Temporal Interaction of Information and Belief

The temporal updating of an agent’s beliefs in response to a flow of information is modeled in a simple modal logic that, for every date t, contains a normal belief operator B _t and a non-normal information operator I _t which is analogous to the ‘only knowing’ operator discussed in the computer science literature. Soundness and completeness of the logic are proved and the relationship between the proposed logic, the AGM theory of belief revision and the notion of plausibility is discussed. A first draft of this paper was presented at the Workshop on Belief Change in Rational Agents: Perspectives from Artificial Intelligence, Philosophy and Economics, Dagstuhl (Germany), August 2005. Special Issue Formal Epistemology II. Edited by Branden Fitelson     

Multilevel Modeling with Correlated Effects

When there exist omitted effects, measurement error, and/or simultaneity in multilevel models, explanatory variables may be correlated with random components, and standard estimation methods do not provide consistent estimates of model parameters. This paper introduces estimators that are consistent under such conditions. By employing generalized method of moments (GMM) estimation techniques in multilevel modeling, the authors present a series of estimators along a robust to efficient continuum. This continuum depends on the assumptions that the analyst makes regarding the extent of the correlated effects. It is shown that the GMM approach provides an overarching framework that encompasses well-known estimators such as fixed and random effects estimators and also provides more options. These GMM estimators can be expressed as instrumental variable (IV) estimators which enhances their interpretability. Moreover, by exploiting the hierarchical structure of the data, the current technique does not require additional variables unlike traditional IV methods. Further, statistical tests are developed to compare the different estimators. A simulation study examines the finite sample properties of the estimators and tests and confirms the theoretical order of the estimators with respect to their robustness and efficiency. It further shows that not only are regression coefficients biased, but variance components may be severely underestimated in the presence of correlated effects. Empirical standard errors are employed as they are less sensitive to correlated effects when compared to model-based standard errors. An example using student achievement data shows that GMM estimators can be effectively used in a search for the most efficient among unbiased estimators. This research was supported by the National Academy of Education/Spencer Foundation and the National Science Foundation, grant number SES-0436274. We thank the editor, associate editor, and referees for detailed feedback that helped improve the paper.     

Combining versus analyzing multiple causes: how domain assumptions and task context affect integration rules

In everyday life, people typically observe fragments of causal networks. From this knowledge, people infer how novel combinations of causes they may never have observed together might behave. I report on 4 experiments that address the question of how people intuitively integrate multiple causes to predict a continuously varying effect. Most theories of causal induction in psychology and statistics assume a bias toward linearity and additivity. In contrast, these experiments show that people are sensitive to cues biasing various integration rules. Causes that refer to intensive quantities (e.g., taste) or to preferences (e.g., liking) bias people toward averaging the causal influences, whereas extensive quantities (e.g., strength of a drug) lead to a tendency to add. However, the knowledge underlying these processes is fallible and unstable. Therefore, people are easily influenced by additional task-related context factors. These additional factors include the way data are presented, the difficulty of the inference task, and transfer from previous tasks. The results of the experiments provide evidence for causal model and related theories, which postulate that domain-general representations of causal knowledge are influenced by abstract domain knowledge, data-driven task factors, and processing difficulty.     

内隐学习的人工神经网络模型

近年来,人工神经网络模型常被用来模拟各种心理活动,从而为心理学的一些相关理论提供丰富的证据,内隐学习也不例外。基于权重调整来学习正确反应的人工神经网络模型和内隐学习的两大本质特征间有着极为相应的匹配,因此,人工神经网络模型特别适用于内隐学习研究。到目前为止,针对两种较为普遍的内隐学习任务,已经相应地出现了两种使用较为广泛的神经网络模型——自动联系者和简单循环网络     

The role of causal models in multiple judgments under uncertainty

Two studies examined a novel prediction of the causal Bayes net approach to judgments under uncertainty, namely that causal knowledge affects the interpretation of statistical evidence obtained over multiple observations. Participants estimated the conditional probability of an uncertain event (breast cancer) given information about the base rate, hit rate (probability of a positive mammogram given cancer) and false positive rate (probability of a positive mammogram in the absence of cancer). Conditional probability estimates were made after observing one or two positive mammograms. Participants exhibited a causal stability effect: there was a smaller increase in estimates of the probability of cancer over multiple positive mammograms when a causal explanation of false positives was provided. This was the case when the judgments were made by different participants (Experiment 1) or by the same participants (Experiment 2). These results show that identical patterns of observed events can lead to different estimates of event probability depending on beliefs about the generative causes of the observations.     

词汇飞跃的本质:命名洞察力的实证研究

通过个案跟踪和控制实验探讨了词汇飞跃的本质。研究结果表明命名洞察力具有现实性,且词汇飞跃与命名洞察力的出现紧密相关,这在一定程度上解释了儿童词汇量快速增长的原因。命名洞察力的获得促进了词汇的快速增长,标志着儿童建立起词汇与指称之间的匹配关系。词汇飞跃之后,儿童很快建立起词语间的符号关系,并最终促进了儿童语法的发展。基于此实证结果,本研究还建构了诠释词汇飞跃本质的理论模型。     

What kind of discipline is psychoanalysis?

