代数图式相似性对样例迁移中原理通达的影响

应用题深层抽象结构有几个不同层次,被试对概念图式的相似性更敏感,而不是方程结构,概念图式的相似性更容易引起原理的通达。学生掌握了代数图式对解题有很大的促进作用,较少受表面内容等因素的影响。该研究对代数应用题教学模式的改革提供了理论支持。     

"[人]在[大场所]"材料范式下观察不到信息整合的原因初探

本研究采用扇效应技术来探求推翻G．A．Radvansky针对其1993年研究结果所作解释的证据。共分为两个分实验。实验1重复Radvansky1993年研究中的实验三,以取得中文材料的基线值,为实验2做准备;实验2通过在材料中使用同样具有可“移动性”的物体的概念(例如快餐车)来探讨“可移动性”是否是“[人]在[大场所]”材料范式下观察不到信息整合的根本原因。结果表明“人的可移动性”不是“[人]在[大场所]”．材料范式下观察不到信息整合的原因。     

基于网络与演化视角的集体球类项目团队共享心智模型

团队既是当今组织中的重要工作方式, 又是集体竞技的组织形式, 故成为众多学科关注的焦点。共享心智模型是团队成员共享的团队相关情境关键要素的有组织理解、知识心理表征或信念。近年来, 中国足、篮、排三大球集体陷入低谷, 输球又输人的状况屡见取端, 引发举国关注。振兴“三大球”成为建设体育强国必然要迈过的坎。研究提出从网络与演化视角出发, 以“三大球”为主要研究对象, 通过对主题专家的深度访谈与“三大球”标杆球队的进球(或得分)录像分析, 寻找“三大球”共享心智模型演化的共性与殊性, 并为后续研究提供素材与参数; 接着, 采用多重个案设计与追踪研究范式, 交叉检定与复现共享心智模型的演化理论与作用机制理论; 再次, 实证检验成员社会网络类型、结构与演化对共享心智模型与团队效能的影响; 最后, 运用Repast 软件对共享心智模型的形成与演化进行模拟, 以为预测与实时干预提供参考。     

不同任务情境中的前瞻记忆是一致的吗？

对47名大学生被试依次进行自然情境中的人为任务、自然情境中的自然任务、实验室情境中的人为任务和实验室情境中的自然任务的测试。结果发现,相同任务情境中的不同性质的前瞻记忆具有一致性,特别是在自然情境中的一致性更为明显;相同任务性质但不同任务情境下的前瞻记忆没有一致性,实验室中的前瞻记忆与自然情境中的前瞻记忆不能相互预测。     

When Problems Lead to Ideas: The Roles of Daily Vigor and Social Interactions

In this study, we investigate idea generation by conducting a diary study. We hypothesized that idea generation depends on problem identification, and that this relation is moderated by two factors: (a) the number of social interactions an individual has with “non‐redundant” network connections, and (b) an individual's level of vigor. The hypotheses were tested by making use of a diary study among 31 employees of a Dutch applied university over a period of 2 weeks. Results showed that idea generation results from the identification of problems that require new ideas to solve them. An individual's number of social interactions and the level of redundancy of these social interactions moderated this relation. The level of vigor of an individual did not predict idea generation, but results suggest that vigor is needed for interacting with others. Furthermore, this study contributes to the creativity research using a new approach to look at the effects of social interactions by relating them to the overall structure of social networks.     

Investigating stereotype structure with empirical network models

In this work we present empirical network models as a new approach in the investigation of stereotype structure. We will argue that empirical network models can provide more insight into stereotype structure because they do not suffer from the inherent constraints of factor analysis and multidimensional scaling (e.g., group features interpreted homogeneously only on the basis of their shared variance, impossibility to adequately represent cognitive schemas, difficulties to make inferences on the basis of dimensions potentially overlapping). In the present research we show how empirical network models can represent stereotypes as dynamic cognitive structures clustered in different substructures. These structures will be based on both the stereotype content and the co-occurrence of features in each group target. Additionally, this research shows how using empirical networks can contribute to broadening the interpretation of stereotypes representing them in the framework of prejudice or intergroup attitudes.     

