive exploration of the Pareto optimal solution space, the weight vectors that are used to scalarize the MSMO optimization problem into its corresponding MSSO optimization problem should vary across the stages. Distinct weight vectors across the stages are tied directly with distinct pairwise comparison matrices across the stages. A pairwise comparison matrix generation method is said to diverge if it can generate distinct pairwise comparison matrices across the stages of an MSMO optimization problem. In this paper, we demonstrate the SGM method's divergence behaviour when the three‐phase methodology is used in conjunction with an augmented high‐dimensional, continuous‐state stochastic dynamic programming method to solve a large‐scale MSMO optimization problem. Copyright © 2013 John Wiley & Sons, Ltd.     

Operator State Estimation to Enable Adaptive Assistance in Manned-Unmanned-Teaming

With the continued development of unmanned aerial vehicle (UAV) technologies, the UAV on-board automation is increasingly more capable of performing tasks formerly done by human operators. Thereby, the role of UAVs is changing from being mere tools to become members of integrated manned-unmanned systems. However, the high automation necessary to achieve this cooperation, introduces a new set of negative effects for the human operator such as complacency or automation bias. Adaptive assistance is one approach to counteract these negative effects seen in human-automation-interaction. To enable adaptive assistance, we present a cognitive state estimation framework for a MUM-T aircraft application. The goal of this approach is to assess attention allocation and SA of a pilot in real-time and identify possible breakdowns in the situational picture that could cause performance decrements and errors. The design of a MUM-T cockpit simulator is presented to describe how this cognitive state estimation framework is integrated into a human-autonomy-teaming environment. The results of initial simulator experiments are presented and areas of further research are identified.     

Understanding the factors that determine workplace coaching effectiveness: a systematic literature review

Meta-analytic results have established that workplace coaching is effective, however, little is known about the determinants of coaching effectiveness. This paper reports an inclusive systematic literature review, covering the quantitative and qualitative research on workplace coaching. We focus on seven promising areas in the current workplace coaching literature that emerged by the synthesis of 117 empirical studies: self-efficacy, coaching motivation, goal orientation, trust, interpersonal attraction, feedback intervention, and supervisory support. The major contribution of our paper is the systematic integration of well-established theoretical constructs in the workplace coaching context and the new insights we provide in the synthesis of these literatures. Based on our review, we provide specific recommendations to be addressed in future research, including recommended research methodologies, which we propose will significantly progress the field of workplace coaching theory and practice.     

Enhancing Digital Fluency through a Training Program for Creative Problem Solving Using Computer Programming

The purpose of this paper is to propose a training program for creative problem solving based on computer programming. The proposed program will encourage students to solve real‐life problems through a creative thinking spiral related to cognitive skills with computer programming. With the goal of enhancing digital fluency through this proposed training program, we investigated its effects. Two sets of experiments were performed in which 119 typical students and 30 younger, gifted students participated. Two synthetic creative problem solving tests, which had a high correlation with logical ability, scientific problem solving ability and divergent thinking ability, were developed to measure creative problem solving ability. We provided the treatment group with a paper‐based booklet with relevant problems developed specifically for that group. ANCOVA statistical procedures were used to analyze the pre‐ and post‐synthetic creative problem solving tests. The findings of our study are as follows: with typical students, the originality of the treatment group outperformed the control group, a result that was compatible with previous research. With gifted students, the fluency of the treatment group outperformed the control group, and overall creative problem solving ability was enhanced. Remarkably, fluency increased significantly, a notable difference from the results of prior studies. In conclusion, we inferred that, given the definition of digital fluency, if creative problem solving ability is enhanced by a training program for creative problem solving based on computer programming, digital fluency will ultimately be improved. In this paper, we discuss the result of fluency enhancement that contradicts prior research. We suggest that this training program could be a new learning environment for the students who have grown up with digital media.     

Through a Glass,Clearly

The issue of sexual boundary violations in our profession is discussed from the vantage point of a faculty member. The major issues that require attention include creation of a safe and secure environment for candidates, dispelling denial, combating mystification, and metabolizing the strong feelings generated by such a breach. Addressing these issues assures a minimization of the possibility of enactment of the violation on an institutional level. It is only in doing so that we create the therapeutic holding environment for the teaching and learning of psychoanalysis to occur.     

上司地位知觉对下属默契的影响——反馈寻求行为和观点采择的作用

下属默契指的是下属与上司间不经言传而暗相投合的状态,它能潜移默化地提升下属绩效。因此,探讨下属默契的产生原因,对组织有着重要意义。鉴于此,本文以目标动机模型为理论基础,探讨了上司地位知觉对下属默契的影响及其内部机制。通过对186名企业员工的调查分析发现,上司地位知觉对下属默契有显著的正向影响;反馈寻求行为在此过程中起部分中介作用;此外,下属的观点采择能力调节了反馈寻求行为与下属默契间的正向关系。     

Modelling relations between sensory processing, speech perception, orthographic and phonological ability, and literacy achievement

The general magnocellular theory postulates that dyslexia is the consequence of a multimodal deficit in the processing of transient and dynamic stimuli. In the auditory modality, this deficit has been hypothesized to interfere with accurate speech perception, and subsequently disrupt the development of phonological and later reading and spelling skills. In the visual modality, an analogous problem might interfere with literacy development by affecting orthographic skills. In this prospective longitudinal study, we tested dynamic auditory and visual processing, speech-in-noise perception, phonological ability and orthographic ability in 62 five-year-old preschool children. Predictive relations towards first grade reading and spelling measures were explored and the validity of the global magnocellular model was evaluated using causal path analysis. In particular, we demonstrated that dynamic auditory processing was related to speech perception, which itself was related to phonological awareness. Similarly, dynamic visual processing was related to orthographic ability. Subsequently, phonological awareness, orthographic ability and verbal short-term memory were unique predictors of reading and spelling development.     

Event-learning and robust policy heuristics

In this paper we introduce a novel reinforcement learning algorithm called event-learning. The algorithm uses events, ordered pairs of two consecutive states. We define event-value function and we derive learning rules. Combining our method with a well-known robust control method, the SDS algorithm, we introduce Robust Policy Heuristics (RPH). It is shown that RPH, a fast-adapting non-Markovian policy, is particularly useful for coarse models of the environment and could be useful for some partially observed systems. RPH may be of help in alleviating the ‘curse of dimensionality’ problem. Event-learning and RPH can be used to separate time scales of learning of value functions and adaptation. We argue that the definition of modules is straightforward for event-learning and event-learning makes planning feasible in the RL framework. Computer simulations of a rotational inverted pendulum with coarse discretization are shown to demonstrate the principle.     

A panel study of the effects of school positions and promotions on absenteeism in the teaching profession

We hypothesized that being promoted to a supervisory position leads to a reduction in subsequent absence spells and time lost in absences, and that the higher the supervisory level, the more pronounced its negative effect on subsequent absence behaviour. We tested our hypotheses by analysing the government records of 51,974 school teachers in the years 2000 and 2001. Controlling for potential confounding variables, we found that relative to rank‐and‐file teachers, the higher the level of the supervisory position that a teacher was promoted to, the more pronounced the year‐to‐year decline in either absence spells or time lost in absences. As hypothesized, for most of the supervisory positions examined, the size of their effect on absence behaviours was lower for veterans than for the newly appointed.