假言推理中的概率效应

近来Oaksford等人的研究表明,概率信息对假言推理的认知过程有着显著的影响,在一定程度上可以用它来解释和预测个体在推理中的行为反应。该文针对这种新的观点,就假言推理中所涉及的前后件概率信息、条件概率信息、连接概率信息、因果概率信息以及额外前提条件等对假言推理的影响分别做了简要的阐述,并介绍了一些相关的实验研究,最后作了总结和展望。     

归纳推理心理效应的研究

归纳推理心理效应指的是归纳论断中各种因素对个体做出归纳结论时把握性（力度）大小的影响,根据影响因素的不同,主要可分为类别效应、属性效应和交互效应三种。文章认为,个体通常会采用分割－综合方式判断归纳力度大小,并且在归纳判断中形成的推理序列及由此造成的顺畅感直接影响着归纳力度的估计,作者还提出了对此模型的一种证明     

关键词对精加工推理的影响作用初探

本实验研究在文本中突出关键词对精加工推理的产生水平和产生阶段的影响。研究采用了让被试阅读后做测试题的方法,通过分析测试题的成绩得出的结果表明：在文章中突出关键词有助于产生近推理和内涵推理,且使这两类推理更有可能在阅读过程中产生。本研究补充了McDaniel和Donnelly 1996年的研究结果。     

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.     

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.