Accounting for fluctuations in body dissatisfaction

The present study evaluated whether the strength of relationship between contextual cues (presence of company and mood) and state body dissatisfaction varied as a function of individual differences in key trait measures (body shame, body surveillance tendencies, internalization of appearance standards, and trait affect) which have been linked to trait body dissatisfaction. Fifty-five undergraduate women completed a questionnaire containing the trait-based measures and then carried a Personal Digital Assistant (PDA) for a 7-day period. The PDA prompted participants six times daily to self-report their current mood and state body dissatisfaction. Multi-level modeling revealed that individual differences in body shame predicted inter-individual variability in the strength of the relationships between presence of company and state body dissatisfaction, and positive mood and state body dissatisfaction. Trait positive affect also explained variance in the positive mood state-body dissatisfaction relationship. The implications of the findings for prevention of body image disturbances are discussed.     

Preparing or executing the wrong task: The influence on switch effects

In a previous study, it was proposed that executing a task leads to task strengthening. In other words, task activation at the moment of response execution determines subsequent switch effects (Steinhauser & Hübner, 2006). The authors investigated this issue by comparing switch effects after task and response errors. However, the use of bivalent stimulus–response mappings might have obscured some of the effects. Therefore, we replicated the experiment using univalent stimulus–response mappings. With this adjusted design, which overcomes some shortcomings of the original study, we were able to replicate the finding of switch benefits after task errors. Closer inspection of the data showed the importance of preexecution processes on subsequent switch effects. In a second experiment, we further elaborated on these preexecution processes. More precisely, we investigated the effect of task preparation on subsequent switch effects. Taken together, our data extend current accounts of task switching by showing that the preparatory processes occurring before the response on trial n influence the switch cost on trial n?+?1.     

Effects of stimulus order on discrimination processes in comparative and equality judgements: Data and models

In typical discrimination experiments, participants are presented with a constant standard and a variable comparison stimulus and their task is to judge which of these two stimuli is larger (comparative judgement). In these experiments, discrimination sensitivity depends on the temporal order of these stimuli (Type B effect) and is usually higher when the standard precedes rather than follows the comparison. Here, we outline how two models of stimulus discrimination can account for the Type B effect, namely the weighted difference model (or basic Sensation Weighting model) and the Internal Reference Model. For both models, the predicted psychometric functions for comparative judgements as well as for equality judgements, in which participants indicate whether they perceived the two stimuli to be equal or not equal, are derived and it is shown that the models also predict a Type B effect for equality judgements. In the empirical part, the models' predictions are evaluated. To this end, participants performed a duration discrimination task with comparative judgements and with equality judgements. In line with the models' predictions, a Type B effect was observed for both judgement types. In addition, a time-order error, as indicated by shifts of the psychometric functions, and differences in response times were observed only for the equality judgement. Since both models entail distinct additional predictions, it seems worthwhile for future research to unite the two models into one conceptual framework.     

Being there when it happens: A novel approach to sampling reflectively observed experience

Examining a number of recently developed methods for acquiring first-person data on consciousness, we detect a lack of sensitivity for distinguishing the experiential moments in which the experiencing person was reflectively attending to her ongoing experience. In order to address this gap, we introduce a novel research format for obtaining data on lived experience, combining random sampling of experience with a subsequent retrospective examination of acquired samples in the form of dialogical phenomenological inquiry. The proposed approach aims at the examination of reflectively observed experiential moments and is based on researchers’ iterative cultivation of the phenomenological attitude. Drawing upon results from a longitudinal study of the potential of meditation as a tool for examining consciousness, we address the epistemological and methodological challenges of the proposed approach, discuss its applicability and research potential, as well as examine the characteristics and validity of phenomenal data thus acquired.     

Inferences about which of J dependent groups has the largest robust measure of location

Recently, a multiple comparisons procedure was derived with the goal of determining whether it is reasonable to make a decision about which of J independent groups has the largest robust measure of location. This was done by testing hypotheses aimed at comparing the group with the largest estimate to the remaining J − 1 groups. It was demonstrated that for the goal of controlling the familywise error rate, meaning the probability of one or more Type I errors, well-known improvements on the Bonferroni method can perform poorly. A technique for dealing with this issue was suggested and found to perform well in simulations. However, when dealing with dependent groups, the method is unsatisfactory. This note suggests an alternative method that is designed for dependent groups.     

Bayesian factor analysis for multilevel binary observations

Multilevel covariance structure models have become increasingly popular in the psychometric literature in the past few years to account for population heterogeneity and complex study designs. We develop practical simulation based procedures for Bayesian inference of multilevel binary factor analysis models. We illustrate how Markov Chain Monte Carlo procedures such as Gibbs sampling and Metropolis-Hastings methods can be used to perform Bayesian inference, model checking and model comparison without the need for multidimensional numerical integration. We illustrate the proposed estimation methods using three simulation studies and an application involving student's achievement results in different areas of mathematics. The authors thank Ian Westbury, University of Illinois at Urbana Champaign for kindly providing the SIMS data for the application.     

An improved stochastic EM algorithm for large-scale full-information item factor analysis

In this paper, we explore the use of the stochastic EM algorithm (Celeux & Diebolt (1985) Computational Statistics Quarterly, 2, 73) for large-scale full-information item factor analysis. Innovations have been made on its implementation, including an adaptive-rejection-based Gibbs sampler for the stochastic E step, a proximal gradient descent algorithm for the optimization in the M step, and diagnostic procedures for determining the burn-in size and the stopping of the algorithm. These developments are based on the theoretical results of Nielsen (2000, Bernoulli, 6, 457), as well as advanced sampling and optimization techniques. The proposed algorithm is computationally efficient and virtually tuning-free, making it scalable to large-scale data with many latent traits (e.g. more than five latent traits) and easy to use for practitioners. Standard errors of parameter estimation are also obtained based on the missing-information identity (Louis, 1982, Journal of the Royal Statistical Society, Series B, 44, 226). The performance of the algorithm is evaluated through simulation studies and an application to the analysis of the IPIP-NEO personality inventory. Extensions of the proposed algorithm to other latent variable models are discussed.     

大脑前扣带回皮层与执行功能

回顾了近期有关大脑前扣带回皮层（ACC）参与执行功能的研究资料。结果显示,ACC可以对正在进行的目标定向行为实施在线监控,在出现反应冲突或错误时提供信号,以便能及时调整与分配注意资源,因此可能是一个行为规划与执行的高级调控结构。这两年来的新发现提示,ACC的执行监控作用可能是基于动机与奖赏期待做出的对“意志行动”的整合性控制,并可能具有对行动结果的情绪评价意义。