The Effect of Interpolated Motor Activities in Short-Term Motor Memory

This experiment examined the effect of an interpolated motor response on short-term retention of a blind positioning movement. The interpolated response was varied in degree of similarity to the response length (± 25, ± 50, and ± 100 mm.), in number of repetitions (0, 1, or 5), and in temporal position (immediate or delayed), with a retention interval of 25 sec. Absolute error was not affected but a systematic change in response bias due to interpolated motor activity was found. When an interpolated response was longer than the response length, the tendency to undershoot decreased. The defay of the interpolated response was relatively more important than the number of repetitions in producing a positive shift in response bias. These results are interpreted as evidence for a form of interference in short-term motor memory.     

Reaction Time Analysis of Two Types of Motor Preparation for Speech Articulation: Action as a Sequence of Chunks

Reaction time (RT) prior to speech articulation increased as a function of response complexity. The RT findings formed 2 patterns, each of which was a different Response Complexity × Paradigm (choice RT vs. simple RT) interaction. That result extends previous findings from manual button-pressing tasks (S. T. Klapp, 1995) to a different action modality. Two different types of response programming, INT and SEQ, are assumed in the interpretation. Whereas INT can be identified with response programming within a word, SEQ fits a different interpretation related to timing of onsets of speech units. A critical assumption is that a long response is represented as a sequence of chunks; that organization is subject to manipulation. New findings suggest some modifications of the previous theory.     

Differential Method of Characterizing Gait Strategies From Step Lengths and Frequencies: Strategy of Velocity Modulation

The differential method consists of the analysis of the variation of gait parameters length, frequency, and velocity with respect to their mean values, respectively, ΔL = L — L_m , Δf = f — f_m , and Δv = v – v_m , where L_m , f_m , and v_m represent the mean values of those parameters. Assuming that the strategy of modulation of velocity implies that L and f are functions of v and that statistical analyses of ratios ΔL/Δv and Δf/Δv have established that there is a very significant linear correlation, close to 1, between those ratios, the mathematical procedure allows one to determine the equation of step length, L = a · f + b · v + K, where a and b are the slope and the intercept of the linear regression and K is close to L_m . The equation was experimentally tested on 140 gait sequences performed by 6 participants and for gait velocities ranging from 0.6 to 2.2 m/s and was found to be very representative of all individual values. The differential method provides another way of using the derivative of velocity, v = L·f, to characterize the strategy of velocity modulation, which then permits one to determine the linear equation of velocity, v = f · L_m + L · f_m — L_m · f_m , and to show that the respective parts played by each parameter in the progression velocity are approximately equal. The author establishes the uniqueness of the different linear adjustments and discusses the differential method's own modes of use, that is, interindividually or globally.     

Lay Personality Theories in Interactive Decisions: Strongly Held,Weakly Supported

In interactive decisions, cues to what others will do are important in forming a strategy. Information about others' personalities appears to be potentially valuable for this purpose. We report a series of four studies examining how information about another actor's personality influences people's own choices in interactive decisions. The studies found widespread beliefs that others' personality characteristics are strongly predictive both of broad classes of decision behavior (competition/cooperation, risk‐seeking/risk‐aversion) (Study 1) and of specific choices (Study 2) in single‐agent settings. These beliefs extended to predicting others' choices in interactive decisions (Study 3) and to shaping the predictor's own decisions in interactive play in Chicken and Assurance games (Study 4). Overall, we found extensive evidence that laypeople believe that the personality traits we selected (angry‐hostility, anxiety, assertiveness, excitement‐seeking, and warmth) have substantial effects on behavior in interactive decisions and they act on those beliefs when making their own decisions. The empirical evidence supporting the predictive validity of these traits was, however, quite weak. Copyright © 2014 John Wiley & Sons, Ltd.     

Multiobjective optimization for interwoven systems

In practical situations, complex systems are often composed of subsystems or subproblems with single or multiple objectives. These subsystems focus on different aspects of the overall system, but they often have strong interactions with each other and they are usually not sequentially ordered or obviously decomposable. Thus, the individual solutions of subproblems do not generally induce a solution for the overall system. Here, we strive to identify “re‐composition architectures” of such “interwoven” systems. Our intention is to connect the subsystems adequately, analyze the resulting performance, model/solve the overall system, and improve the overall solution instead of just solving each subsystem separately. We review recent developments in this field and discuss modeling and solution paradigms in a general and unified framework using the example of an interwoven system consisting of two interacting subsystems.     

