Object‐Label‐Order Effect When Learning From an Inconsistent Source

Learning in natural environments is often characterized by a degree of inconsistency from an input. These inconsistencies occur, for example, when learning from more than one source, or when the presence of environmental noise distorts incoming information; as a result, the task faced by the learner becomes ambiguous. In this study, we investigate how learners handle such situations. We focus on the setting where a learner receives and processes a sequence of utterances to master associations between objects and their labels, where the source is inconsistent by design: It uses both “correct” and “incorrect” object‐label pairings. We hypothesize that depending on the order of presentation, the result of the learning may be different. To this end, we consider two types of symbolic learning procedures: the Object‐Label (OL) and the Label‐Object (LO) process. In the OL process, the learner is first exposed to the object, and then the label. In the LO process, this order is reversed. We perform experiments with human subjects, and also construct a computational model that is based on a nonlinear stochastic reinforcement learning algorithm. It is observed experimentally that OL learners are generally better at processing inconsistent input compared to LO learners. We show that the patterns observed in the learning experiments can be reproduced in the simulations if the model includes (a) an ability to regularize the input (and also to do the opposite, i.e., undermatch) and (b) an ability to take account of implicit negative evidence (i.e., interactions among different objects/labels). The model suggests that while both types of learners utilize implicit negative evidence in a similar way, there is a difference in regularization patterns: OL learners regularize the input, whereas LO learners undermatch. As a result, OL learners are able to form a more consistent system of image‐utterance associations, despite the ambiguous learning task.     

Pseudo maximum likelihood estimation and a test for misspecification in mean and covariance structure models

Using the theory of pseudo maximum likelihood estimation the asymptotic covariance matrix of maximum likelihood estimates for mean and covariance structure models is given for the case where the variables are not multivariate normal. This asymptotic covariance matrix is consistently estimated without the computation of the empirical fourth order moment matrix. Using quasi-maximum likelihood theory a Hausman misspecification test is developed. This test is sensitive to misspecification caused by errors that are correlated with the independent variables. This misspecification cannot be detected by the test statistics currently used in covariance structure analysis.For helpful comments on a previous draft of the paper we are indebted to Kenneth A. Bollen, Ulrich L. Küsters, Michael E. Sobel and the anonymous reviewers of Psychometrika. For partial research support, the first author wishes to thank the Department of Sociology at the University of Arizona, where he was a visiting professor during the fall semester 1987.     

On the interpretation of the CHC factor Gc

On the one hand, the factors Gf and Gc in the Cattell-Horn-Carroll (CHC) model of intelligence are hypothesized to represent individual differences in unique psychological or biological capacities. On the other hand, they are interpreted as representing the theoretical variables fluid and crystallized intelligence in investment theory. With respect to Gc, this leads to a theoretical conflict because in investment theory crystallized intelligence is not a capacity but purely a statistical entity. We contend that if CHC factor Gc represents a capacity, it cannot represent crystallized intelligence, and if Gc represents crystallized intelligence, it does not represent a capacity. In addition, from our discussion of Gc, we conclude that in investment theory the factors Gf and g represent one and the same capacity. In support of our theoretical conclusions, we implemented the CHC model in a confirmatory factor analysis of a HCA (Human Cognitive Abilities project) data set. As expected, Gf and g were statistically indistinguishable. Gc was effectively absent, because it was statistically equivalent to verbal comprehension. Factors Gc and g could be removed from the model without any reduction in model fit. We argue that in the CHC taxonomy the factors Gc and g are redundant as explanatory variables.     

Cross-Sectional Analysis of Longitudinal Mediation Processes

Statistical mediation analysis can help to identify and explain the mechanisms behind psychological processes. Examining a set of variables for mediation effects is a ubiquitous process in the social sciences literature; however, despite evidence suggesting that cross-sectional data can misrepresent the mediation of longitudinal processes, cross-sectional analyses continue to be used in this manner. Alternative longitudinal mediation models, including those rooted in a structural equation modeling framework (cross-lagged panel, latent growth curve, and latent difference score models) are currently available and may provide a better representation of mediation processes for longitudinal data. The purpose of this paper is twofold: first, we provide a comparison of cross-sectional and longitudinal mediation models; second, we advocate using models to evaluate mediation effects that capture the temporal sequence of the process under study. Two separate empirical examples are presented to illustrate differences in the conclusions drawn from cross-sectional and longitudinal mediation analyses. Findings from these examples yielded substantial differences in interpretations between the cross-sectional and longitudinal mediation models considered here. Based on these observations, researchers should use caution when attempting to use cross-sectional data in place of longitudinal data for mediation analyses.     