Models and mechanisms in network neuroscience

This paper considers the way mathematical and computational models are used in network neuroscience to deliver mechanistic explanations. Two case studies are considered: Recent work on klinotaxis by Caenorhabditis elegans, and a long-standing research effort on the network basis of schizophrenia in humans. These case studies illustrate the various ways in which network, simulation, and dynamical models contribute to the aim of representing and understanding network mechanisms in the brain, and thus, of delivering mechanistic explanations. After outlining this mechanistic construal of network neuroscience, two concerns are addressed. In response to the concern that functional network models are nonexplanatory, it is argued that functional network models are in fact explanatory mechanism sketches. In response to the concern that models which emphasize a network’s organization over its composition do not explain mechanistically, it is argued that this emphasis is both appropriate and consistent with the principles of mechanistic explanation. What emerges is an improved understanding of the ways in which mathematical and computational models are deployed in network neuroscience, as well as an improved conception of mechanistic explanation in general.     

“I know you can do this” versus “How can you be so stupid?” Feedback among Chinese and American young adult friends

According to past research, young adults prefer positive feedback from their friends, but emotional reactions to negative feedback are less severe in Korea than in the United States. We extended this past work in several ways: We studied both giving and receiving feedback, recruited Chinese young adults, used realistic scenarios, and employed the situation‐sampling method. Using text‐message‐based vignettes, Study 1 found that both American and Chinese friends used happy emojis when exchanging positive feedback whereas Americans used less positive emojis than did the Chinese when exchanging negative feedback. We then collected actual examples of feedback that people had given to their friends (Study 2a) and showed them to new samples of Chinese and American young adults (Study 2b). This situation‐sampling methodology found that (a) Americans rated negative feedback from both cultures especially negatively, and (b) negative feedback examples from China were rated especially negatively by people in both cultures. That is, although Chinese negative feedback is seemingly more harsh, Chinese young adults do not react as negatively.     

Semantic segmentation via highly fused convolutional network with multiple soft cost functions

Semantic image segmentation is one of the most challenging tasks in computer vision. In this paper, we propose a highly fused convolutional network, which consists of three parts: downsampling, fused upsampling and multiple predictions. We adopt a VGG-net based downsampling structure, followed by multiple steps of upsampling. Feature maps in each pair of corresponding pooling layers and unpooling layers are combined. We also bring out multiple pre-outputs, each is generated from an unpooling layer by a one-step upsampling operation. Finally, we concatenate these pre-outputs to get the final output. As a result, our proposed network makes high use of the feature information by fusing and reusing features in low layers. In addition, when training our model, we add multiple soft cost functions on pre-outputs and the final output. In this way, we can reduce the loss reduction in backpropagation. We evaluate our model on three public segmentation datasets: CamVid, PASCAL VOC, and ADE20K. We achieve considerable segmentation performance on PASCAL VOC dataset and ADE20K dataset. Especially on CamVid dataset, we achieve state-of-the-art performance.     

SEAT-DSR: Spatial and energy aware trusted dynamic distance source routing algorithm for secure data communications in wireless sensor networks

This paper propose a novel secure routing mechanism called Spatial and Energy Aware Trusted Dynamic Distance Source Routing (SEAT-DSR) algorithm for enhancing the network life time of wireless sensor networks. Here, the spatial information, energy level, and the effectiveness of data quality are equalized by the Quality of Service (QoS) based energy aware routing algorithms. In addition to this approach, a standard clustering algorithm is also incorporates for grouping the wireless sensor nodes based on the trust score, spatial information, energy level and the distance between the nodes. In this SEAT-DSR is also capable of making decision over the evaluation metrics that are decided and expressed the QoS. Moreover, a new hierarchical trust mechanism is also introduced in this model which adopts multi-attributes of many wireless sensor nodes according to the data communication speed, data size, energy consumption, and the recommendation. This new hierarchical trust method relies over an improved the sliding window time by considering the presence of various attacks frequency to identify the attackers by discovering their anomalous behaviour. The proposed SEAT-DSR is evaluated by conducting many experiments in a simulation environment that creates by using Network Simulator-2 (NS2). The experimental results of the proposed algorithm are proved that the average packet transfer rate is increased drastically than the existing secure routing methodologies.