Interlinking physical beliefs: children's bias towards logical congruence

Young children's na?ve beliefs about physics are commonly studied as isolated pieces of knowledge. The current paper takes a different approach. It asks whether preschoolers interlink individual beliefs into larger configurations or Gestalts. Such Gestalts bring together knowledge such as how an object's mass relates to its sinking speed, how an object's volume relates to its sinking speed, and how mass and volume are correlated. The particular form of organization explored here is referred to as logical congruence, the logical correspondence in directions among three physical relations. Are children's guesses about one physical relation congruent with their beliefs about the other two relations? And can they learn a congruent set of relations more readily than an incongruent set? Two different physical domains were explored, one in which children commonly hold pre-existing beliefs, and one in which they are likely to lack such beliefs. The results in both domains show a strong bias towards congruent knowledge configurations in young children. These findings may explain children's difficulties learning inherently incongruous concepts such as density.     

Generalized multilevel structural equation modeling

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.gllamm can be downloaded from http://www.gllamm.org. The paper was written while Sophia Rabe-Hesketh was employed at and Anders Skrondal was visiting the Department of Biostatistics and Computing, Institute of Psychiatry, King's College London.     

Extracting a maximum of useful information from statistical research data

The practice of statistical inference in psychological research is critically reviewed. Particular emphasis is put on the fast pace of change from the sole reliance on null hypothesis significance testing (NHST) to the inclusion of effect size estimates, confidence intervals, and an interest in the Bayesian approach. We conclude that these developments are helpful for psychologists seeking to extract a maximum of useful information from statistical research data, and that seven decades of criticism against NHST is finally having an effect.     

Bayes Factors Have Frequency Properties—This Should Not Be Ignored: A Rejoinder to Morey,Wagenmakers, and Rouder

Hoijtink, van Kooten, and Hulsker (2016 Hoijtink, H., van Kooten, P., &; Hulsker, K. (2016). Why Bayesian psychologists should change the way they used the Bayes factor. Multivariate Behavioral Research, 51, 1--9. doi: 10.1080/00273171.2014.969364.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) outline a research agenda for Bayesian psychologists: evaluate and use the frequency properties of Bayes factors. Morey, Wagenmakers, and Rouder (2016 Morey, R. D., Wagenmakers, E. -J., &; Rouder, J. N. (2016). Calibrated Bayes factors should not be used: A reply to Hoijtink, van Kooten, and Hulsker. Multivariate Behavioral Research, 51, 10--17. doi: 10.1080/00273171.2015.1052710.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) respond that Bayes factors calibrated using frequency properties should not be used. This paper contains the response of Hoijtink, van Kooten, and Hulsker to the criticism of Morey, Wagenmakers, and Rouder (2016 Morey, R. D., Wagenmakers, E. -J., &; Rouder, J. N. (2016). Calibrated Bayes factors should not be used: A reply to Hoijtink, van Kooten, and Hulsker. Multivariate Behavioral Research, 51, 10--17. doi: 10.1080/00273171.2015.1052710.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]).     

什么是智力？成年人的朴素智力理论

朴素智力理论是没有系统学习科学智力理论的成年人对智力的经验性认识。对成年人朴素智力理论的研究有助于了解个体如何建立对客观世界的理解。本文从对智力本质的理解、朴素智力理论的影响因素、对自己或他人智商的估计三个视角介绍了有关成年人朴素智力理论的研究。成人对智力本质的理解有跨文化的一致性,大多包括问题解决、言语能力、社会能力三个方面,这与科学智力理论有一定的相似;成人的朴素智力理论也受到环境、文化、“聪明人”原型年龄的影响;成人对智商的估计有中等程度的准确性。总的来说,成年人对智力的朴素认识是比较准确的。不同层面的个体经验很可能塑造了智力的朴素理论。     

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.     

