Loneliness is associated with a greater self-reference effect in episodic memory when compared against a close friend

Loneliness describes a negative experience associated with perceived social disconnection. Despite the clear links between loneliness and mental and physical health, relatively little is known about how loneliness affects cognition. In this study, we tested the effect of loneliness on cognitive distance between the self and others, using a task in which participants completed a surprise memory task for adjectives implicitly encoded in relation to the self, a close friend or a celebrity. We assessed item memory sensitivity, metacognitive sensitivity, metacognitive efficiency and source memory for positive and negative words. In addition, participants reported their trait loneliness and depression. Results revealed an overall self-referential advantage compared with both friend and celebrity encoded items. Likewise, a friend-referential advantage was identified compared to celebrity-encoded items. Individuals who experienced more loneliness showed a greater self-referential bias in comparison to words encoded in relation to a close friend, and a smaller friend-referential bias in comparison to words encoded in relation to celebrity. These findings suggest that loneliness is reflected in a greater cognitive distance between the self and close friends in relation to memory biases. The results have important implications for understanding the social contextual effects on memory and the cognitive ramifications of loneliness.     

Associative links between discrete memories in early infancy

The specificity and independence of two successively established memories were assessed in two studies with 6-month-olds. On different days, infants learned two paired-associate tasks, each involving a specific cue-response pair, in a common distinctive context. Retention of each pair was tested either 3 days later, when both memories were still highly accessible (Experiment 1), or 22 days later, when a reminder was necessary to reactivate them (Experiment 2). In both instances, infants produced only the response associated with the test cue; neither retroactive nor proactive interference occurred. Surprisingly, the cue from one task indirectly reactivated the forgotten memory of the other task, revealing that they were linked in an associative network.     

A minimum-cost network-flow solution to the case V thurstone scaling problem

This paper presents an approach for determining unidimensional scale estimates that are relatively insensitive to limited inconsistencies in paired comparisons data. The solution procedure, shown to be a minimum-cost network-flow problem, is presented in conjunction with a sensitivity diagnostic that assesses the influence of a single pairwise comparison on traditional Thurstone (ordinary least squares) scale estimates. When the diagnostic indicates some source of distortion in the data, the network technique appears to be more successful than Thurstone scaling in preserving the interval scale properties of the estimates.My special thanks go to Alvin Silk, Thomas Magnanti, and Roy Welsch for their support and advice throughout the formative stages of this paper, and to V. Srinivasan for his helpful comments on a later draft of this paper. I also wish to thank the Editor, Associate Editor, and two reviewers for their constructive suggestions.James M. Lattin is Associate Professor of Marketing and Management Science and the James and Doris McNamara Faculty Fellow for 1988-1989.     

汉字部件与英文字母相容性的数学模型──一种群体模板相容性的数学表达

该文运用相容性研究中的维度重叠模型以及Ｆｏｒｋ－Ｊｏｉｎ网络模型,得出群体模板的相容性定义与Ｓ－Ｒ相容性的元素匹配之间的关系式,详细讨论了汉字编码输入法研究中的学习遍数、反应时与汉字部件－英文字母相容性的关系,理论模拟与实验所显示的趋势符合。     

A brain-inspired cognitive support model for stress reduction based on an adaptive network model

Stress is often seen as a negative factor which affects every individual’s life quality and decision making. To help avoid or deal with extreme emotions caused by an external stressor, a number of practices have been introduced. In the scope of this paper, we take three kinds of therapy into account: mindfulness, humor, and music therapy. This paper aims to see how various practices help people to cope with stress, using mathematical modelling. We present practical implementations in the form of client–server software, incorporating the computational model which describes therapy effects for overcoming stress based on quantitative neuropsychological research. The underlying network model simulates the elicitation of an extremely stressful emotion due to a strong stress-inducing event as an external stimulus, followed by a therapy practice simulation leading to a reduction of the stress level. Each simulation is based on user input and preferences, integrating a parameter tuning process; it fits a simulation for a particular user. The client–server architecture software which has been designed and developed completely fulfills this objective. It includes server part with embedded MATLAB interaction and API for client communication.     

Nearest vertex attraction for actively reducing loss

In recent years the neural networks have received considerable success on classification tasks. The randomly initialized network is generally expected to adjust the weights for reducing the loss, which is also indicated as the total distance from the samples to the corresponding vertices. However, the output space of the conventional neural networks suffers from the fixed relation between the labels and vertices during learning. This case forces the mapped points around the unexpected vertex across the decision boundary into the neighborhood of the correct vertex, and simultaneously the boundary points cannot obtain the substantial rectification. Therefore, this study proposes a novel nearest vertex attraction (NVA) to actively adjust the relation between the categories and the output vertices for improving the neural network classifiers. The best relation allows that the data points can be attracted by the nearest vertices to minimize the total moving distances. In this way, the mapped points that are near to the vertices and the decision boundaries obtain the decent management. We evaluated the NVA with several conventional classification techniques and other neural network classifiers on 12 public UCI datasets. The numerical experiments demonstrate that the proposed method improves performance of the neural network classifiers on the involved benchmarks.     

社会网络视角的团队情绪智力

据估计, 60%的团队都没有达到它们的目标, 这给学术界提出了重要且具有挑战性的研究课题。情绪智力(emotional intelligence)是在人际关系基础上发展起来的重要概念。研究发现, 基于微观个体或宏观整体的团队情绪智力在团队成功达到目标过程中起到重要作用。然而, 当前尚无研究探讨基于团队局部成员间情绪智力行为交换而产生的团队情绪智力对团队的影响。为此, 本课题从配对层次(一对一关系中两个人, 即actor-target)这一新角度探讨团队情绪智力, 通过把它看做团队过程中一种产生的状态(emergent state), 在整合团队成员交换理论和社会网络方法基础上对它进行研究。具体围绕团队内配对单元存在的交换关系(指目标者感受到行动者对其实施的情绪智力行为)在团队中形成的网络所代表的团队情绪智力开展实证研究, 以期从理论上揭示团队内情绪智力行为交换的特征, 情绪智力自下而上(bottom-up)由个体发展到团队的过程, 以及配对基础的团队情绪智力的影响效应, 从而为团队管理实践提供具体的改善建议。     

Networks of prospective thoughts: The organisational role of emotion and its impact on well-being

Recent research has shown that many prospective thoughts are organised in networks of related events, but the relational dimensions that contribute to the formation of such networks are not fully understood. Here, we investigated the organisational role of emotion by using cues of different valence for eliciting event networks. We found that manipulating the emotional valence of cues influenced the characteristics of events within networks, and that members of a network were more similar to each other on affective components than they were to members of other networks. Furthermore, a substantial proportion of events within networks were part of thematic clusters and cluster membership significantly modulated the impact of represented events on current well-being, in part through an intensification of the emotion felt when thinking about these events. These findings demonstrate that emotion contributes to the organisation of future thoughts in networks that can affect people's well-being.