Probabilistic reasoning in prediction and diagnosis: Effects of problem type,response mode,and individual differences

In prediction, subset relations require that the probability of conjoined events is never higher than that of constituent events. However, people's judgments regularly violate this principle, producing conjunction errors. In diagnosis, the probability of a hypothesis normatively is often higher for conjoined cues. An online survey used a within‐subjects design to explore the degree to which participants (n = 347) differentiated diagnosis and prediction using matched scenarios and both choice and estimation responses. Conjunctions were judged more probable than a constituent in diagnosis (76%) more often than prediction (64%) and in choice (84%) more often than direct estimation (57%), with no interaction of type of task and response mode. Correlation, regression, and path analyses were used to determine the relationships among individual difference variables and the diagnosis and prediction tasks. Among the correlation findings was that time spent on the task predicted higher conjunction probabilities in diagnosis but not prediction and that class inclusion errors predicted increased conjunction errors in choice but not estimation. Need for cognition and numeracy were only minimally related to reasoning about conjunctions. Results are consistent with the idea that people may misapply diagnostic reasoning to the prediction task and consequently commit the conjunction error. Copyright © 2010 John Wiley & Sons, Ltd.     

小麦中烟曲霉生长温度与水分活度预测模型的建立

为快速预测和监控引起小麦霉变的烟曲霉的生长,在15～40℃温度及0.70～0.96水分活度下进行小麦储藏模拟实验,建立和验证烟曲霉D6生长的一级和二级预测模型。结果表明,一级生长模型中Bo-ltsmann模型优于Logistic模型。以Boltsmann模型拟合数据建立二级模型,采用平方根模型拟合,得烟曲霉D6的最低生长温度和水分活度分别为5.784℃和0.721。经验证,平方根模型能较好描述烟曲霉D6最大比生长速率与温度、水分活度之间的对应关系。     

Quelles méthodes et quelle modélisation d’analyse de l’activité de travail de l’encadrement ? L’exemple de l’encadrement des chantiers de la construction

By referring to the ergonomic analysis of work and to the management sciences, the authors propose an analysis model of the managers’ activity. Until then, few researchs were realized in ergonomics science with this population which has to prepare and to organize the works of other operators. According to the authors, it is possible to apply to this population the model of “the centre de décision”. It allows to understand how their activity is determined by “upstream” centres and then how it determines the activity of centres “approval”. Applied to the project and site managers, this model brings solutions to transform their work and the work of the workmen.     

贝叶斯推理中的“自击中率参照抑制”

本文旨在研究贝叶斯推理中基础比率、击中率起作用的机制。通过设置肝炎情境、中彩情境的贝叶斯推理问题,同时操作基础比率和击中率的水平,考察449名大学生被试的概率估计情况。结果表明,总体上,贝叶斯推理中的基础比率和击中率均起作用,表现为后验概率估计值随基础比率水平的提高而增加,随击中率水平的提高而增加。然而,在低基础比率的中彩情境中,后验概率估计值先随击中率的增加而增加;之后却随击中率水平的继续提高而显著降低,出现自击中率参照抑制。这似乎说明,击中率在贝叶斯推理中并非一致地起作用,而可能与推理者的知识经验对击中率加工的调节有关。     

Three ideal observer models for rule learning in simple languages

Children learning the inflections of their native language show the ability to generalize beyond the perceptual particulars of the examples they are exposed to. The phenomenon of “rule learning”—quick learning of abstract regularities from exposure to a limited set of stimuli—has become an important model system for understanding generalization in infancy. Experiments with adults and children have revealed differences in performance across domains and types of rules. To understand the representational and inferential assumptions necessary to capture this broad set of results, we introduce three ideal observer models for rule learning. Each model builds on the next, allowing us to test the consequences of individual assumptions. Model 1 learns a single rule, Model 2 learns a single rule from noisy input, and Model 3 learns multiple rules from noisy input. These models capture a wide range of experimental results—including several that have been used to argue for domain-specificity or limits on the kinds of generalizations learners can make—suggesting that these ideal observers may be a useful baseline for future work on rule learning.     

