Intruding male red swamp crayfish,Procambarus clarkii,immediately dominate members of established communities of smaller,mixed-sex conspecifics

The effects of competing asymmetries (intruder size advantage vs. prior residence) on dominance relationships were investigated in a laboratory setting. Sexually mature (Form I) male red swamp crayfish, 25%-27% larger than the average member of several mixedsex communities of 20-25 sexually mature conspecifics, individually intruded upon these communities on successive days. Each community was invaded once a day, with each of these large intruders invading every community once during the experiment. Five days after the last large intruder invasion, novel intruder group males, approximately the same size as the average community member, individually invaded the same communities, all communities being invaded once during a single day of testing. These novel intruders were used to differentiate the effects of intruder size from those effects of being put into a novel environment. During each intrusion, the frequencies of dominance, submission, aggressive standoffs, and nonaggressive interactions between the intruder and members of the community were recorded. The large intruders on each day immediately and virtually completely dominated all encountered community members, and the large intruders were significantly more dominant than the novel, smaller-sized intruders. The size advantage of the large intruders overwhelmed prior residence in influencing dominance outcomes, even in these well established communities. © 1995 Wiley-Liss, Inc.     

Notes on selective influence, probabilistic causality, and probabilistic dimensionality

The paper provides conceptual clarifications for the issues related to the dependence of jointly distributed systems of random entities on external factors. This includes the theory of selective influence as proposed in Dzhafarov [(2003a). Selective influence through conditional independence. Psychometrika, 68, 7-26] and generalized versions of the notions of probabilistic causality [Suppes, P., & Zanotti, M. (1981). When are probabilistic explanations possible? Synthese, 48, 191-199] and dimensionality in the latent variable models [Levine, M. V. (2003). Dimension in latent variable models. Journal of Mathematical Psychology, 47, 450-466]. One of the basic observations is that any system of random entities whose joint distribution depends on a factor set can be represented by functions of two arguments: a single factor-independent source of randomness and the factor set itself. In the case of random variables (i.e., real-valued random entities endowed with Borel sigma-algebras) the single source of randomness can be chosen to be any random variable with a continuous distribution (e.g., uniformly distributed between 0 and 1).     

Maximum Likelihood Methods in Treating Outliers and Symmetrically Heavy-Tailed Distributions for Nonlinear Structural Equation Models with Missing Data

By means of more than a dozen user friendly packages, structural equation models (SEMs) are widely used in behavioral, education, social, and psychological research. As the underlying theory and methods in these packages are vulnerable to outliers and distributions with longer-than-normal tails, a fundamental problem in the field is the development of robust methods to reduce the influence of outliers and the distributional deviation in the analysis. In this paper we develop a maximum likelihood (ML) approach that is robust to outliers and symmetrically heavy-tailed distributions for analyzing nonlinear SEMs with ignorable missing data. The analytic strategy is to incorporate a general class of distributions into the latent variables and the error measurements in the measurement and structural equations. A Monte Carlo EM (MCEM) algorithm is constructed to obtain the ML estimates, and a path sampling procedure is implemented to compute the observed-data log-likelihood and then the Bayesian information criterion for model comparison. The proposed methodologies are illustrated with simulation studies and an example. The research described herein was fully supported by a grant (CUHK 4243/03H) from the Rearch Grants Council of the Hong Kong Special Administration Region. The authors are thankful to the Editor, the Associate Editor, and anonymous reviewers for valuable comments which improve the paper significantly, and are grateful to ICPSR and the relevant funding agency for allowing the use of their data. Requests for reprints should be sent to S. Y. Lee, Department of Statistics, The Chinese University of Hong Kong, Shatin, N. T., Hong Kong.     

先前知识对初中学生学习家族相似性类别的影响研究

采用特征—主题的类别学习范式,考察了先前知识对初中学生学习具有家族相似性结构人工类别的影响。结果表明:基于先前知识的主题关系对初中学生的类别学习有显著促进;初中学生在基于已有知识的类别学习中更多地获得了知识关联特征的信息,同时也能对机械特征保持敏感。     

