基于时空变化的步态识别算法研究

本文提出了一种八椭圆人体模型,并在此基础上提出了基于变化信息的步态识别算法。对每个视频序列,采用基于贝叶斯规则的检测算法检测出目标人体区域;将目标人体区域按比例划分为八个区域并分别用椭圆拟合,建立人体椭圆模型;用人体姿势的时空变化——相邻帧间的模型参数的差值作为特征,用归一化后的Mahalanobis距离和Euclidean距离分别进行相似性度量,NN和KNN技术用于最终的分类。实验结果表明,该算法拥有较高的识别率和较低的计算代价。     

Bayesian power equivalence in latent growth curve models

Longitudinal studies are the gold standard for research on time-dependent phenomena in the social sciences. However, they often entail high costs due to multiple measurement occasions and a long overall study duration. It is therefore useful to optimize these design factors while maintaining a high informativeness of the design. Von Oertzen and Brandmaier (2013,Psychology and Aging, 28, 414) applied power equivalence to show that Latent Growth Curve Models (LGCMs) with different design factors can have the same power for likelihood-ratio tests on the latent structure. In this paper, we show that the notion of power equivalence can be extended to Bayesian hypothesis tests of the latent structure constants. Specifically, we show that the results of a Bayes factor design analysis (BFDA; Schönbrodt & Wagenmakers (2018,Psychonomic Bulletin and Review, 25, 128) of two power equivalent LGCMs are equivalent. This will be useful for researchers who aim to plan for compelling evidence instead of frequentist power and provides a contribution towards more efficient procedures for BFDA.     

Experiences of the Risen Jesus: The Foundational Historical Issue in the Early Proclamation of the Resurrection

Abstract : That Jesus' disciples experienced what they believed were appearances of the risen Jesus after his death is generally conceded by the critical scholarly community. The nature of these experiences is the key component in recent historical discussions. Drawn from a survey of recent scholarly resurrection studies, and building upon agreed data, various reasons are given to establish the reality of the disciples' experiences. Three major responses to the cause of these experiences are also outlined. The chief goal of this essay is not to decide between these three positions, but to narrow the focus of the current discussion.     

Bayesian test and Kuhn’s paradigm

Kuhn’s theory of paradigm reveals a pattern of scientific progress, in which normal science alternates with scientific revolution. But Kuhn underrated too much the function of scientific test in his pattern, because he focuses all his attention on the hypothetico-deductive schema instead of Bayesian schema. This paper employs Bayesian schema to re-examine Kuhn’s theory of paradigm, to uncover its logical and rational components, and to illustrate the tensional structure of logic and belief, rationality and irrationality, in the process of scientific revolution. __________ Translated from Ziran Bianzhengfa Tongxun 自然辩证法通讯 (Journal of Dialectics of Nature), 2005 (6) by Zhang Jianfeng, proofread by Wang Dong     

变量相对重要性评估的方法选择及应用

高维数据爆发的背景下,心理学研究目前急需变量相对重要性评估的有效方法。相对重要性评估的关键是选择合适的评估指标和统计推断方法。相对重要性的评估指标种类繁多,优势分析和相对权重是重点推荐的相对重要性评估指标。相对重要性的统计推断方法适用情境不同,Bootstrap抽样是推断单变量重要性和两变量重要性差异的常用方法,而贝叶斯检验是评估多变量重要性次序的新方法。线性回归模型之外,相对重要性研究已拓展到Logistic回归模型、结构方程模型、多水平模型等,但适用数据类型仍较为有限。相对重要性评估已广泛应用于心理学实证研究,但存在不恰当的指标解释和方法选择问题。为此,结合具体例子说明变量相对重要性的评估过程。     

Integrating situational probability and kinematic information when anticipating disguised movements

ObjectivesThe current study sought to examine the relative contributions of kinematic and situational probability information to anticipation using different levels of disguised kinematics. More specifically, it was tested whether the weighting of the informational sources (kinematic vs. probabilistic) shifts relative to the certainty of the available kinematic information.Design and MethodHuman-like avatars were generated performing penalty throws and displayed in a virtual reality environment. The ambiguity of the kinematic information available from the avatars was systematically manipulated using linear morphing between genuine and disguised throws. In a perceptual classification task, trained novice observers (N = 23) were asked to classify as quickly and accurately as possible whether observed throws were either genuine or disguised. In addition, information about the performer’s action preferences was also systematically manipulated by explicitly informing participants about the performer’s AP to disguise their throw (25%, 50%, and 75%).ResultsParticipants’ response behavior showed that observers relied more heavily on the probabilistic information when the kinematics were ambiguous. For the AP 25% condition, observers were more likely to report that ambiguous throws were genuine (p < 0.001), whereas they classified the ambiguous throws as being disguised in the AP 75% condition (p < 0.001).ConclusionFindings suggest that observers rely more strongly on non-kinematic (situational probability) information when the reliability of the observable movement kinematics becomes less certain.     

Bayesian estimation of a multilevel IRT model using gibbs sampling

In this article, a two-level regression model is imposed on the ability parameters in an item response theory (IRT) model. The advantage of using latent rather than observed scores as dependent variables of a multilevel model is that it offers the possibility of separating the influence of item difficulty and ability level and modeling response variation and measurement error. Another advantage is that, contrary to observed scores, latent scores are test-independent, which offers the possibility of using results from different tests in one analysis where the parameters of the IRT model and the multilevel model can be concurrently estimated. The two-parameter normal ogive model is used for the IRT measurement model. It will be shown that the parameters of the two-parameter normal ogive model and the multilevel model can be estimated in a Bayesian framework using Gibbs sampling. Examples using simulated and real data are given.     

Bayesian estimation in the three-parameter logistic model

A joint Bayesian estimation procedure for the estimation of parameters in the three-parameter logistic model is developed in this paper. Procedures for specifying prior beliefs for the parameters are given. It is shown through simulation studies that the Bayesian procedure (i) ensures that the estimates stay in the parameter space, and (ii) produces better estimates than the joint maximum likelihood procedure as judged by such criteria as mean squared differences between estimates and true values. The research reported here was performed pursuant to Grant No. N0014-79-C-0039 with the Office of Naval Research. A related article by Robert J. Mislevy (1986) appeared when the present paper was in the printing stage.     

Predictive coding explains binocular rivalry: an epistemological review

Binocular rivalry occurs when the eyes are presented with different stimuli and subjective perception alternates between them. Though recent years have seen a number of models of this phenomenon, the mechanisms behind binocular rivalry are still debated and we still lack a principled understanding of why a cognitive system such as the brain should exhibit this striking kind of behaviour. Furthermore, psychophysical and neurophysiological (single cell and imaging) studies of rivalry are not unequivocal and have proven difficult to reconcile within one framework. This review takes an epistemological approach to rivalry that considers the brain as engaged in probabilistic unconscious perceptual inference about the causes of its sensory input. We describe a simple empirical Bayesian framework, implemented with predictive coding, which seems capable of explaining binocular rivalry and reconciling many findings. The core of the explanation is that selection of one stimulus, and subsequent alternation between stimuli in rivalry occur when: (i) there is no single model or hypothesis about the causes in the environment that enjoys both high likelihood and high prior probability and (ii) when one stimulus dominates, the bottom-up, driving signal for that stimulus is explained away while, crucially, the bottom-up signal for the suppressed stimulus is not, and remains as an unexplained but explainable prediction error signal. This induces instability in perceptual dynamics that can give rise to perceptual transitions or alternations during rivalry.