The larger the cause,the larger the effect: evidence of speed judgment biases in causal scenarios

ABSTRACT

When two motions appear to be causally related, the spatiotemporal features of motions are sometimes distorted in order to increase the consistency with causal impressions. Here, in four experiments, we tested if varying the speed of an object A could affect the judged speed of an object B that appeared to be causally related to A. Participants were presented with classic launching stimuli (Experiment 1), a variant of launching stimuli in which A could move with uniformly accelerated or decelerated motion (Experiment 2), non-launching stimuli that elicited a causal impression (Experiment 3), and stimuli showing a three-object launching event (Experiment 4). Main results showed that the judged speed of B was systematically biased towards the speed of A, and moreover that the judged speed of B depended on the average speed of A, rather than on the speed of A at the moment of collision as it would be predicted by Newtonian mechanics. The results are consistent with the hypothesis that internal representations of causal events based on property transmission (for instance, impetus) can affect judgments of the low-level properties of causal scenarios.     

An extended application ‘Brain Q’ processing EEG and MEG data of finger stimulation extended from ‘Zeffiro’ based on machine learning and signal processing

GoalTo apply signal processing and machine learning skills and knowledge in processing the EEG and MEG signal and further localize and evaluate the source of the finger stimulation.MethodsCognitive control is usually applied in information processing and behavioral response. In the preprocessing, baseline correction is implemented to analyze the pre-stimuli, combining ERP to mark the event related potential, studying the time-locked only behavior. Z-score transform, coherence and spec trum are calculated and analyzed in the functional connectivity analysis.In addition to the functional analysis, Bayes Optimizer evaluates the neuro imaging according to the hierarchical Bayes. The introduction of the application is described from both user and developer’s prospects. Results: Introduction of both user and developers aspects, on its modules from pre-processing, functional analysis and results visualization and evaluation is conducted with one specific clinical data case, including the correlation is higher especially on gamma band and the MVAR coherence on the whole source space depicting the relation between different regions, especially on somatosensory (compared by thalamus) when stimulated by finger activity, phase-lock property of the E/MEG signal and etc. Compared to a manual selection, the scaling parameter prediction can be improved with support vector machine (SVM). The evaluation results with Bayes Optimization, location prediction is superior in the somatosensory area and in the thalamus, the total reconstructed source space is larger, one of the realization of cognitive system comparing different kernels and classifiers. The SVM and discriminant classifier gives similar results evaluating the dipole localization and the parameter choice related as well to the shape parameter, noise level, hyperprior and etc.ConclusionApproaches of Brain Q are found to be suitable for pre-processing for the EEG and MEG data. The system is capable of functional analysis including coherence and spectral related computation. Machine learning techniques are conducted as well to analyze and evaluate the result of the dipole reconstruction and help to predict the better model parameters and the localization of the origin dipoles. A case on finger stimulation clinical data is conducted and the results of the analysis temporarily and spatially manifests its functionality for users and potential extensions for developers.     

Bayesian evaluation of informative hypotheses for multiple populations

The software package Bain can be used for the evaluation of informative hypotheses with respect to the parameters of a wide range of statistical models. For pairs of hypotheses the support in the data is quantified using the approximate adjusted fractional Bayes factor (BF). Currently, the data have to come from one population or have to consist of samples of equal size obtained from multiple populations. If samples of unequal size are obtained from multiple populations, the BF can be shown to be inconsistent. This paper examines how the approach implemented in Bain can be generalized such that multiple-population data can properly be processed. The resulting multiple-population approximate adjusted fractional Bayes factor is implemented in the R package Bain.     

Children's and adults' understanding of the impact of nutrition on biological and psychological processes

Four studies examined children's and adults' beliefs about the impact of nutrition on growth and mood states. In Studies 1 and 2, 271 participants (preschoolers through adults) judged the impact of healthy and unhealthy nutrition on height and weight. In Studies 3 and 4, 267 participants judged the impact of healthy and unhealthy nutrition on positive and negative mood states. The results suggest that young children demonstrate a co‐existence of an ontologically distinct theory of biology as well as a theory of cross‐domain interaction when reasoning about the impact of food on biological and psychological processes.