Component Models for Fuzzy Data

The fuzzy perspective in statistical analysis is first illustrated with reference to the “Informational Paradigm” allowing us to deal with different types of uncertainties related to the various informational ingredients (data, model, assumptions). The fuzzy empirical data are then introduced, referring to J LR fuzzy variables as observed on I observation units. Each observation is characterized by its center and its left and right spreads (LR₁ fuzzy number) or by its left and right “centers” and its left and right spreads (LR₂ fuzzy number). Two types of component models for LR₁ and LR₂ fuzzy data are proposed. The estimation of the parameters of these models is based on a Least Squares approach, exploiting an appropriately introduced distance measure for fuzzy data. A simulation study is carried out in order to assess the efficacy of the suggested models as compared with traditional Principal Component Analysis on the centers and with existing methods for fuzzy and interval valued data. An application to real fuzzy data is finally performed. We would like to express our gratitude to the Editor, the Associate Editor, and the Referees whose comments and suggestions improved significantly the quality of the paper.     

飞行错觉水平评定方法的初步研究

本文从飞行错觉发生的类型和频率出发,尝试对飞行员在实际飞行中所产生的错觉水平或空间定向能力做出基本的评定。本研究采用区间模糊统计的方法,对飞行错觉的类型（倾斜错觉、俯仰错觉、方位错觉、反旋转错觉和倒飞错觉）和频率（从无、偶尔、时有、经常、总是）做了经验的赋值工作,分别计算出了这些模糊概念（除从无外）的心理量表值和模糊度。同时我们认为,飞行错觉水平是频率与类型的函数,其数学表达式为：,并将这一表达式作为评定飞行员飞行错觉水平的基本模型。     

语义联系对关联再认神经机制的调节

已有研究显示,关联再认可记录到分别反映双重加工模型的两阶段（即熟悉和回忆）的ERP成分（即FN400和LPC成分）,且具有高语义联系配对的关联再认可通过整合在基于熟悉加工的基础上完成。然而,不同语义联系对关联再认神经机制的影响及单个项目的熟悉和回忆加工的调节作用尚未揭示。为此,本研究比较了主题相关和类别相关图形关联再认的波形波幅异同,并分析了单个项目的熟悉和回忆加工对配对识别的影响。结果显示,主题相关和类别相关条件的旧配对、重组配对和“旧+新”配对均记录到反映熟悉加工的FN400成分,主题相关条件的旧配对、重组配对和“旧+新”配对均记录到反映回忆加工的LPC成分,类别相关条件的旧配对记录到LPC成分。表明关联再认的神经机制受语义联系所调节,单个项目的熟悉程度可影响配对识别时的神经机制,上述结论支持双重加工模型。     

判断与决策研究纵观:基于模糊痕迹理论的视角(英文)

模糊痕迹理论是用于解释记忆、判断与决策的综合性理论,该理论的提出和发展主要基于对信息存储、表征、提取和加工过程的研究。本文首先介绍了模糊痕迹理论的基本原则,在此基础上重点讨论了其要义(gist)如何发挥核心作用,使得模糊痕迹理论有别于其他传统的决策模型。该理论将高级直觉与原始冲动性进行了区分,并且预测决策误差来源于判断与决策的各种不同成分,如背景知识、信息表征、提取和加工过程等。模糊痕迹理论不仅可以解释诸如框架效应、合取谬误等传统决策与判断文献中常讨论的误差现象,同时基于该理论的研究还得到了一些与传统决策理论相悖的新发现。此外,对脑与行为如何发育性变化的研究为我们了解成人的认知过程提供了至关重要的新视角,这些对脑与行为的发育性研究和对特殊人群的研究结果也都支持了模糊痕迹理论对要义加工依赖的预测。     

