The Appeal to Expert Opinion: Quantitative Support for a Bayesian Network Approach

The appeal to expert opinion is an argument form that uses the verdict of an expert to support a position or hypothesis. A previous scheme‐based treatment of the argument form is formalized within a Bayesian network that is able to capture the critical aspects of the argument form, including the central considerations of the expert's expertise and trustworthiness. We propose this as an appropriate normative framework for the argument form, enabling the development and testing of quantitative predictions as to how people evaluate this argument, suggesting that such an approach might be beneficial to argumentation research generally. We subsequently present two experiments as an example of the potential for future research in this vein, demonstrating that participants' quantitative ratings of the convincingness of a proposition that has been supported with an appeal to expert opinion were broadly consistent with the predictions of the Bayesian model.     

Concept Appraisal

This paper reports the first empirical investigation of the hypothesis that epistemic appraisals form part of the structure of concepts. To date, studies of concepts have focused on the way concepts encode properties of objects and the way those features are used in categorization and in other cognitive tasks. Philosophical considerations show the importance of also considering how a thinker assesses the epistemic value of beliefs and other cognitive resources and, in particular, concepts. We demonstrate that there are multiple, reliably judged, dimensions of epistemic appraisal of concepts. Four of these dimensions are accounted for by a common underlying factor capturing how well people believe they understand a concept. Further studies show how dimensions of concept appraisal relate to other aspects of concepts. First, they relate directly to the hierarchical organization of concepts, reflecting the increase in specificity from superordinate to basic and subordinate levels. Second, they predict inductive choices in category-based induction. Our results suggest that epistemic appraisals of concepts form a psychologically important yet previously overlooked aspect of the structure of concepts. These findings will be important in understanding why individuals sometimes abandon and replace certain concepts; why social groups do so, for example, during a “scientific revolution”; and how we can facilitate such changes when we engage in deliberate “conceptual engineering” for epistemic, social, and political purposes.     

非参数认知诊断方法下诊断结果的概率化表征

非参数认知诊断分类方法非常适合课堂评估,其诊断结果采用0-1形式而缺乏概率化表征,不能精细地区分被试属性掌握程度的差异或变化,还缺乏可用于评价真实测验分类结果的信度和效度指标。要刻画被试属性掌握程度的差异,首要的问题是要为非参数认知诊断方法提供一种可以量化属性掌握概率的方法。针对此问题,基于二项分布和玻尔兹曼分布提出非参数认知诊断方法下诊断结果的概率化表征方法,并用于构建分类准确性和分类一致性指标。模拟研究与实测数据分析结果显示：概率化表征方法与非参数认知诊断方法的分类结果高度一致;概率化表征方法与认知诊断模型所得的属性掌握概率十分接近;概率化表征方法所得的属性（模式）掌握概率可用于计算属性（模式）分类准确性和分类一致性指标,在实际测验情景下可作为信度和效度指标,评价诊断结果的重测一致率和判准率。     

Dispositional optimism and luck attributions: Implications for philosophical theories of luck

We conducted two studies to determine whether there is a relationship between dispositional optimism and the attribution of good or bad luck to ambiguous luck scenarios. Study 1 presented five scenarios that contained both a lucky and an unlucky component, thereby making them ambiguous in regard to being an overall case of good or bad luck. Participants rated each scenario in toto on a four-point Likert scale and then completed an optimism questionnaire. The results showed a significant correlation between optimism and assignments of luck: more optimistic people rated the characters in the ambiguous scenarios as more lucky while more pessimistic people rated the same characters in the same scenarios as more unlucky. Study 2 separated the good and bad luck components of the study 1 scenarios and presented the components individually to a new group of participants. Participants rated the luckiness of each component on the same four-point scale and then completed the optimism questionnaire. We found that the luckiness of the bad luck component could be significantly predicted by their level of optimism. We discuss how these findings pose problems for philosophical accounts that treat luck as an objective property.     

认知灵活性对概率类别学习的影响

本研究采用“数字-字母转换任务”区分高低认知灵活性者, 构建概率配对模式相同但形式不同的两个概率类别学习任务, 借助ERP技术探讨认知灵活性对概率类别学习任务的作用特点与机制。结果发现, 本研究的两个任务中, 高认知灵活性组的规则习得水平均优于低认知灵活性组, 认知灵活性能促进概率类别的学习。同时, 对不同学习阶段的ERPs分析结果显示, 高认知灵活性者在概率类别学习中的优势源于反馈加工过程。     

How Affect Shapes Risky Choice: Distorted Probability Weighting Versus Probability Neglect

People's choices between prospects with relatively affect‐rich outcomes (e.g., medical side effects) can diverge markedly from their choices between prospects with relatively affect‐poor outcomes (e.g., monetary losses). We investigate the cognitive mechanisms underlying this “affect gap” in risky choice. One possibility is that affect‐rich prospects give rise to more distortion in probability weighting. Another is that they lead to the neglect of probabilities. To pit these two possibilities against each other, we fitted cumulative prospect theory (CPT) to the choices of individual participants, separately for choices between options with affect‐rich outcomes (adverse medical side effects) and options with affect‐poor outcomes (monetary losses); additionally, we tested a simple model of probability neglect, the minimax rule. The results indicated a qualitative difference in cognitive mechanisms between the affect‐rich and affect‐poor problems. Specifically, in affect‐poor problems, the large majority of participants were best described by CPT; in affect‐rich problems, the proportion of participants best described by the minimax rule was substantially higher. The affect gap persisted even when affect‐rich outcomes were supplemented by numerical information, thus providing no support for the thesis that choices in affect‐rich and affect‐poor problems diverge because the information provided in the former is nonnumerical. Our findings suggest that the traditional expectation‐based framework for modeling risky decision making may not readily generalize to affect‐rich choices. Copyright © 2015 John Wiley & Sons, Ltd.     

Context affects the interpretation of low but not high numerical probabilities: A hypothesis testing account of subjective probability

Low numerical probabilities tend to be directionally ambiguous, meaning they can be interpreted either positively, suggesting the occurrence of the target event, or negatively, suggesting its non-occurrence. High numerical probabilities, however, are typically interpreted positively. We argue that the greater directional ambiguity of low numerical probabilities may make them more susceptible than high probabilities to contextual influences. Results from five experiments supported this premise, with perceived base rate affecting the interpretation of an event’s numerical posterior probability more when it was low than high. The effect is consistent with a confirmatory hypothesis testing process, with the relevant perceived base rate suggesting the directional hypothesis which people then test in a confirmatory manner.     

Discrete Process Model for Quantum Systems of Matter and Mind

Attempts to create a coherent scientific picture of the world as a whole on the basis of quantum physics has sped up at the turn of the millennium. There particularly seem to be expectations that the development of a new kind of quantum mechanics could make it possible to describe both matter and consciousness in one frame of reference (“dual aspect approach”). These ideas are often results of brilliant intuitive visions but as yet not able to produce testable hypotheses. Maybe “wave mechanics” is not very suitable in the study of consciousness from the quantum mechanical point of view. The aim of this article is to show how both the matter and the mind systems can be described with one coherent mathematical model if we assume both space and time to be discrete.