贝叶斯推理研究的三个层次

从纵向发展来看, 近年来贝叶斯推理研究从重推理问题轻推理者因素(关注频率信息和图表呈现的促进效应, 推理问题内容对推理的影响, 生态效度等), 逐渐转向重视推理者因素(知识背景, 主体关联性程度等)和推理过程, 最后发展到将推理问题、推理者和推理过程整合的趋势; 从横向发展来看, 各个纵向发展的阶段内部也有横向发展阶段, 整个研究结构呈立体状。未来的研究应进一步结合推理材料、推理过程考察推理者因素如焦虑、认知风格、元认知和工作记忆等对贝叶斯推理的影响, 并利用新的研究手段如ERP、眼动仪等探索和验证贝叶斯推理的影响因素及其认知加工过程。     

Multiple layers of information processing in deductive reasoning: combining dual strategy and dual-source approaches to reasoning

The idea that inferential performance cannot be analyzed within a single model has been suggested within two theoretical contexts. The dual strategy model suggests that people reason using different approaches to processing statistical information. The dual-source model suggests that people reason probabilistically using both statistical information and some intuition about logical form. Each model suggests that people have different approaches to processing information while making inferences. The following studies examined whether these different forms of information processing were equally present during reasoning. Participants were given a series of problems designed to distinguish counterexample from statistical reasoners. They were then given a series of MP or AC inferences for which identical statistical information was provided. Results show that MP inferences were considered to be deductively valid more often than equivalent AC inferences. The effect of logical form was independent of reasoning strategy, and of relatively equivalent size for both counterexample and statistical reasoners. The second study examined explicitly probabilistic inferences, and showed smaller effects of logical form and of reasoning strategy, although with a complex set of interactions. These results show that understanding the way that people use information when making inferences requires a multidimensional approach.     

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.     

因果信息和提示指导语对大学生贝叶斯推理的影响

基于三重加工心智模型,以大学生为被试,采用经典贝叶斯推理的文本范式,通过操纵自变量：因果信息（有或无）与提示指导语（提供或不提供）,试图探讨激发反省心智,消解理性障碍的情况下,因果贝叶斯框架的作用机制。估计正确率和准确性的结果都表明因果信息显著提高了贝叶斯推理成绩,准确性的结果也说明指导语可以提示被试放下既有观念,以无偏见的方式进行推理,从而有效促进了贝叶斯推理表现。而在提示条件下增加因果信息并没有促进作用,表明对于较高元认知的被试因果信息作用是有限的。     

因果信息和提示指导语对大学生贝叶斯推理的影响

基于三重加工心智模型，以大学生为被试，采用经典贝叶斯推理的文本范式，通过操纵自变量：因果信息（有或无）与提示指导语（提供或不提供），试图探讨激发反省心智，消解理性障碍的情况下，因果贝叶斯框架的作用机制。估计正确率和准确性的结果都表明因果信息显著提高了贝叶斯推理成绩，准确性的结果也说明指导语可以提示被试放下既有观念，以无偏见的方式进行推理，从而有效促进了贝叶斯推理表现。而在提示条件下增加因果信息并没有促进作用，表明对于较高元认知的被试因果信息作用是有限的。     

基于非逻辑机制的条件推理模式：P—Q映射模型及其实证检验

研究结合数学分析方法,提出了基于非逻辑机制的条件推理模型：P-Q映射模型。并根据这个模型,对人们在不同命题类型奈件下的推理行为进行了预测。预测结果显示,当推理前提为LH和HL型命题时,基于P-Q映射模型的预测结果与基于条件概率模型的预测结果完全一致。但当推理前提为LL和HH型命题时,两种模型给出的预测结果存在差异。实验结果表明,当前提命题为LL和HH型命题时,被试的条件推理行为与P-Q映射模型的预言完全一致。     

四卡问题的视角效应发生机制：领域一般性还是领域特殊性？

视角效应（Perspective Effects)是指四卡问题解决中,当被试从不同的角度对条件规则进行检验时,其选择偏向会在卡片p,q和-p,q之间发生稳定变化的现象。视角效应对四卡问题的解决有重要影响,但其存在条件及发生机制仍存在争议。本研究通过3个实验,探讨四卡问题中的视角效应。实验1和实验2采用混合实验设计,结果表明,转换社会契约、因果规则的四卡问题中,发现视角效应,抽象规则中,提供反例比提供充分必要条件更有利于产生视角效应。实验3在不同视角下研究四卡问题与条件推理的关系。结果表明,四卡问题与条件推理之间不具有逻辑一致性,产生分离现象。总之,视角效应在抽象规则和特定规则中均存在,而提供反例更有利于视角效应的产生。领域一般性或领域特殊性均不能有效解释视角效应的产生,两种机制的综合调节为解释视角效应提供新的角度。     