Current controversies involving clinical, conceptual and empirical research shed light on how psychoanalysis confronts its nature and its future. Some relevant debates in which Wallerstein, Green, Hoffman, Eagle and Wolitzky, Safran, Stern, Blass and Carmeli, and Panksepp have participated are examined regarding the characteristics of their argumentation. Agreements and disagreements are explored to find ways that could have allowed the discussion to progress. Two foci are highlighted in these debates: (a) whether a clinical common ground exists in psychoanalysis and what kind of procedure could contribute to further clarification; (b) complementation of in‐clinical and extra‐clinical evidence. Both aspects are scrutinized: the possibility of complementing diverse methodologies, and the nature of the shared clinical evidence examined in clinical discussion groups such as those promoted by the IPA Clinical Observation Committee. The importance of triangulation and consilience is brought to bear regarding their contribution to the robustness of psychoanalysis. So as to strengthen a critical perspective that enhances the discipline's argumentative field, psychoanalysis should take into account arguments from different sources according to their specific merits. By doing this, psychoanalysis increases its relevance within the current interdisciplinary dialogue.     

第三方医学检验机构法律问题研究——从两个典型判例的实证研究切入

第三方医学检验机构主要从美国兴起,我国的第三方医学检验机构随着医疗改革的深入也在迅猛发展。但是比起医疗机构附设的医学检验部门,第三方医学检验机构是独立的法人主体,因此与患者和医疗机构之间形成多元法律关系。通过整理涉医学检验机构诉讼纠纷发现,法律实践中主要存在两类典型的法律问题：一类是法理或法律可以予以解决的问题,但解决的结果无法达到案结事了或者公平公正,这类问题源于实践超出理论的复杂性;另一类是落入法律或法理的“间隙”,在理论上存疑的问题。诉诸法律来解决的这些问题虽然还未能成为第三方医学检验机构发展的重要风险,但是所展现出来的问题都具有相当的典型性和代表性。

     

Why Bayesian Psychologists Should Change the Way They Use the Bayes Factor

The discussion following Bem’s (2011 Bem, D. J. (2011). Feeling the future: Experimental evidence for anomalous retroactive influences on cognition and affect. Journal of Personality and Social Psychology, 100, 407–425. doi: 10.1037/a0021524[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) psi research highlights that applications of the Bayes factor in psychological research are not without problems. The first problem is the omission to translate subjective prior knowledge into subjective prior distributions. In the words of Savage (1961 Savage, L. J. (1961). The foundations of statistical inference reconsidered. In J. Neyman, (Ed.), Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics, pp. 575–586. BerkeleyCA: University of California. [Google Scholar]): “they make the Bayesian omelet without breaking the Bayesian egg.” The second problem occurs if the Bayesian egg is not broken: the omission to choose default prior distributions such that the ensuing inferences are well calibrated. The third problem is the adherence to inadequate rules for the interpretation of the size of the Bayes factor. The current paper will elaborate these problems and show how to avoid them using the basic hypotheses and statistical model used in the first experiment described in Bem (2011 Bem, D. J. (2011). Feeling the future: Experimental evidence for anomalous retroactive influences on cognition and affect. Journal of Personality and Social Psychology, 100, 407–425. doi: 10.1037/a0021524[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). It will be argued that a thorough investigation of these problems in the context of more encompassing hypotheses and statistical models is called for if Bayesian psychologists want to add a well-founded Bayes factor to the tool kit of psychological researchers.     

Calibrated Bayes Factors Should Not Be Used: A Reply to Hoijtink,van Kooten,and Hulsker

Hoijtink, Kooten, and Hulsker (2016 Hoijtink, H., van Kooten, P., &; Hulsker, K. (2016). Why Bayesian psychologists should change the way they use the Bayes factor. Multivariate Behavioral Research, 51, 1--9. doi: 10.1080/00273171.2014.969364.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) present a method for choosing the prior distribution for an analysis with Bayes factor that is based on controlling error rates, which they advocate as an alternative to our more subjective methods (Morey &; Rouder, 2014 Morey, R.D., &; Rouder, J.N. (2014). Bayesfactor: Computation of Bayes factors for common designs. R package version 0.9.9. Retrieved from http://CRAN.R-project.org/package=BayesFactor [Google Scholar]; Rouder, Speckman, Sun, Morey, &; Iverson, 2009 Rouder, J.N., Speckman, P.L., Sun, D., Morey, R.D., &; Iverson, G. (2009). Bayesian t-tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin and Review, 16, 225–237. doi: 10.3758/PBR.16.2.225[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]; Wagenmakers, Wetzels, Borsboom, &; van der Maas, 2011 Wagenmakers, E.-J., Wetzels, R., Borsboom, D., &; van der Maas, H. (2011). Why psychologists must change the way they analyze their data: The case of psi. A comment on Bem (2011). Journal of Personality and Social Psychology, 100, 426–432. doi: 10.1037/a0022790[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). We show that the method they advocate amounts to a simple significance test, and that the resulting Bayes factors are not interpretable. Additionally, their method fails in common circumstances, and has the potential to yield arbitrarily high Type II error rates. After critiquing their method, we outline the position on subjectivity that underlies our advocacy of Bayes factors.