The Scandal of Deduction

This article provides the first comprehensive reconstruction and analysis of Hintikka’s attempt to obtain a measure of the information yield of deductive inferences. The reconstruction is detailed by necessity due to the originality of Hintikka’s contribution. The analysis will turn out to be destructive. It dismisses Hintikka’s distinction between surface information and depth information as being of any utility towards obtaining a measure of the information yield of deductive inferences. Hintikka is right to identify the failure of canonical information theory to give an account of the information yield of deductions as a scandal, however this article demonstrates that his attempt to provide such an account fails. It fails primarily because it applies to only a restricted set of deductions in the polyadic predicate calculus, and fails to apply at all to the deductions in the monadic predicate calculus and the propositional calculus. Some corollaries of these facts are a number of undesirable and counterintuitive results concerning the proposed relation of linguistic meaning (and hence synonymy) with surface information. Some of these results will be seen to contradict Hintikka’s stated aims, whilst others are seen to be false. The consequence is that the problem of obtaining a measure of the information yield of deductive inferences remains an open one. The failure of Hintikka’s proposal will suggest that a purely syntactic approach to the problem be abandoned in favour of an intrinsically semantic one.     

Regularized Multiple-Set Canonical Correlation Analysis

Multiple-set canonical correlation analysis (Generalized CANO or GCANO for short) is an important technique because it subsumes a number of interesting multivariate data analysis techniques as special cases. More recently, it has also been recognized as an important technique for integrating information from multiple sources. In this paper, we present a simple regularization technique for GCANO and demonstrate its usefulness. Regularization is deemed important as a way of supplementing insufficient data by prior knowledge, and/or of incorporating certain desirable properties in the estimates of parameters in the model. Implications of regularized GCANO for multiple correspondence analysis are also discussed. Examples are given to illustrate the use of the proposed technique. The work reported in this paper is supported by Grants 10630 and 290439 from the Natural Sciences and Engineering Research Council of Canada to the first and the second authors, respectively. The authors would like to thank the two editors (old and new), the associate editor, and four anonymous reviewers for their insightful comments on earlier versions of this paper. Matlab programs that carried out the computations reported in the paper are available upon request.     

Valuing information and options: an experimental study

Participants in four studies were faced with everyday‐life decision scenarios involving risk, such as purchasing an airline ticket whose price may change. They were asked to state their maximum willingness to pay (WTP) for resolving the uncertainty with either perfect information or an option. The two are strategically equivalent, therefore, should be valued in the same way. Across all experiments, individuals tend to value the option more than the information. In addition, the distributions of responses reveal frequent and gross violations of normative bounds. Contrary to normative predictions, no relationship is found between individuals' valuation of the gamble and their reported WTP for information or option. Furthermore, although participants pay attention to the probabilities of outcomes in valuing the gambles, they ignore the probabilities in valuing uncertainty resolution. Several reasoning heuristics that participants use to come up with a value of information are identified, and it appears that the Information and Option contexts tend to trigger different heuristics. Shift in reference point and regret are consistent with the Information–Option valuation discrepancy. Copyright © 2007 John Wiley & Sons, Ltd.     

现实驱动和情节驱动对时间信息表征的影响

采用动窗技术,探讨在记叙文理解中现实驱动和情节驱动对时间信息表征的影响.实验1验证了强印象假设中时间转换的效应,说明读者是根据对现实的期望来建构情境模型的,具体来说,读者采用现实驱动的加工方式表征情境模型中的时间信息.实验2探讨现实驱动和情节驱动对时间信息表征的共同影响,结果表明,读者根据故事情节产生的愿望在一定程度上改变了现实驱动所产生的时间转换的效应,证实了情节驱动在表征情境模型中的时间信息时的作用.     

时间限制和成就动机对职业决策线索加工的影响

采用信息板技术探讨了时间限制条件下成就动机对职业决策线索加工的影响.结果表明:(1)时间限制影响了个体职业选择过程的平均决策时间、信息搜索深度和信息搜索模式,但未对决策满意度造成显著影响;(2)追求成功动机影响决策时的信息搜索模式,避免失败动机影响决策时的信息搜索深度.     

比较广告传播的作用机制及影响因素

比较广告是一种新异但颇具争议的广告形式。研究者们对比较广告的概念和传播效果已有较为统一的认识,新近研究的重点是比较广告传播的作用机制及影响其传播效果的因素。文章从比较广告的传播效果、传播机制和影响因素三方面进行了系统的考察,提出现有研究在机制探讨和影响因素控制上的一些不足,并展望了研究亚文化下比较广告的方向     

碰撞时间估计的影响因素研究

本研究采用遮挡范式,考察了专职公交车驾驶员对不同类型、速度和方向运动物体的碰撞时间(TTC)估计.结果发现,被试对不同类别和速度运动物体的TTC估计有显著差异.这表明,除了视觉信息和物理信息之外,运动物体的概念信息也是影响横向运动TTC估计的重要因素.     

任务信息通达对视觉表象眼动的影响

通过两个实验探讨了视觉表象任务信息的通达对表象加工眼动的影响。结果表明, 在低通达条件下, 表象任务的眼动复制了知觉任务的眼动; 随着表象任务信息通达水平的提高, 眼动的注视点平均持续时间、平均眼跳距离和平均眼跳时间会发生规律性变化; 眼动控制与任务信息通达水平对表象眼动的影响存在不同的机制。实验结果佐证了眼动在视觉表象中起机能性作用的观点。     

动态信息呈现对问题发现的影响研究

在多轮次动态和一次性静态两种呈现方式下,采用出声思维研究20名大学生潜藏与矛盾情境的问题发现特点。研究表明:(1)动态呈现促进被试更全面加工信息,发现问题数量和变通性优于静态呈现,但也阻断信息间的关联,不利于发现跨区问题。(2)动态呈现下,问题均匀分布在每个区域并按轮次先后顺序出现,静态呈现与此相反。(3)被试在新信息呈现后发现的问题分全新、改变方向、改变程度、一致性和联系性五类,潜藏情境中问题与先前信息关联更多。(4)被试对新信息影响做出积极评价。     

情绪即社会信息模型述评

情绪即社会信息(EASI)模型提出,个体对他人的情绪信息加工具有两种方式:情感反应与推断加工。认识动机水平及不同的社会情境会影响两种加工的强度,进而影响个体的决策。该模型在谈判、领导者和最后通牒、独裁者博弈研究中获得了直接和间接的实证支持。心理理论与移情的新近神经生理研究成果也有助于深入探索该模型的神经生理基础。该模型具有很强的应用潜力,但形成时间尚短,需要进一步探索并应用到实践领域中去。