Diagnostic causal reasoning with verbal information

In diagnostic causal reasoning, the goal is to infer the probability of causes from one or multiple observed effects. Typically, studies investigating such tasks provide subjects with precise quantitative information regarding the strength of the relations between causes and effects or sample data from which the relevant quantities can be learned. By contrast, we sought to examine people’s inferences when causal information is communicated through qualitative, rather vague verbal expressions (e.g., “X occasionally causes A”). We conducted three experiments using a sequential diagnostic inference task, where multiple pieces of evidence were obtained one after the other. Quantitative predictions of different probabilistic models were derived using the numerical equivalents of the verbal terms, taken from an unrelated study with different subjects. We present a novel Bayesian model that allows for incorporating the temporal weighting of information in sequential diagnostic reasoning, which can be used to model both primacy and recency effects. On the basis of 19,848 judgments from 292 subjects, we found a remarkably close correspondence between the diagnostic inferences made by subjects who received only verbal information and those of a matched control group to whom information was presented numerically. Whether information was conveyed through verbal terms or numerical estimates, diagnostic judgments closely resembled the posterior probabilities entailed by the causes’ prior probabilities and the effects’ likelihoods. We observed interindividual differences regarding the temporal weighting of evidence in sequential diagnostic reasoning. Our work provides pathways for investigating judgment and decision making with verbal information within a computational modeling framework.     

Logic programming,probability, and two-system accounts of reasoning: a rejoinder to Oaksford and Chater (2014)

This reply to Oaksford and Chater’s (O&C)’s critical discussion of our use of logic programming (LP) to model and predict patterns of conditional reasoning will frame the dispute in terms of the semantics of the conditional. We begin by outlining some common features of LP and probabilistic conditionals in knowledge-rich reasoning over long-term memory knowledge bases. For both, context determines causal strength; there are inferences from the absence of certain evidence; and both have analogues of the Ramsey test. Some current work shows how a combination of counting defeaters and statistics from network monitoring can provide the information for graded responses from LP reasoning. With this much introduction, we then respond to O&C’s specific criticisms and misunderstandings.     

知识图式对贝叶斯推理的影响

选取具有概率知识的大学生(专家)和没有概率知识的大学生(新手)为被试,进行贝叶斯推理中的概率估计,探讨知识图式对贝叶斯推理的影响。结果表明,具有概率知识背景的大学生比没有概率知识背景的大学生概率估计的准确性更高,反应时更长,说明知识图式影响概率信息的搜索和判断。     

Reasoning with conditionals

This paper reviews the psychological investigation of reasoning with conditionals, putting an emphasis on recent work. In the first part, a few methodological remarks are presented. In the second part, the main theories of deductive reasoning (mental rules, mental models, and the probabilistic approach) are considered in turn; their content is summarised and the semantics they assume for if and the way they explain formal conditional reasoning are discussed, in particular in the light of experimental work on the probability of conditionals. The last part presents the recent shift of interest towards the study of conditional reasoning in context, that is, with large knowledge bases and uncertain premises.     

The effect of emotion on statistical reasoning: Findings from a base rates task

In this paper we explore the effect of emotion on statistical reasoning. We employed a base rates task to examine reliance on statistical information in the presence of congruent or incongruent anecdotal information. The impact of emotional content on statistical reasoning was investigated in controls and victims of sexual abuse. Two types of emotional contents were used, one generally emotional, and one related to sexual abuse. The latter category was included to investigate the effect of semantically identical stimuli that could potentially produce different levels of emotion in the victim and control groups. For control participants, emotional contents were associated with decreased use of statistical information and increased use of anecdotal information compared to neutral contents. Results from the victims group showed this effect to be increased for contents related to sexual abuse, suggesting that the effect is linked to the emotional connotation rather than semantic content. Victims perceived contents relating to their experience as more emotional and this was associated with specific reasoning impairments for these topics.     

Traditional difference-score analyses of reasoning are flawed

Studies of the belief bias effect in syllogistic reasoning have relied on three traditional difference score measures: the logic index, belief index, and interaction index. Dube, Rotello, and Heit (2010, 2011) argued that the interaction index incorrectly assumes a linear receiver operating characteristic (ROC). Here, all three measures are addressed. Simulations indicated that traditional analyses of reasoning experiments are likely to lead to incorrect conclusions. Two new experiments examined the role of instructional manipulations on the belief bias effect. The form of the ROCs violated assumptions of traditional measures. In comparison, signal detection theory (SDT) model-based analyses were a better match for the form of the ROCs, and implied that belief bias and instructional manipulations are predominantly response bias effects. Finally, reanalyses of previous studies of conditional reasoning also showed non-linear ROCs, violating assumptions of traditional analyses. Overall, reasoning research using traditional measures is at risk of drawing incorrect conclusions.     

