Multi-layer architecture for adaptive fuzzy inference system with a large number of input features

The Sugeno adaptive fuzzy neural network using training data is a good approximation to model different systems. The large number of adaptive neuro-fuzzy inference system (ANFIS) input features is a major challenge in using ANFIS and is not applicable with increased parameters. We present a solution for many input features solving modular problems; we created a multi-layer architecture of SUB-ANFIS (MLA-ANFIS) for this purpose. Different topologies were created with various combinations of multiple input features, and an error indicator was calculated for each combination of topologies. Finally, the best topology was chosen among the states with the highest possible performance. We implemented a multi-layered approach based on 365-day concrete compressive strength data with eight input features and the optimized MLA-ANFIS topology (5-3-1) for this purpose from different ANFIS topologies and neural networks. Finally, the results from five other datasets prove the impact of the proposed MLA-ANFIS approach compared to the neural network method.     

A computational analysis of general intelligence tests for evaluating cognitive development

The progression in several cognitive tests for the same subjects at different ages provides valuable information about their cognitive development. One question that has caught recent interest is whether the same approach can be used to assess the cognitive development of artificial systems. In particular, can we assess whether the ‘fluid’ or ‘crystallised’ intelligence of an artificial cognitive system is changing during its cognitive development as a result of acquiring more concepts? In this paper, we address several IQ tests problems (odd-one-out problems, Raven’s Progressive Matrices and Thurstone’s letter series) with a general learning system that is not particularly designed on purpose to solve intelligence tests. The goal is to better understand the role of the basic cognitive operational constructs (such as identity, difference, order, counting, logic, etc.) that are needed to solve these intelligence test problems and serve as a proof-of-concept for evaluation in other developmental problems. From here, we gain some insights into the characteristics and usefulness of these tests and how careful we need to be when applying human test problems to assess the abilities and cognitive development of robots and other artificial cognitive systems.     

急性应激对威胁刺激注意定向和注意解除的影响:认知神经机制研究

急性应激和注意偏向是焦虑障碍和创伤后应激障碍发生和症状保持的两个重要因素。急性应激导致交感神经系统激活以及儿茶酚胺和糖皮质激素分泌增加,因而影响对威胁刺激的注意偏向。但是急性应激如何影响注意偏向中的注意定向和注意解除尚不清楚。本项目采用点探测任务、威胁线索空间提示任务,结合恐惧条件反射、眼动和事件相关电位技术,研究急性应激对注意定向和注意解除影响的认知神经机制。研究结果可为治疗焦虑和创伤后应激障碍提供支持,为公共卫生管理政策的制定提供建议。     

情节式未来思考中目标加工研究进展

情节式未来思考是个体将自我投射到未来以预先经历某事件的能力,个人目标加工在情节式未来思考中发挥着重要作用。目前对情节式未来思考中目标加工的行为学研究通常将其划分为目标过程模拟和目标结果模拟两种类型,以探究其对执行意图的影响为主;脑机制神经影像学研究则聚焦于默认网络的内侧前额叶和后扣带的重要作用,以及默认网络、额顶控制网络和奖赏区的协同作用。未来研究应进一步探讨情节式未来思考目标加工的形式、年龄差异及其与时间距离的关系。     

Diversity and Community: The Role of Agent‐Based Modeling

Community psychology involves several dialectics between potentially opposing ideals, such as theory and practice, rights and needs, and respect for human diversity and sense of community. Some recent papers in the American Journal of Community Psychology have examined the diversity‐community dialectic, some with the aid of agent‐based modeling and concepts from network science. This paper further elucidates these concepts and suggests that research in community psychology can benefit from a useful dialectic between agent‐based modeling and the real‐world concerns of community psychology.     

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。     

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.