中学生完成系列回忆任务错误类型发展的实验研究

本研究以初中一、三年级和高中二年级学生为被试,以三种类型的汉字(普通字、形似字和同音字)为实验材料,采用系列回忆任务的个别实验法,考察中学生完成系列回忆任务的错误类型特点。结果发现:1.各年级都出现了遗漏错误、侵入错误、移动错误和重复错误,其中移动错误随年级升高而减少且错误率最高。2.四种错误类型表现出不同的位置效应。3.同音字引起的错误率明显高于普通字和形似字。     

The association between implicit theories of personality and the attributional process

The influence of implicit theories of personality (entity vs. incremental theorists; see Dweck, Chiu and Hong, 1995) on the stages of the Sequential Operations Model of attribution (Gilbert, Pelham, and Krull, 1988) was investigated. Two hundred eighty Norwegian participants were given a Norwegian translation of the implicit personality theories measure. Participants then read two essays, one advocating the pro-life position and the other advocating the pro-choice position on the abortion issue. The essay positions were ostensibly assigned rather than freely chosen by the author. After each essay, participants were asked to rate the essay position and the true attitude of the author. Entity and incremental theorists showed no differences in their ratings of the essay position; however, entity theorists made significantly stronger correspondent inferences about the author's attitude than did incremental theorists. These results support the contention that entity theorists engage in less attributional correction than incremental theorists.     

Is predictive processing a theory of perceptual consciousness?

Predictive Processing theory, hotly debated in neuroscience, psychology and philosophy, promises to explain a number of perceptual and cognitive phenomena in a simple and elegant manner. In some of its versions, the theory is ambitiously advertised as a new theory of conscious perception. The task of this paper is to assess whether this claim is realistic. We will be arguing that the Predictive Processing theory cannot explain the transition from unconscious to conscious perception in its proprietary terms. The explanations offered by PP theorists mostly concern the preconditions of conscious perception, leaving the genuine material substrate of consciousness untouched.     

A close-up comparison of the misclassification error distance and the adjusted Rand index for external clustering evaluation

The misclassification error distance and the adjusted Rand index are two of the most common criteria used to evaluate the performance of clustering algorithms. This paper provides an in-depth comparison of the two criteria, with the aim of better understand exactly what they measure, their properties and their differences. Starting from their population origins, the investigation includes many data analysis examples and the study of particular cases in great detail. An exhaustive simulation study provides insight into the criteria distributions and reveals some previous misconceptions.     

好奇心的机制及作用

作为一种常见的心理状态和人格特质, 好奇心是认知心理学研究领域中的重要主题。大脑多个脑区的分工和协同作用, 使个体形成了产生与评估预测误差、触发与缓解好奇心以及产生惊奇与新预测误差的认知过程。这些认知过程能够减少对事物和环境的预测误差与信息差距, 消除对事物的不确定性。好奇心在个体终生发展过程中, 对促进认知功能, 保持心理和身体健康有积极的作用。未来研究可以从跨物种、跨学科和多领域交叉的角度切入, 推动好奇心主题研究的深入、研究手段的发展以及研究成果的应用。     

On The Validity of a Simple Argument for Moral Error Theory

In The Myth of Morality Richard Joyce presents a simple and very influential argument for the truth of moral error theory. In this paper I point out that (i) the argument does not have the form Joyce attributes to it, (ii) the argument is not valid in an extensional propositional logic and (iii) on the most natural way of explicating the meanings of the involved terms, it remains invalid. I conclude that more explanation is needed if we are to accept this particular argument for moral error theory.     

The Problem with Having Two Watches: Assessment of Fit When RMSEA and CFI Disagree

The root mean square error of approximation (RMSEA) and the comparative fit index (CFI) are two widely applied indices to assess fit of structural equation models. Because these two indices are viewed positively by researchers, one might presume that their values would yield comparable qualitative assessments of model fit for any data set. When RMSEA and CFI offer different evaluations of model fit, we argue that researchers are likely to be confused and potentially make incorrect research conclusions. We derive the necessary as well as the sufficient conditions for inconsistent interpretations of these indices. We also study inconsistency in results for RMSEA and CFI at the sample level. Rather than indicating that the model is misspecified in a particular manner or that there are any flaws in the data, the two indices can disagree because (a) they evaluate, by design, the magnitude of the model's fit function value from different perspectives; (b) the cutoff values for these indices are arbitrary; and (c) the meaning of “good” fit and its relationship with fit indices are not well understood. In the context of inconsistent judgments of fit using RMSEA and CFI, we discuss the implications of using cutoff values to evaluate model fit in practice and to design SEM studies.     

Using an abbreviated assessment to identify effective error‐correction procedures for individual learners during discrete‐trial instruction

Previous research comparing the effectiveness of error‐correction procedures has involved lengthy assessments that may not be practical in applied settings. We used an abbreviated assessment to compare the effectiveness of five error‐correction procedures for four children with autism spectrum disorder or a developmental delay. During the abbreviated assessment, we sampled participants' responding with each procedure and completed the assessment before participants reached our mastery criterion. Then, we used the results of the abbreviated assessment to predict the most efficient procedure for each participant. Next, we conducted validation assessments, comparing the number of sessions, trials, and time required for participants to master targets with each procedure. Results showed correspondence between the abbreviated assessment and validation assessments for two of four participants and partial correspondence for the other two participants. Findings suggest that a brief assessment may be a useful tool for identifying the most efficient error‐correction procedure for individual learners.     

Character,Common-Sense,and Expertise

Gilbert Harman has argued that the common-sense characterological psychology employed in virtue ethics is rooted not in unbiased observation of close acquaintances, but rather in the ‘fundamental attribution error’. If this is right, then philosophers cannot rely on their intuitions for insight into characterological psychology, and it might even be that there is no such thing as character. This supports the idea, urged by John Doris and Stephen Stich, that we should rely exclusively on experimental psychology for our explanations of behaviour. The purported ‘fundamental attribution error’ cannot play the explanatory role required of it, however, and anyway there is no experimental evidence that we make such an error. It is true that trait-attribution often goes wrong, but this is best explained by a set of difficulties that beset the explanation of other people’s behaviour, difficulties that become less acute the better we know the agent. This explanation allows that we can gain genuine insight into character on the basis of our intuitions, though claims about the actual distribution of particular traits and the correlations between them must be based on more objective data.

Evaluating correlations in studies of personality and behavior: Beyond the number of significant findings to be expected by chance

When large numbers of statistical tests are computed, such as in broad investigations of personality and behavior, the number of significant findings required before the total can be confidently considered beyond chance is typically unknown. Employing modern software, specially written code, and new procedures, the present article uses three sets of personality data to demonstrate how approximate randomization tests can evaluate (a) the number of significant correlations between a single variable and a large number of other variables, (b) the number of significant correlations between two large sets of variables, and (c) the average size of a large number of effects. Randomization tests can free researchers to fully explore large data sets and potentially have even wider applicability.