A Gibbs sampler for the multidimensional four-parameter logistic item response model via a data augmentation scheme

The four-parameter logistic (4PL) item response model, which includes an upper asymptote for the correct response probability, has drawn increasing interest due to its suitability for many practical scenarios. This paper proposes a new Gibbs sampling algorithm for estimation of the multidimensional 4PL model based on an efficient data augmentation scheme (DAGS). With the introduction of three continuous latent variables, the full conditional distributions are tractable, allowing easy implementation of a Gibbs sampler. Simulation studies are conducted to evaluate the proposed method and several popular alternatives. An empirical data set was analysed using the 4PL model to show its improved performance over the three-parameter and two-parameter logistic models. The proposed estimation scheme is easily accessible to practitioners through the open-source IRTlogit package.     

Re-framing the characteristics of concepts and their relation to learning and cognition in artificial agents

In this work, the problems of knowledge acquisition and information processing are explored in relation to the definitions of concepts and conceptual processing, and their implications for artificial agents.The discussion focuses on views of cognition as a dynamic property in which the world is actively represented in grounded mental states which only have meaning in the action context. Reasoning is understood as an emerging property consequence of actions-environment couplings achieved through experience, and concepts as situated and dynamic phenomena enabling behaviours.Re-framing the characteristics of concepts is considered crucial to overcoming settled beliefs and reinterpreting new understandings in artificial systems.The first part presents a review of concepts from cognitive sciences. Support is found for views on grounded and embodied cognition, describing concepts as dynamic, flexible, context-dependent, and distributedly coded.That is argued to contrast with many technical implementations assuming concepts as categories, whilst explains limitations when grounding amodal symbols, or in unifying learning, perception and reasoning.The characteristics of concepts are linked to methods of active inference, self-organization, and deep learning to address challenges posed and to reinterpret emerging techniques.In a second part, an architecture based on deep generative models is presented to illustrate arguments elaborated. It is evaluated in a navigation task, showing that sufficient representations are created regarding situated behaviours with no semantics imposed on data. Moreover, adequate behaviours are achieved through a dynamic integration of perception and action in a single representational domain and process.     

Bayesian benefits with JASP

We illustrate the Bayesian approach to data analysis using the newly developed statistical software program JASP. With JASP, researchers are able to take advantage of the benefits that the Bayesian framework has to offer in terms of parameter estimation and hypothesis testing. The Bayesian advantages are discussed using real data on the relation between Quality of Life and Executive Functioning in children with Autism Spectrum Disorder.     

Adaptive scaling of reward in episodic memory: a replication study

Reward is thought to enhance episodic memory formation via dopaminergic consolidation. Bunzeck, Dayan, Dolan, and Duzel [(2010). A common mechanism for adaptive scaling of reward and novelty. Human Brain Mapping, 31, 1380–1394] provided functional magnetic resonance imaging (fMRI) and behavioural evidence that reward and episodic memory systems are sensitive to the contextual value of a reward—whether it is relatively higher or lower—as opposed to absolute value or prediction error. We carried out a direct replication of their behavioural study and did not replicate their finding that memory performance associated with reward follows this pattern of adaptive scaling. An effect of reward outcome was in the opposite direction to that in the original study, with lower reward outcomes leading to better memory than higher outcomes. There was a marginal effect of reward context, suggesting that expected value affected memory performance. We discuss the robustness of the reward memory relationship to variations in reward context, and whether other reward-related factors have a more reliable influence on episodic memory.     

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.     

Free-energy and the brain

If one formulates Helmholtz’s ideas about perception in terms of modern-day theories one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory inputs and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain’s organisation and responses. In this paper, we suggest that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception, respectively, and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the system’s state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed by the brain and how minimisation of free-energy can explain its dynamics and structure.     

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。     

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.     

Using MCMC chain outputs to efficiently estimate Bayes factors

One of the most important methodological problems in psychological research is assessing the reasonableness of null models, which typically constrain a parameter to a specific value such as zero. Bayes factor has been recently advocated in the statistical and psychological literature as a principled means of measuring the evidence in data for various models, including those where parameters are set to specific values. Yet, it is rarely adopted in substantive research, perhaps because of the difficulties in computation. Fortunately, for this problem, the Savage–Dickey density ratio (Dickey & Lientz, 1970) provides a conceptually simple approach to computing Bayes factor. Here, we review methods for computing the Savage–Dickey density ratio, and highlight an improved method, originally suggested by Gelfand and Smith (1990) and advocated by Chib (1995), that outperforms those currently discussed in the psychological literature. The improved method is based on conditional quantities, which may be integrated by Markov chain Monte Carlo sampling to estimate Bayes factors. These conditional quantities efficiently utilize all the information in the MCMC chains, leading to accurate estimation of Bayes factors. We demonstrate the method by computing Bayes factors in one-sample and one-way designs, and show how it may be implemented in WinBUGS.