Evolution of communication in perfect and imperfect worlds

We extend previous work on cooperation to some related questions regarding the evolution of simple forms of communication. The evolution of cooperation within the iterated Prisoner's Dilemma has been shown to follow different patterns, with significantly different outcomes, depending on whether the features of the model are classically perfect or stochastically imperfect (Axelrod, 1980a,b, 1984, 1985; Axelrod and Hamilton, 1981; Nowak and Sigmund, 1990, 1992; Sigmund, 1993). Our results here show that the same holds for communication. Within a simple model, the evolution of communication seems to require a stochastically imperfect world.     

INTEGRAL GEOGRAPHY: SPACE,PLACE, AND PERSPECTIVE

Considering the role of space and place in Integral Ecology is presented as the concept of Integral Geography. First, an ecological AQAL model is proposed to situate the diverse scientific disciplines used in geography, giving equal consideration for their respective contributions in knowledge and understanding of the world. Second, a model for incorporating perspectives provided in the arts and humanities is proposed in situating scientific understanding in relation to aesthetic and cultural aspects of “being and becoming.” Third, a Geographical Information System (GIS) based map model illustrates how biophysical and social realities can be viewed and analyzed from a geographical perspective as “Integral places.”     

From Good to Great: Examining Exemplary Counselor Development

Some counselors excel in the profession. Few do so while maintaining a balance in life. This article examines the life of one such individual, Samuel T. Gladding. He models for counselors an aspirational process of making significant contributions to the profession while staying personally grounded. Qualities that propel professionals like Gladding from being good as a young professional into prominence as a mature counselor are examined.     

Can Psychoanalysis Become Empirically Supported?

The article discusses the application of the ideas of evidence-based medicine or empirically supported treatment to the field of psychoanalysis. The author argues that empirical support is gradual rather than categorical, and that the randomized clinical trial (RCT), which is commonly heralded as the appropriate method of support, confounds patient suitability factors with treatment 'as such'. As a consequence of the exclusive reliance on the RCT, empirical validation of psychological treatments has generally favoured cognitive-behavioural treatments, although there is ample evidence - albeit non-RCT - for the beneficial effects of short-term and long-term psychoanalytic treatment. The author argues for a suitability matching approach to empirical validation, where each treatment is tested, as powerfully as feasible, on samples based on selfselection (patient's choice, clinical assignment, suitability judgments etc.). The paper concludes by referring to two recent studies, indicating that there is reason to evaluate alleged empirical support with caution.     

A New Model for Acquiescence at the Interface of Psychometrics and Cognitive Psychology

ABSTRACT

When measuring psychological traits, one has to consider that respondents often show content-unrelated response behavior in answering questionnaires. To disentangle the target trait and two such response styles, extreme responding and midpoint responding, Böckenholt (2012a Böckenholt, U. (2012a). Modeling multiple response processes in judgment and choice. Psychological Methods, 17, 665–678. doi:10.1037/a0028111[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) developed an item response model based on a latent processing tree structure. We propose a theoretically motivated extension of this model to also measure acquiescence, the tendency to agree with both regular and reversed items. Substantively, our approach builds on multinomial processing tree (MPT) models that are used in cognitive psychology to disentangle qualitatively distinct processes. Accordingly, the new model for response styles assumes a mixture distribution of affirmative responses, which are either determined by the underlying target trait or by acquiescence. In order to estimate the model parameters, we rely on Bayesian hierarchical estimation of MPT models. In simulations, we show that the model provides unbiased estimates of response styles and the target trait, and we compare the new model and Böckenholt’s model in a recovery study. An empirical example from personality psychology is used for illustrative purposes.     