Examining Transfer Effects of Dual-Task Training Protocols for a Complex Locomotor Task

Training protocols designed to improve dual-task performance of an obstacle crossing and auditory Stroop task (OBS+Stroop) were tested. In Experiment 1, following baseline collection of OBS+Stroop trials, proximally related walking training was performed, and participants were then retested on the OBS+Stroop test. After training, participants adopted a more cautious obstacle crossing strategy, indicating a potentially safer navigation strategy. Transfer effects from distally related training were then examined (Experiment 2); a computer game training paradigm was examined using the same testing protocol as Experiment 1. Computer training demonstrated improved dual-task performance on some measures, but did not induce a more cautious stepping strategy. Results indicate that dual-task training needs to be similar to targeted tasks to yield reliable, positive training outcomes.     

Intellectual property protection,technological innovation and enterprise value—An empirical study on panel data of 80 advanced manufacturing SMEs

Study the role of intellectual property protection in advanced manufacturing SMEs. Is it to protect the growth of advanced manufacturing SMEs, or to inhibit the growth of SMEs in advanced manufacturing? the paper brings the medium variable – technological innovation to explore acting paths of intellectual property protection in enterprises’ value improvement. Through analysis of panel data of 80 SMEs of advanced manufacture collected during the period from 2013 to 2015, Research results show that as for SMEs of advanced manufacture, the inverted-U relationship still exists in the influences brought by intellectual property protection to enterprise values. Meanwhile, U-shaped relations exist between intellectual property protection and technological innovation. Increase of technological innovation investment can promote increase of enterprise values.     

Achievement-oriented and dependency-oriented parental psychological control: An examination of specificity to middle childhood achievement and dependency-related problems

Middle childhood is considered a sensitive phase for the development of both dependency and achievement-related problems. In order to target efficient prevention and treatment, it is necessary to identify unique associations with possible precursors. This study hypothesized that children’s dependency-related problems (i.e., separation anxiety and generalized anxiety) would uniquely relate to parental dependency-oriented psychological control and that children’s achievement-related problems (i.e., extrinsic motivation and maladaptive perfectionism) would uniquely relate to parental achievement-oriented psychological control. The study included 180 elementary school children. Results showed that higher levels of children’s separation anxiety uniquely related to more parental dependency-oriented psychological control, while higher levels of children’s extrinsic motivation uniquely related to more parental achievement-oriented psychological control. Contrary to the expectations, higher levels of children’s maladaptive perfectionism were associated with both dimensions of parental psychological control, and children’s generalized anxiety was not associated with any dimension. Study implications are discussed.     

An overview of applied robust methods

Data in social sciences are typically non-normally distributed and characterized by heavy tails. However, most widely used methods in social sciences are still based on the analyses of sample means and sample covariances. While these conventional methods continue to be used to address new substantive issues, conclusions reached can be inaccurate or misleading. Although there is no ‘best method’ in practice, robust methods that consider the distribution of the data can perform substantially better than the conventional methods. This article gives an overview of robust procedures, emphasizing a few that have been repeatedly shown to work well for models that are widely used in social and behavioural sciences. Real data examples show how to use the robust methods for latent variable models and for moderated mediation analysis when a regression model contains categorical covariates and product terms. Results and logical analyses indicate that robust methods yield more efficient parameter estimates, more reliable model evaluation, more reliable model/data diagnostics, and more trustworthy conclusions when conducting replication studies. R and SAS programs are provided for routine applications of the recommended robust method.     

工作嵌入的概念、测量及相关变量

工作嵌入代表个体与工作的嵌入程度,用于解释个体为什么留在组织中的一系列因素,具有非情感性、多维度的特点。工作嵌入明显区别于工作满意度、组织承诺等组织行为学变量,包括匹配、联结以及牺牲三个维度。该文明确了工作嵌入与相关变量的概念区别,重点介绍了工作嵌入测量方法的发展以及其在组织行为领域中的研究成果,并指出了今后有必要对其影响因素、测量工具、维度以及结果变量拓展等四个方面进行探讨     

归纳推理属性效应中背景关系提取原则的探究

本实验以生态关系、类别关系和无关三类项目为推理背景, 以基因X和疾病Y为不同的推理属性, 通过考察实验材料快速或延迟呈现时对推理可能性和推理信心的影响来探究属性效应中背景关系提取的原则。结果发现实验材料快速和延迟呈现时对推理可能性无显著影响, 但对推理信心有显著影响, 快速呈现时被试的推理信心显著高于延迟呈现时。由此可以认为归纳推理属性效应中人们优先按照相关性原则提取背景关系, 同时该结果还表明属性效应既有跨文化的普遍性也有其文化特殊性。