Deductive and inductive conditional inferences: Two modes of reasoning

A number of single- and dual-process theories provide competing explanations as to how reasoners evaluate conditional arguments. Some of these theories are typically linked to different instructions—namely deductive and inductive instructions. To assess whether responses under both instructions can be explained by a single process, or if they reflect two modes of conditional reasoning, we re-analysed four experiments that used both deductive and inductive instructions for conditional inference tasks. Our re-analysis provided evidence consistent with a single process. In two new experiments we established a double dissociation of deductive and inductive instructions when validity and plausibility of conditional problems were pitted against each other. This indicates that at least two processes contribute to conditional reasoning. We conclude that single-process theories of conditional reasoning cannot explain the observed results. Theories that postulate at least two processes are needed to account for our findings.     

Beyond belief bias: Reasoning from conceptual structures by mental models manipulation

Mental models constitute an alternative to the rule-based systems in the explanation of human reasoning (Johnson-Laird, 1983). In this paper, we claim that the concept of believability generally used to categorize content and context effects is of little use within a semantic theory. Thus, we propose the use of categories that are directly extracted from subjective relations among concepts within the reasoning problem. We demonstrate that manipulations based on this kind of categorization produce predictable patterns of responses in reasoning problems. We present two experiments to test our predictions, using conditional and syllogistic reasoning problems, and in both cases, we demonstrate the influence of conceptual knowledge not only in natural contexts, but also in experimentally created artificial contexts.     

Abstract reasoning and the interpretation of basic conditionals

Studies examining the interpretation that is given to if–then statementstypically use what are referred to as basic conditionals, which give contextless relations between two unrelated concrete terms (If the ball is blue, then the shape is square). However, there is some evidence that basic conditionals require a more abstract form of representation. In order to examine this, we presented participants with truth-table tasks involving either basic conditionals or conditionals referring to imaginary categories (If it is a bori, then it has red wings), and standard conditional inference tasks with abstract and familiar premises. As expected, fewer typical defective conditional interpretations were given to basic conditionals. In addition, partial correlations showed a unique relationship between the interpretation of basic conditionals and abstract inferential reasoning. Results suggest that people process basic conditionals as a form of abstract reasoning, and that the interpretation of conditionals must consider the semantic context.     

条件推理:“演绎”与“概率”两种实验研究范式之比较

本研究通过推理心理学研究中的“演绎”和“概率”两种实验范式设计对同一个班级的大学生参与者(实验一中N=57,实验二中N=43)进行先后两次有关条件推理的实验研究后,得出如下主要结果:(1)推理者在对不同的“纯形式条件命题本身的认可度”以及对由它们各自建构的同类型推理题的推理结果之间的作答反应模式之间的差异都很小且具有较高的一致性;(2)对由不同的“含具体内容的假言命题”本身的认可度之间以及由它们建构的同类型条件推理题的推理结果之间具有较大的差异性;(3)推理者对“演绎”和“概率”两种不同实验范式分别建构的内容近似的推进题进行推理时具有大致相同的作答反应趋势。由此可以推论推理者在“概率推理实验范式”中的作答或推理结果可以被视为只是对“演绎推理实验范式”的相应推理题给出“概率解”的心理加工过程。     

条件推理的条件概率模型述评

该文从理论模型和实验范式两个方面对Oaksford等人提出的条件推理概率模型作了综合评述,并充分肯定该模型将演绎推理与归纳推理结合起来进行研究对推理心理学的发展所作出的重要贡献。     

不同概率表征的贝叶斯推理试题形式结构的同质性研究

研究从先验概率、概率表征、推理任务等方面探讨了经典贝叶斯推理研究中存在的不足,试图在＂知识和试题双重模型＂框架下,探索现实和标准贝叶斯试题的形式结构的同质性,结果表明：1）自然频次表征比百分比表征的贝叶斯推算题正确率高,这是因为试题的形式结构不同,与概率表征无关;2）贝叶斯判断题与贝叶斯推算题的试题形式结构存在显著差异;3）贝叶斯推算题中,概率词表征与其它两种表征的试题形式结构存在显著差异,其实质是贝叶斯判断。