条件推理与概率判断

选取6个具体内容的条件命题和1个纯形式的条件命题作为实验材料,以大学生为被试,深入探讨了条件推理的认知加工机制。结果发现,大学生被试对四种推理形式的认可程度主要与范畴前提的概率值成正比,条件推理似乎是一种基于概率判断的直觉而高效的认知加工过程,进一步证实了Oaksford（2000）以及Evans（2003）等人的观点。     

Young children's use of statistical sampling evidence to infer the subjectivity of preferences

A crucial task in social interaction involves understanding subjective mental states. Here we report two experiments with toddlers exploring whether they can use statistical evidence to infer the subjective nature of preferences. We found that 2-year-olds were likely to interpret another person’s nonrandom sampling behavior as a cue for a preference different from their own. When there was no alternative in the population or if the sampling was random, 2-year-olds did not ascribe a preference and persisted in their initial beliefs that the person would share their own preference. We found similar but weaker patterns of responses in 16-month-olds. These results suggest that the ability to infer the subjectivity of preferences based on sampling information begins to emerge between 16 months and 2 years. Our findings provide some of the first evidence that from early in development, young children can use statistical evidence to make rational inferences about the social world.     

日常生活事件时间关系推理的初步研究

对时间推理的研究主要有两种范式,习俗周期性时间推理和时间关系推理。以往大多数研究集中在习俗周期性时间推理上,而时间关系推理研究则相对薄弱。本研究在以往研究的基础上,对日常生活事件的时间关系推理进行了研究。实验设计考虑了推理任务的模型数量、前提数量、无关前提以及有无肯定答案等因素,形成了六类推理任务。研究发现:推理任务的模型数量、前提数量、无关前提、是否有肯定答案以及推理者时间维度上的人格特征等都会影响个体的时间关系推理;被试一般是以呈现的前提顺序来表征推理任务的。但是,当被试意识到无关信息时,可以从整体上表征问题。     

条件推理的语义关系表达形式效应

作者用以大学生为被试的实验考察, 基本条件句语义关系表达形式(充分关系、必要关系和析取关系表达形式)和作为心理模型外显建构的可能性判断任务对条件推理的可能影响。实验结果发现, 条件推理的语义关系表达形式效应显著, 条件句语义关系表达形式对被试条件推理有显著的影响, 条件推理成绩随条件句语义关系表达的外显程度增加而增加; 被试在可能性判断任务中对条件句所含心理模型的外显建构并没有明显改善条件推理的成绩; 在有可能性判断任务条件下, 被试外显心理模型建构的成绩变化模式并不能一致地预测条件推理成绩的变化模式。这些结果说明, 人们的条件推理可能并不是完全基于心理模型建构, 而是还受对条件句前后件之间语义关系理解的影响; 条件句表达形式中语义关系的外显内隐模式影响对条件句语义关系的知觉理解难易, 从而影响相应条件推理的成绩。     

Optimal Predictions in Everyday Cognition: The Wisdom of Individuals or Crowds?

Griffiths and Tenenbaum (2006) asked individuals to make predictions about the duration or extent of everyday events (e.g., cake baking times), and reported that predictions were optimal, employing Bayesian inference based on veridical prior distributions. Although the predictions conformed strikingly to statistics of the world, they reflect averages over many individuals. On the conjecture that the accuracy of the group response is chiefly a consequence of aggregating across individuals, we constructed simple, heuristic approximations to the Bayesian model premised on the hypothesis that individuals have access merely to a sample of k instances drawn from the relevant distribution. The accuracy of the group response reported by Griffiths and Tenenbaum could be accounted for by supposing that individuals each utilize only two instances. Moreover, the variability of the group data is more consistent with this small-sample hypothesis than with the hypothesis that people utilize veridical or nearly veridical representations of the underlying prior distributions. Our analyses lead to a qualitatively different view of how individuals reason from past experience than the view espoused by Griffiths and Tenenbaum.     

Qualitative probabilistic inference under varied entropy levels

In previous work, we studied four well known systems of qualitative probabilistic inference, and presented data from computer simulations in an attempt to illustrate the performance of the systems. These simulations evaluated the four systems in terms of their tendency to license inference to accurate and informative conclusions, given incomplete information about a randomly selected probability distribution. In our earlier work, the procedure used in generating the unknown probability distribution (representing the true stochastic state of the world) tended to yield probability distributions with moderately high entropy levels. In the present article, we present data charting the performance of the four systems when reasoning in environments of various entropy levels. The results illustrate variations in the performance of the respective reasoning systems that derive from the entropy of the environment, and allow for a more inclusive assessment of the reliability and robustness of the four systems.     

Chances and frequencies in probabilistic reasoning: rejoinder to Hoffrage,Gigerenzer, Krauss,and Martignon

Do individuals unfamiliar with probability and statistics need a specific type of data in order to draw correct inferences about uncertain events? Girotto and Gonzalez (Cognition 78 (2001) 247) showed that naive individuals solve frequency as well as probability problems, when they reason extensionally, in particular when probabilities are represented by numbers of chances. Hoffrage, Gigerenzer, Krauss, and Martignon (Cognition 84 (2002) 343) argued that numbers of chances are natural frequencies disguised as probabilities, though lacking the properties of true probabilities. They concluded that we failed to demonstrate that naive individuals can deal with true probabilities as opposed to natural frequencies. In this paper, we demonstrate that numbers of chances do represent probabilities, and that naive individuals do not confuse numbers of chances with frequencies. We conclude that there is no evidence for the claim that natural frequencies have a special cognitive status, and the evolutionary argument that the human mind is unable to deal with probabilities.     

The User Interface in Computer-based Selection and Assessment: Applied and Theoretical Problematics of an Evolving Technology

Although its central role in human-computer interaction is recognized by industry, the user interface (UI) has received scant attention in the computer-based testing (CBT) literature. Industrial and European Community ergonomic directives have been established regarding UI design issues, however, which may significantly impact upon CBT. Accordingly, the UI’s CBT role is discussed regarding central CBT/UI constructs and dynamics. On the basis of one CBT UI design issue (here: paging) it is shown that many ergonomic regulatory definitional traps exist for the unwary CBT practitioner and UI designer. Newer UI developments in windowing technology and computer-based UI ergonomic regulation conformance analysis are described. Finally, open issues regarding CBT UI problematics are discussed, showing that, rather than continuing to lead a shadowed existence, the UI is deserving of considerably increased research effort regarding its CBT impact.     

推理与工作记忆

系统地总结了推理与工作记忆的研究进展,具体包括：（1）工作记忆的含义及其在推理中作用的相关研究;（2）推理与工作记忆关系的理论模型;（3）不同类型推理与工作记忆的实验研究;（4）推理与工作记忆的认知神经研究;（5）推理与工作记忆的未来研究展望     

Learning the form of causal relationships using hierarchical bayesian models

People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.     

关于心理学统一理论的探讨

借鉴物理学中研究物理世界各种相互作用及其统一性的思路,分析心理现象中的各种相互作用。指出心理现象中存在以下几种不同性质的心理相互作用:心理成分之间的相互作用,心脑相互作用,心身相互作用,心理活动与环境相互作用,以及心理活动与社会相互作用。这些不同性质的心理相互作用具有共同的基础,由此得到心理相互作用的大统一理论。在此理论基础上可以构建大统一心理学的理论框架,尝试把心理学各个研究领域和心理学各个分支学科统一在这个理论框架中。