Route learning in Korsakoff's syndrome: Residual acquisition of spatial memory despite profound amnesia

Korsakoff's syndrome (KS) is characterized by explicit amnesia, but relatively spared implicit memory. The aim of this study was to assess to what extent KS patients can acquire spatial information while performing a spatial navigation task. Furthermore, we examined whether residual spatial acquisition in KS was based on automatic or effortful coding processes. Therefore, 20 KS patients and 20 matched healthy controls performed six tasks on spatial navigation after they navigated through a residential area. Ten participants per group were instructed to pay close attention (intentional condition), while 10 received mock instructions (incidental condition). KS patients showed hampered performance on a majority of tasks, yet their performance was superior to chance level on a route time and distance estimation tasks, a map drawing task and a route walking task. Performance was relatively spared on the route distance estimation task, but there were large variations between participants. Acquisition in KS was automatic rather than effortful, since no significant differences were obtained between the intentional and incidental condition on any task, whereas for the healthy controls, the intention to learn was beneficial for the map drawing task and the route walking task. The results of this study suggest that KS patients are still able to acquire spatial information during navigation on multiple domains despite the presence of the explicit amnesia. Residual acquisition is most likely based on automatic coding processes.     

内隐和外显记忆任务之间的实验性分离:编码程度和提取要求的作用

通过操纵编码程度(实验1)和提取要求(实验2)比较了内隐和外显记忆任务之间的操作变化.结果表明,编码时的语义和非语义加工之间存在着相互抑制的倾向;提取要求中更多意识成份的参与使先前情节中的语义成份得到更多的提取,而很少影响非语义成份的提取,编码程度和提取要求之间存在着相互作用.     

A multitrait-multimethod model with minimal assumptions

A new model of confirmatory factor analysis (CFA) for multitrait-multimethod (MTMM) data sets is presented. It is shown that this model can be defined by only three assumptions in the framework of classical psychometric test theory (CTT). All other properties of the model, particularly the uncorrelated-ness of the trait with the method factors are logical consequences of the definition of the model. In the model proposed there are as many trait factors as different traits considered, but the number of method factors is one fewer than the number of methods included in an MTMM study. The covariance structure implied by this model is derived, and it is shown that this model is identified even under conditions under which other CFA-MTMM models are not. The model is illustrated by two empirical applications. Furthermore, its advantages and limitations are discussed with respect to previously developed CFA models for MTMM data.     

内隐记忆和内隐学习的整合研究趋向

内隐记忆和内隐学习代表了人类学习与记忆的无意识机制,它们打开了理解人类无意识奥妙的大门。从内隐记忆和内隐学习的诞生与实验室发展来看,二者似乎长期以来处在相对独立的位置。然而,随着研究的不断深入,走向整合已经成为不可抑制的趋向。该文旨在结合内隐记忆和内隐学习的定义、经验类似性、理论框架和实验研究等方面的证据,论述它们的整合必要以及已有的整合途径,促进研究对学习和记忆的无意识过程的全面把握     

人工语法范式下的无意识知识探析

人工语法范式不仅是内隐学习领域广泛运用的范式,同时也是研究无意识知识的主要方法。文章通过对人工语法范式组成材料中的刺激频率、组块等方面进行深入探讨,不仅使人们对无意识知识有了更全面的认识,而且也有利于对内隐学习获得规则知识的假设提出更确定的回答;并通过对无意识知识的神经机制研究以及考察镜射规则学习过程的脑神经激活状况,获得了规则学习对应更为准确的脑神经活动,推动了人工语法范式下无意识知识的深入研究。     

Philosophy’s Diversity Problem

This paper explores the underrepresentation of women and minorities in academic philosophy. Specifically, it focuses on why, given the relatively even male/female ratio at undergraduate level, women are underrepresented at every level above this. It addresses some of the misconceptions and myths surrounding women in philosophy, including those surrounding the discussion of the different‐intuition hypothesis. It also explores the ways in which feminist research in philosophy is subject to marginalisation as a result of systematic exclusionary practices typical of the dominant culture of justification within traditional philosophy. It argues for the necessity of an intersectional approach, and the importance of taking seriously the psychological and sociological analysis of underrepresentation, in order to improve both the experience and the representation of women and other minorities at all levels of professional philosophy, as well as of the discipline as a whole.     

Conceptualizing Creativity: General and Cultural Biases in Gough's Creative Personality Scale

This study analyzes the validity of Gough's Creative Personality Scale (CPS) for the Adjective Check List (ACL) by using 1773 Swiss, South Korean, and Mainland Chinese students as a sample. Four sources of potential bias were identified in Gough's CPS, two of which are general and two cultural in nature. The two general biases were investigated by conducting correlation analyses and evaluating alternative scoring methods for the CPS. As a result of the first bias, checking a large number of adjectives was found to be more important for achieving a high score than checking the relevant ones. Due to the second bias, the CPS score mostly depends on the number of positive adjectives checked while negative items have little impact. The two cultural biases were analyzed using an implicit version of the CPS (iCPS) and factor analysis. The latter revealed three different clusters of creativity type: exploratory‐type, socially responsible‐type, and intellectual‐type creativity. Based on cultural background, they are all weighted differently, causing a potential experiential bias in the CPS. Findings indicate that in South Korea and Mainland China socially responsible‐type creativity dominates whereas in Switzerland exploratory‐type creativity prevails. Findings from the iCPS suggest the second cultural bias, the socially desirable responding bias arising from differences in responding styles among the three cultures.     

Out-of-bag Prediction Error: A Cross Validation Index for Generalized Structured Component Analysis

Cross validation is a useful way of comparing predictive generalizability of theoretically plausible a priori models in structural equation modeling (SEM). A number of overall or local cross validation indices have been proposed for existing factor-based and component-based approaches to SEM, including covariance structure analysis and partial least squares path modeling. However, there is no such cross validation index available for generalized structured component analysis (GSCA) which is another component-based approach. We thus propose a cross validation index for GSCA, called Out-of-bag Prediction Error (OPE), which estimates the expected prediction error of a model over replications of so-called in-bag and out-of-bag samples constructed through the implementation of the bootstrap method. The calculation of this index is well-suited to the estimation procedure of GSCA, which uses the bootstrap method to obtain the standard errors or confidence intervals of parameter estimates. We empirically evaluate the performance of the proposed index through the analyses of both simulated and real data.     

A Limited Information Estimator for Dynamic Factor Models

Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor models within the SEM framework. We demonstrate the implementation of this estimator and examine its performance under both correct and incorrect model specifications via a small simulation study. The estimates from this estimator are compared to those from the most common system-wide estimators and are found to be more robust to the structural misspecifications considered.