The knowledge content of statistical data

An information-theoretic framework is used to analyze the knowledge content in multivariate cross classified data. Several related measures based directly on the information concept are proposed: the knowledge content (S) of a cross classification, its terseness (Zeta), and the separability (Gamma _X ) of one variable, given all others. Exemplary applications are presented which illustrate the solutions obtained where classical analysis is unsatisfactory, such as optimal grouping, the analysis of very skew tables, or the interpretation of well-known paradoxes. Further, the separability suggests a solution for the classic problem of inductive inference which is independent of sample size.     

Effects of lines of progress and semilogarithmic charts on ratings of charted data

The extent to which interrater agreement and ratings of significance on both changes in level and trend are affected by lines of progress and semilogarithmic charts was investigated. Thirteen graduate students rated four sets of charts, each set containing 19 phase changes. Set I data were plotted on equal interval charts. In Set II a line of progress was drawn through each phase on each chart. In Set III data points were replotted on semilogarithmic charts. In Set IV a line of progress was drawn through each phase of each Set III chart. A significant main effect on interrater agreement was found for lines of progress as well as a significant 2-way interaction between lines of progress and change type. Three main effects (chart type, lines of progress, and type of change) and a significant 3-way interaction were found for ratings of significance. Implications of these data for visual analysis of charted data are discussed.     

Statistical inference for false positive and false negative error rates in mastery testing

This paper describes an asymptotic inferential procedure for the estimates of the false positive and false negative error rates. Formulas and tables are described for the computations of the standard errors. A simulation study indicates that the asymptotic standard errors may be used even with samples of 25 cases as long as the Kuder-Richardson Formula 21 reliability is reasonably large. Otherwise, a large sample would be required.This work was performed pursuant to Grant No NIE-G-78-0087 with the National Institute of Education, Department of Health, Education and Welfare, Huynh Huynh, Principal Investigator. Points of view or opinions stated do not necessarily reflect NIE position or policy and no official endorsement should be inferred. The editorial assistance of Joseph C. Saunders is gratefully acknowledged.     

Learning How to Generalize

Generalization is a fundamental problem solved by every cognitive system in essentially every domain. Although it is known that how people generalize varies in complex ways depending on the context or domain, it is an open question how people learn the appropriate way to generalize for a new context. To understand this capability, we cast the problem of learning how to generalize as a problem of learning the appropriate hypothesis space for generalization. We propose a normative mathematical framework for learning how to generalize by learning inductive biases for which properties are relevant for generalization in a domain from the statistical structure of features and concepts observed in that domain. More formally, the framework predicts that an ideal learner should learn to generalize by either taking the weighted average of the results of generalizing according to each hypothesis space, with weights given by how well each hypothesis space fits the previously observed concepts, or by using the most likely hypothesis space. We compare the predictions of this framework to human generalization behavior with three experiments in one perceptual (rectangles) and two conceptual (animals and numbers) domains. Across all three studies we find support for the framework's predictions, including individual‐level support for averaging in the third study.     

The body as laboratory: Prediction-error minimization,embodiment, and representation

In his (2014) paper, Jakob Hohwy outlines a theory of the brain as an organ for prediction-error minimization (PEM), which he claims has the potential to profoundly alter our understanding of mind and cognition. One manner in which our understanding of the mind is altered, according to PEM, stems from the neurocentric conception of the mind that falls out of the framework, which portrays the mind as “inferentially-secluded” from its environment. This in turn leads Hohwy to reject certain theses of embodied cognition. Focusing on this aspect of Hohwy’s argument, we first outline the key components of the PEM framework such as the “evidentiary boundary,” before looking at why this leads Hohwy to reject certain theses of embodied cognition. We will argue that although Hohwy may be correct to reject specific theses of embodied cognition, others are in fact implied by the PEM framework and may contribute to its development. We present the metaphor of the “body as a laboratory” in order to highlight what we believe is a more significant role for the body than Hohwy suggests. In detailing these claims, we will expose some of the challenges that PEM raises for providing an account of representation.     

阅读理解困难儿童的认知加工

阅读理解困难是阅读障碍的一个主要类型,近年来受到西方研究者的重视。阅读理解认知加工,核心是有层次的语义加工,理解困难儿童在词汇语义加工和篇章推理和整合上都存在落后。理解困难儿童的信息推理水平落后,可能原因来自两个方面：一方面是语音工作记忆缺陷导致推理加工能力缺陷,另一方面是阅读元认知缺陷导致阅读目的和理解监控发展不正常。这些缺陷之间复杂的关系,还需要进一步研究     

类别标签与典型性程度对类别特征推理的影响

本研究采用学习-测试二阶段实验范式,探讨了类别标签及典型性程度对类别特征推理任务的共同影响,结果表明,类别标签及典型性程度都会影响类别特征推理任务,而且,类别标签及典型性程度之间存在交互作用,典型性程度为高条件下类别标签对类别特征推理任务的影响要高于典型性程度为低条件,类别标签匹配条件下,典型性程度对类别特征推理任务的影响要高于类别标签不匹配条件。     

CRITICAL REALISM REDUX: A RESPONSE TO JOSH REEVES

This article combines an appreciation of several themes in Josh Reeves's Against Methodology in Science and Religion: Recent Debates on Rationality and Theology while arguing in favor of critical realism. The author holds that critical realism manages to combine the objective truth reached through inference and especially cognitive acts of judgment as well as the various, contingent historical contexts that also define where science is practiced. Reeves advocates a historical perspective, but this article claims that in order for critical realism to be credible, a philosophical perspective must be maintained.     

THEORIZING RELIGION AND QUESTIONING THE FUTURE OF ISLAM AND SCIENCE

Will there be any joint future for science and Islam? Although such questions have recently received considerable attention, more basic questions are often ignored. This article aims at addressing some of those more basic questions through exploring the assumptions that underlie different possible understandings of the question about the future of Islam and science. By investigating the relation between conceptualizations of religion and the question about the future of Islam and science, it will be argued that different understandings of the concept of religion (i.e., whether it denotes real objects, whether it is universal, and whether it is belief centered) lead to extremely different readings of the question. Besides, it will be argued that different answers to the question about the future of Islam and science can be understood in terms of the inference to best theological explanation; thus, the criteria that one assumes for the best theological explanation result in different criteria for evaluation of the answers.     

Valuing intervention and observation

Understanding causal relations is fundamental to effective action but causal data can be confounded. We examined the value that participants placed on data derived from a hypothetical intervention or observation. Our materials involved a possible cause (“bottled water”), a possible confound (“food”), and a context (“a restaurant”). We supposed that participants seek to draw as specific a causal inference as possible from presented data and value information sources more highly that allow them to do so. On this basis, we predicted that in circumstances where an intervention removed the confounding causal factor but observation did not, participants would prefer data derived from an intervention when the possible cause was present (the bottled water was drunk) but show the reverse preference when the possible cause was absent (the bottled water was not drunk). Experiment 1 confirmed this prediction. Using a between-subjects design, Experiment 2 tested for a difference in confidence in causal judgements given identical data, including data on the confound, as a function of method of data collection (intervention or observation). There was no significant difference in confidence ratings between the two methods but confidence ratings were sensitive to the probability of an effect (illness) given the cause. Using a within-subjects design, Experiment 3 revealed systematic individual differences in preference for the two methods. Participants were divided between those who considered intervention more confounded and those who considered observation more confounded. Our experiments point to the subtleties of participants' evaluation of data from studies of human beings.