Who caused it? Interpersonal causal inferences in young children

Three experiments were designed to test 4- and 6-year-old children's causal inferences in interpersonal settings where emotions (glad, angry, and sad) were effect responses. The results showed that emotion and orientation (towards or away from) were central cues, and that sex and age also were used to some extent. Cues related to regularity philosophic notions (e.g. David Hume), such as contiguity in time and space, and time order of cause and effect were little used by comparison. The results raise questions about the basic role attributed to regularity cues both by philosophers and psychologists, and suggest a multiple cue contribution rather than a basic cue generalization approach to causal cognition development.     

归因过程“背景效应假设”的初步实验研究

通过两个实验考证了归因过程的“背景效应假设”：实验1表明,当要求活动者和观察者采用同样的背景事件时．他们之间的归因差异消失了;实验2表明,当有其它的背景信息可供参考时,被试就不再完全按照Kelley所假定的一致性和区别性信息进行归因推断。     

Failure of Retrospective Inference in Rats' Taste Aversion

Three experiments explored the possibility of retrospective inference in the rat. Experiment 1 revealed that poisoning of an element of a taste compound after single compound poisoning enhanced aversion to the other element, and that presentation of an element without poison after the compound poisoning reduced aversion to the other element. These results were opposite to those predicted by retrospective inference. Experiment 2 eliminated some confounding variables and examined the effect of element poisoning after compound poisoning. The result again was opposite to what the retrospective view predicts. The results, however, accorded with the idea that within-compound learning was established during compound presentation, and subsequent poisoning or nonpoisoning of one element affected response to the other element via the within-compound learning. In Experiment 3, the possibility of within-compound learning was reduced by using sequential presentation of tastes, but there was no indication of retrospective inference even under this condition.     

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.     

语言理解中的预设加工

预设是指以交际双方的共同背景(共享的知识或信念)为前提的、听话者根据特定语言标记(触发标记)及其限定的对象(计算内容)而推理出的非外显意义。例如,“张明又触发标记发表了论文计算内容”引发了“张明之前发表过论文”的预设推论。理解者依赖触发标记通达交际双方的共同背景,并在计算内容上生成完整的预设;随后,理解者连接生成的预设和共同背景,更新两者之间的关系。触发标记范畴、共同背景与预设的关联度、共同背景类型,以及理解者的参与动机会影响不同阶段的预设加工。未来研究可以从三个角度进一步探讨预设加工的认知基础：(1)采用计算建模的方法,量化交际互动中听话者理解预设的过程(如观点采择);(2)采用脑成像技术,揭示预设加工过程的神经基础;(3)以特殊人群为研究对象,检验和修正预设加工的认知模型。     

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.