运用基因表达式编程的自陈量表数据建模

探讨基因表达式编程对自陈量表测量数据的建模方法。运用威廉斯创造力测验和认知需求量表获得400位中学生的测量分数,通过数据清洗,保留383个被试的分数作为建模的数据集。运用哈曼单因素检验方法没有发现共同方法偏差。采用均匀设计方法对基因表达式编程中的5个参数进行优化配置,在测试拟合度最大的试验条件下,找到了测试误差最小的模型。比较基因表达式编程和BP神经网络、支持向量回归机、多元线性回归、二次多项式回归所建模型的预测精度。研究表明,基因表达式编程能用于自陈量表测量数据的建模,该模型比传统方法所建的模型具有更高的预测精度,而且模型是稳健的。     

A review of multiobjective programming and its application in quantitative psychology

Multiobjective programming, a technique for solving mathematical optimization problems with multiple conflicting objectives, has received increasing attention among researchers in various academic disciplines. A summary of multiobjective programming techniques and a review of their applications in quantitative psychology are provided.     

多媒体互动插图对科学说明文学习的影响

48名 1 3岁中学生被分为高低两种学习能力 ,每种被试又被分成两组分别学习配有两种插图 (多媒体静态插图和多媒体互动插图 )的一篇生物说明文 ,结果表明 :( 1 )在重在考查插图加工水平的图片测验上 ,学习能力和插图类型主效应均显著 ,但两者之间不存在显著交互作用 ;( 2 )在重在考查文图整合加工水平的文字测验上 ,学习能力和插图类型之间存在显著的交互作用 ,即只有当学习能力高时 ,两种插图之间存在显著差异。 ( 3 )在被试的填图过程中 ,被试的尝试次数与其插图加工时间及学习效果之间存在显著的相关。这说明 ,多媒体互动插图可促进所有被试对插图的加工 ,但只能促进学习能力高者对图文的整合加工 ,而且多媒体互动插图对学习的促进效果与被试加工插图时的实际加工深度有关     

A neural implementation of multi-adjoint logic programming

A neural net based implementation of propositional [0,1]-valued multi-adjoint logic programming is presented, which is an extension of earlier work on representing logic programs in neural networks carried out in [A.S. d'Avila Garcez et al., Neural-Symbolic Learning Systems: Foundations and Applications, Springer, 2002; S. Hölldobler et al., Appl. Intelligence 11 (1) (1999) 45–58]. Proofs of preservation of semantics are given, this makes the extension to be well-founded.The implementation needs some preprocessing of the initial program to transform it into a homogeneous program; then, transformation rules carry programs into neural networks, where truth-values of rules relate to output of neurons, truth-values of facts represent input, and network functions are determined by a set of general operators; the net outputs the values of propositional variables under its minimal model.     

An encompassing framework for Paraconsistent Logic Programs

We propose a framework which extends Antitonic Logic Programs [Damásio and Pereira, in: Proc. 6th Int. Conf. on Logic Programming and Nonmonotonic Reasoning, Springer, 2001, p. 748] to an arbitrary complete bilattice of truth-values, where belief and doubt are explicitly represented. Inspired by Ginsberg and Fitting's bilattice approaches, this framework allows a precise definition of important operators found in logic programming, such as explicit and default negation. In particular, it leads to a natural semantical integration of explicit and default negation through the Coherence Principle [Pereira and Alferes, in: European Conference on Artificial Intelligence, 1992, p. 102], according to which explicit negation entails default negation. We then define Coherent Answer Sets, and the Paraconsistent Well-founded Model semantics, generalizing many paraconsistent semantics for logic programs. In particular, Paraconsistent Well-Founded Semantics with eXplicit negation (WFSX_p) [Alferes et al., J. Automated Reas. 14 (1) (1995) 93–147; Damásio, PhD thesis, 1996]. The framework is an extension of Antitonic Logic Programs for most cases, and is general enough to capture Probabilistic Deductive Databases, Possibilistic Logic Programming, Hybrid Probabilistic Logic Programs, and Fuzzy Logic Programming. Thus, we have a powerful mathematical formalism for dealing simultaneously with default, paraconsistency, and uncertainty reasoning. Results are provided about how our semantical framework deals with inconsistent information and with its propagation by the rules of the program.