Negativity bias in false memory: moderation by neuroticism after a delay

The negativity bias is the tendency for individuals to give greater weight, and often exhibit more rapid and extreme responses, to negative than positive information. Using the Deese-Roediger-McDermott illusory memory paradigm, the current study sought to examine how the negativity bias might affect both correct recognition for negative and positive words and false recognition for associated critical lures, as well as how trait neuroticism might moderate these effects. In two experiments, participants studied lists of words composed of semantic associates of an unpresented word (the critical lure). Half of the lists were comprised of positive words and half were comprised of negative words. As expected, individuals remembered negative list words better than positive list words, consistent with a negativity bias in correct recognition. When tested immediately (Experiment 1), individuals also exhibited greater false memory for negative versus positive critical lures. When tested after a 24-hr delay (Experiment 2), individuals higher in neuroticism maintained greater false memory for negative versus positive critical lures, but those lower in neuroticism showed no difference in false memory between negative and positive critical lures. Possible mechanisms and implications for mental health disorders are discussed.     

Caregivers’ emotional competence and behavioral responsiveness as correlates of early childcare workers’ relationships with children in their care

In the current study, we examined associations among early childcare workers’ emotional competence, observed responsiveness, comfort with socioemotional teaching practices, and the quality of their relationships with children in their care. The participants were 100 early childcare workers (72 center-based Early Head Start teachers and 28 family childcare providers). Results showed that caregivers’ emotion regulation ability was positively associated with caregiver–child relational closeness. Understanding and regulation of emotion were both positively associated with childcare workers’ comfort with socioemotional teaching practices. Their observed responsiveness was positively related to relational closeness and negatively related to relational conflict. Findings are consistent with aspects of the prosocial classroom model, which asserts that educators high in emotional and social competence tend to adopt childcare practices that result in supportive relationships with children. Results provide insight into whether childcare workers’ responsiveness to young children and their perceived socioemotional teaching practices provide a pathway between emotional competence and the quality of caregiver–child relationships.     

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.     

Quality Instances and the Structure of the Concrete Particular

In this paper, I examine a puzzle that emerges from what J. P. Moreland has called the traditional realist view of quality instances. Briefly put, the puzzle is to figure out how quality instances fit into the overall structure of a concrete particular, given that the traditional realist view of quality instances prima facie seems incompatible with what might be called the traditional realist view of concrete particulars. After having discussed the traditional realist views involved and the puzzle that emerges from their juxtaposition, I propose an alternative realist view of quality instances which resolves the puzzle. In short, the puzzle is solved by treating the distinction between a concrete particular and its quality instances as a distinction of reason, and by adopting the view that the individuating element of a concrete particular must also serve as its unifying element – a view which Moreland, one of traditional realism’s most stalwart contemporary defenders, rejects.     

On the (numerical) ranking associated with any finite binary relation

We generalize the concept of a ‘ranking associated with a linear order’ from linear orders to arbitrary finite binary relations. Using the concept of differential of an object in a binary relation as theoretical primitive, we axiomatically introduce several measurement scales, some of which include the generalized ranking as a special case. We provide a computational formula for this generalized ranking, discuss its many elegant properties and offer some illustrating examples.     

Proper Semantics for Substructural Logics,from a Stalker Theoretic Point of View

We study filters in residuated structures that are associated with congruence relations (which we call -filters), and develop a semantical theory for general substructural logics based on the notion of primeness for those filters. We first generalize Stone’s sheaf representation theorem to general substructural logics and then define the primeness of -filters as being “points” (or stalkers) of the space, the spectrum, on which the representing sheaf is defined. Prime FL-filters will turn out to coincide with truth sets under various well known semantics for certain substructural logics. We also investigate which structural rules are needed to interpret each connective in terms of prime -filters in the same way as in Kripke or Routley-Meyer semantics. We may consider that the set of the structural rules that each connective needs in this sense reflects the difficulty of giving the meaning of the connective. A surprising discovery is that connectives , ⅋ of linear logic are linearly ordered in terms of the difficulty in this sense. Presented by Wojciech Buszkowski