A probabilistic model of cross-categorization

Most natural domains can be represented in multiple ways: we can categorize foods in terms of their nutritional content or social role, animals in terms of their taxonomic groupings or their ecological niches, and musical instruments in terms of their taxonomic categories or social uses. Previous approaches to modeling human categorization have largely ignored the problem of cross-categorization, focusing on learning just a single system of categories that explains all of the features. Cross-categorization presents a difficult problem: how can we infer categories without first knowing which features the categories are meant to explain? We present a novel model that suggests that human cross-categorization is a result of joint inference about multiple systems of categories and the features that they explain. We also formalize two commonly proposed alternative explanations for cross-categorization behavior: a features-first and an objects-first approach. The features-first approach suggests that cross-categorization is a consequence of attentional processes, where features are selected by an attentional mechanism first and categories are derived second. The objects-first approach suggests that cross-categorization is a consequence of repeated, sequential attempts to explain features, where categories are derived first, then features that are poorly explained are recategorized. We present two sets of simulations and experiments testing the models’ predictions about human categorization. We find that an approach based on joint inference provides the best fit to human categorization behavior, and we suggest that a full account of human category learning will need to incorporate something akin to these capabilities.     

The mental representation of causal conditional reasoning: mental models or causal models

In this paper, two experiments are reported investigating the nature of the cognitive representations underlying causal conditional reasoning performance. The predictions of causal and logical interpretations of the conditional diverge sharply when inferences involving pairs of conditionals—such as if P₁then Q and if P₂then Q—are considered. From a causal perspective, the causal direction of these conditionals is critical: are the P_icauses of Q; or symptoms caused byQ. The rich variety of inference patterns can naturally be modelled by Bayesian networks. A pair of causal conditionals where Q is an effect corresponds to a “collider” structure where the two causes (P_i) converge on a common effect. In contrast, a pair of causal conditionals where Q is a cause corresponds to a network where two effects (P_i) diverge from a common cause. Very different predictions are made by fully explicit or initial mental models interpretations. These predictions were tested in two experiments, each of which yielded data most consistent with causal model theory, rather than with mental models.     

基于不对称区间估计的有调节的中介模型检验

有调节的中介模型是中介过程受到调节变量影响的模型。评介了基于Bootstrap不对称置信区间和贝叶斯不对称可靠区间进行有调节的中介模型检验的3种方法, 包括亚组分析法、差异分析法和系数乘积法。模拟研究发现, 偏差校正的百分位Bootstrap置信区间和无先验信息的贝叶斯可靠区间在有调节的中介模型检验中表现相当, 都优于百分位Bootstrap置信区间的表现。建议使用系数乘积法进行第一阶段或第二阶段被调节的中介模型检验, 使用差异分析法进行两阶段被调节的中介模型检验, 并用一个实际例子演示如何用不对称区间估计检验有调节的中介模型。随后评述了3种有调节的中介模型检验方法在国内心理学的应用现状, 并展望了检验的拓展方向。     

What Can Artificial Intelligence Learn from Wittgenstein＇s On Certainty？.

Meta-philosophically speaking, the philosophy of artificial intelligence （AI） is intended not only to explore the theoretical possibility of building ＂thinking machines,＂ but also to reveal philosophical implications of specific AI approaches. Wittgenstein＇s comments on the analytic/empirical dichotomy may offer inspirations for AI in the second sense. According to his ＂river metaphor＂ in On Certainty, the analytic/empirical boundary should be delimited in a way sensitive to specific contexts of practical reasoning. His proposal seems to suggest that any cognitive modeling project needs to render the system context-sensitive by avoiding representing large amounts of truisms in its cognitive processes, otherwise neither representational compactness nor computational efficiency can be achieved. In this article, different AI approaches （like the Common Sense Law of Inertia approach, the Bayesian approach and the connectionist approach） will be critically evaluated under the afore-mentioned Wittgensteinian criteria, followed by the author＇s own constructive suggestion on what AI needs to try to do in the near future.     

贝叶斯因子及其在JASP中的实现

统计推断在科学研究中起到关键作用, 然而当前科研中最常用的经典统计方法——零假设检验(Null hypothesis significance test, NHST)却因难以理解而被部分研究者误用或滥用。有研究者提出使用贝叶斯因子(Bayes factor)作为一种替代和(或)补充的统计方法。贝叶斯因子是贝叶斯统计中用来进行模型比较和假设检验的重要方法, 其可以解读为对零假设H₀或者备择假设H₁的支持程度。其与NHST相比有如下优势：同时考虑H₀和H₁并可以用来支持H₀、不“严重”地倾向于反对H₀、可以监控证据强度的变化以及不受抽样计划的影响。目前, 贝叶斯因子能够很便捷地通过开放的统计软件JASP实现, 本文以贝叶斯t检验进行示范。贝叶斯因子的使用对心理学研究者来说具有重要的意义, 但使用时需要注意先验分布选择的合理性以及保持数据分析过程的透明与公开。