动画速度和学习者经验对多媒体学习的影响

以往研究显示,动画教学的效果很可能受到学习者先前知识经验与动画速度的影响。研究选取高经验和低经验的学习者,操纵动画播放速度为快、中和慢三个水平,利用眼动仪记录学习过程中的注意,探讨学习者经验和动画速度对学习效果和注意分配的影响。结果发现,在即时测验上,学习者经验和动画速度没有交互作用;在延迟测验上,低经验者在快速下成绩优于中速;高经验者对图片注视时间长于低经验者。研究认为,在即时测验上,学习者的经验水平不会影响动画速度的教学效果;在延迟测验上,低经验者在快速呈现动画时学习效果更好,学习者经验和动画速度对学习效果的影响更可能随着时间推移体现出来;基于整体型学习材料,高经验者比低经验者更关注图片。     

Making remembering more memorable*

Memory retrieval is a cognitive operation that itself can be remembered or forgotten, with potentially important consequences. To study memory for prior remembering, we had participants first study target words (e.g., bark) alongside semantically related cue words (e.g., dog). Then, on Test 1, participants retrieved targets in response to either the study cue or a changed cue that was semantically related to a homograph of the target (e.g., birch). Finally, on Test 2, participants retrieved all targets in response to the original study cues, and participants judged whether targets were previously retrieved on Test 1. As in previous research, cue change on Test 1 rendered target retrievals less memorable, suggesting context changes harm memory for prior remembering. We hypothesised that the negative effect of context change could be ameliorated by reminding participants of the original study cues during Test 1. We had participants either retrieve (Experiments 1 and 3, Ns?=?46 and 62) or view (Experiment 2, N?=?118) the study cue following each target retrieval. Reminding significantly reduced the negative effect of cue change, with self-generation being especially potent. This indicates that reminding can make remembering more memorable in the face of context change.     

Defining Creativity: Don't We Also Need to Define What Is Not Creative?

I argue that any attempt to define creative ideas cannot fully succeed without also defining uncreative ideas. This argument begins by defining three parameters that characterize a potentially creative thought: the idea's initial probability (p), the final utility (u), and the creator's prior knowledge of that utility (v). The three parameters then lead to a three‐criterion multiplicative definition of personal creativity, namely, c = (1 ? p)u(1 ? v), where the first factor indicates originality and the third factor surprise. Although creativity can only maximize as originality, utility, and surprise all approach unity, the same definition indicates that there are seven different ways that creativity can minimize. These alternatives were identified as (a) routine, reproductive, or habitual ideas, (b) fortuitous response bias, (c) irrational perseveration, (d) problem finding, (e) rational suppression, (f) irrational suppression, and (g) blissful ignorance. If the third parameter v is omitted, then the number of creative and noncreative outcomes reduces to just four, making creativity indistinguishable from irrational suppression. The alternative outcomes are then illustrated using the classic two‐string problem. Besides providing a more finely differentiated conception of creativity failures, the definition has critical implications regarding the processes and procedures required to generate highly creative ideas.     

A Bayesian analysis of finite mixtures in the LISREL model

In this paper, we propose a Bayesian framework for estimating finite mixtures of the LISREL model. The basic idea in our analysis is to augment the observed data of the manifest variables with the latent variables and the allocation variables. The Gibbs sampler is implemented to obtain the Bayesian solution. Other associated statistical inferences, such as the direct estimation of the latent variables, establishment of a goodness-of-fit assessment for a posited model, Bayesian classification, residual and outlier analyses, are discussed. The methodology is illustrated with a simulation study and a real example.This research was supported by a Hong Kong UGC Earmarked grant CUHK 4026/97H. The authors are indebted to the Editor, the Associate Editor, and three anonymous reviewers for constructive comments in improving the paper, and also to ICPSR and the relevant funding agency for allowing the use of the data. The assistance of Michael K.H. Leung and Esther L.S. Tam is gratefully acknowledged.     

Regression away from the mean: Theory and examples

Using a standard repeated measures model with arbitrary true score distribution and normal error variables, we present some fundamental closed-form results which explicitly indicate the conditions under which regression effects towards (RTM) and away from the mean are expected. Specifically, we show that for skewed and bimodal distributions many or even most cases will show a regression effect that is in expectation away from the mean, or that is not just towards but actually beyond the mean. We illustrate our results in quantitative detail with typical examples from experimental and biometric applications, which exhibit a clear regression away from the mean (‘egression from the mean’) signature. We aim not to repeal cautionary advice against potential RTM effects, but to present a balanced view of regression effects, based on a clear identification of the conditions governing the form that regression effects take in repeated measures designs.