Improved Insight into and Prediction of Network Dynamics by Combining VAR and Dimension Reduction

To understand within-person psychological processes, one may fit VAR(1) models (or continuous-time variants thereof) to multivariate time series and display the VAR(1) coefficients as a network. This approach has two major problems. First, the contemporaneous correlations between the variables will frequently be substantial, yielding multicollinearity issues. In addition, the shared effects of the variables are not included in the network. Consequently, VAR(1) networks can be hard to interpret. Second, crossvalidation results show that the highly parametrized VAR(1) model is prone to overfitting. In this article, we compare the pros and cons of two potential solutions to both problems. The first is to impose a lasso penalty on the VAR(1) coefficients, setting some of them to zero. The second, which has not yet been pursued in psychological network analysis, uses principal component VAR(1) (termed PC-VAR(1)). In this approach, the variables are first reduced to a few principal components, which are rotated toward simple structure; then VAR(1) analysis (or a continuous-time analog) is applied to the rotated components. Reanalyzing the data of a single participant of the COGITO study, we show that PC-VAR(1) has the better predictive performance and that networks based on PC-VAR(1) clearly represent both the lagged and the contemporaneous variable relations.     

Modeling Interactions Between Latent Variables in Research on Type D Personality: A Monte Carlo Simulation and Clinical Study of Depression and Anxiety

Several approaches exist to model interactions between latent variables. However, it is unclear how these perform when item scores are skewed and ordinal. Research on Type D personality serves as a good case study for that matter. In Study 1, we fitted a multivariate interaction model to predict depression and anxiety with Type D personality, operationalized as an interaction between its two subcomponents negative affectivity (NA) and social inhibition (SI). We constructed this interaction according to four approaches: (1) sum score product; (2) single product indicator; (3) matched product indicators; and (4) latent moderated structural equations (LMS). In Study 2, we compared these interaction models in a simulation study by assessing for each method the bias and precision of the estimated interaction effect under varying conditions. In Study 1, all methods showed a significant Type D effect on both depression and anxiety, although this effect diminished after including the NA and SI quadratic effects. Study 2 showed that the LMS approach performed best with respect to minimizing bias and maximizing power, even when item scores were ordinal and skewed. However, when latent traits were skewed LMS resulted in more false-positive conclusions, while the Matched PI approach adequately controlled the false-positive rate.     

Evaluating Structural Equation Models for Categorical Outcomes: A New Test Statistic and a Practical Challenge of Interpretation

This research is concerned with two topics in assessing model fit for categorical data analysis. The first topic involves the application of a limited-information overall test, introduced in the item response theory literature, to structural equation modeling (SEM) of categorical outcome variables. Most popular SEM test statistics assess how well the model reproduces estimated polychoric correlations. In contrast, limited-information test statistics assess how well the underlying categorical data are reproduced. Here, the recently introduced C₂ statistic of Cai and Monroe (2014) is applied. The second topic concerns how the root mean square error of approximation (RMSEA) fit index can be affected by the number of categories in the outcome variable. This relationship creates challenges for interpreting RMSEA. While the two topics initially appear unrelated, they may conveniently be studied in tandem since RMSEA is based on an overall test statistic, such as C₂. The results are illustrated with an empirical application to data from a large-scale educational survey.     

Trait anxiety and trait anger measured by ecological momentary assessment and their correspondence with traditional trait questionnaires

Ecological momentary assessments (EMA) of anxiety and anger/hostility were obtained every 25–30 min over two 24-h periods, separated by a median of 6 months, from 165 employees at a university in the Northeast. We used a multilevel trait-state-error structural equation model to estimate: (1) the proportion of variance in EMA anxiety and anger/hostility attributable to stable trait-like individual differences; (2) the correspondence between these trait-like components of EMA anxiety and anger/hostility and traditional questionnaire measures of each construct; and (3) the test–retest correlation between two 24-h averages obtained several months apart. After adjustment for measurement error, more than half the total variance in EMA reports of anxiety and anger/hostility is attributable to stable trait-like individual differences; however, the trait-like component of each construct is only modestly correlated with questionnaire measures of that construct. The 6-month “test–retest” correlations of latent variables representing the true 24-h EMA average anxiety and average anger are quite high (r ? 0.83). This study represents the longest follow-up period over which EMA-based estimates of traits have been examined. The results suggest that although the trait component (individual differences) of EMA momentary ratings of anxiety and anger is larger than the state component, traditional self-report questionnaires of trait anxiety and anger correspond only weakly with EMA-defined traits.