Generating and sharing of market intelligence in sales teams: an economic social network perspective

In this research, we take a multimethod approach to shed light on the potential costs to sales teams that generate and share market intelligence (MI). First, we introduce an analytical model to propose the respective levels of effort that sales managers, experts, and team members spend generating and sharing MI. To test our propositions, we utilize social network data from 40 independent, business-to-business (B2B) sales teams, representing 287 salespeople. Interestingly, our results support the premise that team members become dependent (reduce MI efforts) when their sales manager or team expert shares MI among the team. We term this a “sharing tax” that sales managers and team experts pay when they share MI. Consequently, sales managers demonstrate greater MI-generation efforts the more they share MI. We also find that experts who share more (less) also show greater (lesser) MI-generation efforts, but only for teams where sales managers share low (high) levels of MI. In summary, our research innovatively conducts an empirical test of the Nash Equilibrium pattern of sales team effort to show that two critical team members, the sales manager and expert, are at a disadvantage when they share valuable MI.     

认知神经研究中的积极共情

积极共情指个体对他人积极情绪状态的理解和间接分享过程。虽然它与消极共情在产生过程中都依赖于镜像神经系统和心理理论系统的活动, 但是两者在产生难度上不同。对于积极共情情感如何在大脑中表征的, 不同的研究者观点各异：一些认为与消极共情一致, 都集中在脑岛及其相关脑区; 而另一些则认为是大脑的愉悦系统。关于积极共情影响因素的研究主要集中于共情主体和客体的关系方面。今后需要进一步了解积极共情在产生机制和情感表征上的特点, 拓展其影响因素的研究并展开对特质积极共情神经基础的探讨。     

不安全依恋者注意偏向的形成机制及神经基础

不安全依恋者的注意偏向是指依恋焦虑和回避个体对依恋信息产生或趋近或回避的注意偏好, 这一注意偏好使得个体形成了处理与依恋对象关系的特殊认知机制：依恋回避个体偏好采用防御性机制, 倾向于回避对依恋信息的注意; 依恋焦虑个体偏好采用夸大性机制, 倾向于夸大威胁性信息, 因而会积极寻求与依恋对象的亲近。不安全依恋者注意偏向的差异主要体现在情绪信息、痛觉感受和工作记忆三个方面; 加工阶段特征主要涉及P1、N1波等代表的注意自动加工和LPP波代表的注意控制加工; 脑网络上主要包括两个子网络, 即负责情绪评估的边缘系统和负责情绪调节的以前额叶为代表的神经网络。未来研究中更应该关注刺激物的生态效度, 检验依恋系统是否激活, 不安全依恋者注意偏向的自动加工和控制加工阶段的关系, 不安全依恋者注意偏向产生的分子遗传机制以及注意偏向的跨文化研究。     

赌博障碍的认知功能缺陷及神经基础

赌博障碍是指持续且反复的赌博行为,给个人、家庭和社会都带来了严重后果,近年来受到越来越多研究者的关注。本研究总结了赌博障碍的认知功能缺陷及其神经基础,主要集中在如下四个方面：①认知扭曲、②奖赏和惩罚敏感性、③注意偏向和④决策。未来研究应多从认知神经科学角度深入探究赌博障碍的发生机制和发展过程,关注大脑结构变化及功能网络改变,并将其整合到一个统一的神经生物机制框架中,找到更加有效的干预和治疗手段。     

预先承诺的理论解释及其神经机制

预先承诺是指个体提前做出一个有长远目标的决定来限制对即时诱惑的选择,从而避免在未来决策中由于意志力缺乏而出现的自我控制失败。最后期限法和限制选择法是开展预先承诺研究的常用范式。本研究在系统回顾预先承诺的三种理论解释（自我控制资源模型、神经网络模型和时间差强化学习模型）的基础上,尝试提出一个整合性的认知神经机制模型来解释预先承诺的作用过程。未来研究可进一步丰富预先承诺的研究范式和应用范围,并探究预先承诺调控非理性决策的心理与神经机制。     

认知主义与联结主义之比较

认知主义的研究定向和联结主义的研究定向是广义的现代认知心理学的两种主要研究范式。这两种研究范式都各有自己的研究内容和方法论,也取得了各自不同的成就,同时也存在各自不同的问题。通过对两种研究范式的比较,我们可以清楚地认识到二者的相互沟通和融合才是认知心理学未来发展的必然趋势。     

Emergence from brain architectures: a new cognitive science?

The way in which artificial intelligence has developed over the last 50 years has had a major role in shaping cognitive science as it is today. This has generated computational models of behaviour. The connectionist revival of the 1980s added a tinge of neurodynamics to this. Here I suggest that some post-connectionist work in artificial intelligence is turning towards an understanding and formalisation of the mechanisms of brain architectures which contribute to an emergence of cognition providing a closer link between brain mechanisms and experienced brain states. This even addresses the neurological basis of consciousness.     

Semantic computations of truth based on associations already learned

This article sets forth a detailed theoretical proposal of how the truth of ordinary empirical statements, often atomic in form, is computed. The method of computation draws on psychological concepts such as those of associative networks and spreading activation, rather that the concepts of philosophical or logical theories of truth. Axioms for a restricted class of cases are given, as well as some detailed examples.     

An evolutionary system for neural logic networks using genetic programming and indirect encoding

Nowadays, intelligent connectionist systems such as artificial neural networks have been proved very powerful in a wide area of applications. Consequently, the ability to interpret their structure was always a desirable feature for experts. In this field, the neural logic networks (NLN) by their definition are able to represent complex human logic and provide knowledge discovery. However, under contemporary methodologies, the training of these networks may often result in non-comprehensible or poorly designed structures. In this work, we propose an evolutionary system that uses current advances in genetic programming that overcome these drawbacks and produces neural logic networks that can be arbitrarily connected and are easily interpretable into expert rules. To accomplish this task, we guide the genetic programming process using a context-free grammar and we encode indirectly the neural logic networks into the genetic programming individuals. We test the proposed system in two problems of medical diagnosis. Our results are examined both in terms of the solution interpretability that can lead in knowledge discovery, and in terms of the achieved accuracy. We draw conclusions about the effectiveness of the system and we propose further research directions.     

Logic programs and connectionist networks

One facet of the question of integration of Logic and Connectionist Systems, and how these can complement each other, concerns the points of contact, in terms of semantics, between neural networks and logic programs. In this paper, we show that certain semantic operators for propositional logic programs can be computed by feedforward connectionist networks, and that the same semantic operators for first-order normal logic programs can be approximated by feedforward connectionist networks. Turning the networks into recurrent ones allows one also to approximate the models associated with the semantic operators. Our methods depend on a well-known theorem of Funahashi, and necessitate the study of when Funahashi's theorem can be applied, and also the study of what means of approximation are appropriate and significant.