Sticking to the Evidence? A Behavioral and Computational Case Study of Micro‐Theory Change in the Domain of Magnetism

Constructing an intuitive theory from data confronts learners with a “chicken‐and‐egg” problem: The laws can only be expressed in terms of the theory's core concepts, but these concepts are only meaningful in terms of the role they play in the theory's laws; how can a learner discover appropriate concepts and laws simultaneously, knowing neither to begin with? We explore how children can solve this chicken‐and‐egg problem in the domain of magnetism, drawing on perspectives from computational modeling and behavioral experiments. We present 4‐ and 5‐year‐olds with two different simplified magnet‐learning tasks. Children appropriately constrain their beliefs to two hypotheses following ambiguous but informative evidence. Following a critical intervention, they learn the correct theory. In the second study, children infer the correct number of categories given no information about the possible causal laws. Children's hypotheses in these tasks are explained as rational inferences within a Bayesian computational framework.     

个体化医疗和大数据时代的机遇和挑战

随着分子生物学的发展,高通量测序技术的引入和“大数据”处理能力的提高,现代医学正在经历巨大的变革,由传统的标准化医疗模式向个体化医疗模式转变。随着人类基因组计划和DNA元素百科全书计划的完成及人类和肿瘤基因图谱的绘制,我们正逐步解开人类基因组的奥秘。这极大地推进了人们对疾病,尤其是对肿瘤的认识。作为疾病诊断的“金标准”,病理学也在经历着深刻的变革,逐渐向个体化病理学发展。个体化医疗时代的到来,使医学各领域和社会多方面都面临新的机遇和挑战。     

Categorization as causal reasoning⋆

A theory of categorization is presented in which knowledge of causal relationships between category features is represented in terms of asymmetric and probabilistic causal mechanisms. According to causal‐model theory, objects are classified as category members to the extent they are likely to have been generated or produced by those mechanisms. The empirical results confirmed that participants rated exemplars good category members to the extent their features manifested the expectations that causal knowledge induces, such as correlations between feature pairs that are directly connected by causal relationships. These expectations also included sensitivity to higher‐order feature interactions that emerge from the asymmetries inherent in causal relationships. Quantitative fits of causal‐model theory were superior to those obtained with extensions to traditional similarity‐based models that represent causal knowledge either as higher‐order relational features or “prior exemplars” stored in memory.     

Inferring causal networks from observations and interventions

Information about the structure of a causal system can come in the form of observational data—random samples of the system's autonomous behavior—or interventional data—samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people's ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of observational data. In three causal inference tasks, participants were to some degree capable of distinguishing between competing causal hypotheses on the basis of purely observational data. Performance improved substantially when participants were allowed to observe the effects of interventions that they performed on the systems. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision‐making and maximizing expected information gain. These analyses suggest that people can make rational causal inferences, subject to psychologically reasonable representational assumptions and computationally reasonable processing constraints.     

Adjusting for the correspondence bias: Effects of causal uncertainty, cognitive busyness, and causal strength of situational information

This research examined the conditions under which people who have more chronic doubt about their ability to make sense of social behavior (i.e., are causally uncertain; [Weary and Edwards, 1994] and [Weary and Edwards, 1996]) are more likely to adjust their dispositional inferences for a target’s behaviors. Using a cognitive busyness manipulation within the attitude attribution paradigm, we found in Study 1 that higher causal uncertainty predicted increased correction of dispositional inferences, but only when participants had sufficient attentional resources to devote to the task. In Study 2, we found that higher-causal uncertainty predicted greater inferential correction, but only when the additional information provided a more compelling alternative explanation for the observed behavior. Results of this research are discussed in terms of their relevance to the Causal Uncertainty (Weary & Edwards, 1994) and dispositional inference models.     

An Extension of Multiple Correspondence Analysis for Identifying Heterogeneous Subgroups of Respondents

An extension of multiple correspondence analysis is proposed that takes into account cluster-level heterogeneity in respondents’ preferences/choices. The method involves combining multiple correspondence analysis and k-means in a unified framework. The former is used for uncovering a low-dimensional space of multivariate categorical variables while the latter is used for identifying relatively homogeneous clusters of respondents. The proposed method offers an integrated graphical display that provides information on cluster-based structures inherent in multivariate categorical data as well as the interdependencies among the data. An empirical application is presented which demonstrates the usefulness of the proposed method and how it compares to several extant approaches. The work reported in this paper was supported by Grant 290439 and Grant A6394 from the Natural Sciences and Engineering Research Council of Canada to the first and third authors, respectively. We wish to thank Ulf B?ckenholt, Paul Green, and Marc Tomiuk for their insightful comments on an earlier version of this paper. We also wish to thank Byunghwa Yang for generously providing us with his data.     

The Role of Independence and Stationarity in Probabilistic Models of Binary Choice

After more then 50 years of probabilistic choice modeling in economics, marketing, political science, psychology, and related disciplines, theoretical and computational advances give scholars access to a sophisticated array of modeling and inference resources. We review some important, but perhaps often overlooked, properties of major classes of probabilistic choice models. For within‐respondent applications, we discuss which models require repeated choices by an individual to be independent and response probabilities to be stationary. We show how some model classes, but not others, are invariant over variable preferences, variable utilities, or variable choice probabilities. These models, but not others, accommodate pooling of responses or averaging of choice proportions within participant when underlying parameters vary across observations. These, but not others, permit pooling/averaging across respondents in the presence of individual differences. We also review the role of independence and stationarity in statistical inference, including for probabilistic choice models that, themselves, do not require those properties. Copyright © 2017 John Wiley & Sons, Ltd.     

高自尊的异质性研究述评

自尊不只有高低之分,很多研究者分别从实证研究的结果矛盾性、对自尊的概念及其测量方法的理论思考等方面论述了高自尊异质性存在的可能性,并分别从不同角度提出了高自尊的不同类型及其测量方法,如,不稳定的高自尊与稳定的高自尊、防御的高自尊与真诚的高自尊、相倚的高自尊与真正的高自尊、高外显低内隐自尊与高外显高内隐自尊、脆弱的高自尊与安全的高自尊等,其测量方法也因不同类型而不同。这些研究均说明高自尊存在异质性,需在未来的自尊研究中加以注意     

What mediation analysis can (not) do

The present article is concerned with a common misunderstanding in the interpretation of statistical mediation analyses. These procedures can be sensibly used to examine the degree to which a third variable (Z) accounts for the influence of an independent (X) on a dependent variable (Y) conditional on the assumption that Z actually is a mediator. However, conversely, a significant mediation analysis result does not prove that Z is a mediator. This obvious but often neglected insight is substantiated in a simulation study. Using different causal models for generating Z (genuine mediator, spurious mediator, correlate of the dependent measure, manipulation check) it is shown that significant mediation tests do not allow researchers to identify unique mediators, or to distinguish between alternative causal models. This basic insight, although well understood by experts in statistics, is persistently ignored in the empirical literature and in the reviewing process of even the most selective journals.     

群组发展模型——干预研究的新方法

群组发展模型(group-based trajectory model,GBTM)是一种用于研究群体异质性的新方法,旨在识别群体内遵循不同发展轨迹的亚组,并描绘亚组成员特征。这一模型为干预研究提供了新的研究视角,群组发展模型不仅可以探讨干预是否引起分组比例变化,也关注干预对个体的发展轨迹的影响,并评估干预对不同轨迹组的效果差异。