Improvement in working memory is not related to increased intelligence scores

The acknowledged high relationship between working memory and intelligence suggests common underlying cognitive mechanisms and, perhaps, shared biological substrates. If this is the case, improvement in working memory by repeated exposure to challenging span tasks might be reflected in increased intelligence scores. Here we report a study in which 288 university undergraduates completed the odd numbered items of four intelligence tests on time 1 and the even numbered items of the same tests one month later (time 2). In between, 173 participants completed three sessions, separated by exactly one week, comprising verbal, numerical, and spatial short-term memory (STM) and working memory (WMC) tasks imposing high processing demands (STM–WMC group). 115 participants also completed three sessions, separated by exactly one week, but comprising verbal, numerical, and spatial simple speed tasks (processing speed, PS, and attention, ATT) with very low processing demands (PS-ATT group). The main finding reveals increased scores from the pre-test to the post-test intelligence session (more than half a standard deviation on average). However, there was no differential improvement on intelligence between the STM-WMC and PS-ATT groups.     

Learning to Troubleshoot: Multistrategy Learning of Diagnostic Knowledge for a Real-World Problem-Solving Task

This article presents a computational model of the learning of diagnostic knowledge, based on observations of human operators engaged in real-world troubleshooting tasks. We present a model of problem solving and learning in which the reasoner introspects about its own performance on the problem-solving task, identifies what it needs to learn to improve its performance, formulates learning goals to acquire the required knowledge, and pursues its learning goals using multiple learning strategies. The model is implemented in a computer system which provides a case study based on observations of troubleshooting operators and protocol analysis of the data gathered in the test area of an operational electronics manufacturing plant. The model not only addresses issues in human learning, but, in addition, is computationally justified as a uniform, extensible framework for multistrategy learning.     

Nonlinear Structured Growth Mixture Models in Mplus and OpenMx

Growth mixture models (GMMs; B. O. Muthén & Muthén, 2000; B. O. Muthén & Shedden, 1999) are a combination of latent curve models (LCMs) and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. GMMs are often fit with linear, latent basis, multiphase, or polynomial change models because of their common use, flexibility in modeling many types of change patterns, the availability of statistical programs to fit such models, and the ease of programming. In this article, we present additional ways of modeling nonlinear change patterns with GMMs. Specifically, we show how LCMs that follow specific nonlinear functions can be extended to examine the presence of multiple latent classes using the Mplus and OpenMx computer programs. These models are fit to longitudinal reading data from the Early Childhood Longitudinal Study–Kindergarten Cohort to illustrate their use.     

Neighborhood and community factors: effects on deviant behavior and social competence

Socialization in a neighborhood and community at risk, defined in terms of violence, social alienation, school failure, and disruptive behavior, is a risk factor for the acquisition of antisocial and delinquent behavior. In order to test this hypothesis and examine the underlying mechanisms involved, 346 participants, 155 high-risk and 191 low-risk, aged 11 to 13, that is, under the age of criminal responsibility as established by the Spanish Law 5/2000 were selected. The results reveal that high-risk youngsters had higher rates of antisocial behavior and lower levels of social skills (i.e., greater tendency to externalize attribution of responsibility, fewer conflict resolution strategies, lower self esteem, and a lower degrees of emotional intelligence) in comparison to the lower-risk group. Finally, the results and implications of the study are discussed in the light of designing prevention programs.     

Rotating item banks versus restriction of maximum exposure rates in computerized adaptive testing

If examinees were to know, beforehand, part of the content of a computerized adaptive test, their estimated trait levels would then have a marked positive bias. One of the strategies to avoid this consists of dividing a large item bank into several sub-banks and rotating the sub-bank employed (Ariel, Veldkamp & van der Linden, 2004). This strategy permits substantial improvements in exposure control at little cost to measurement accuracy, However, we do not know whether this option provides better results than using the master bank with greater restriction in the maximum exposure rates (Sympson & Hetter, 1985). In order to investigate this issue, we worked with several simulated banks of 2100 items, comparing them, for RMSE and overlap rate, with the same banks divided in two, three... up to seven sub-banks. By means of extensive manipulation of the maximum exposure rate in each bank, we found that the option of rotating banks slightly outperformed the option of restricting maximum exposure rate of the master bank by means of the Sympson-Hetter method.     

Recidivism risk assessment in sex offenders

Violent behaviour risk assessment is one of the most relevant research areas in current Psychology of Crime. Various scales for violence risk assessment have recently been developed from research about crime careers and risk factors. One of these instruments is the Sexual Violence Risk Assessment-20 (SVR-20), translated and adapted to Spanish by the Group of Advanced Studies in Violence of the University of Barcelona. The goal of this study is to verify the predictive capacity of the SVR-20 to predict sexual violence recidivism in a Spanish sample of sexual offender inmates. The method used was a retrospective study based in 163 sexual offender files and a 4-year time lag. The data were analysed with the logistic regression technique. Of the sample, 79.9% non-recidivist individuals were correctly classified, and 70.8% recidivist individuals. The ROC curve obtained for the model shows a very good discriminant capacity for the SVR-20, with a 0.83 AUC value. The main conclusion of this study is that the Spanish adaptation of SVR-20 is a good instrument to predict the risk of sexual violence.     

Classification statistical techniques: an applied and comparative study

The aim of this article is to assess and compare three classification statistical techniques--logistic regression, discriminant analysis and classification trees--to identify the personality characteristics associated with the risk of suffering from ischemic cardiovascular acute episodes (ICAE). The sample comprised 313 participants, men and women, aged from 36 to 80. Participants were divided into two groups: a clinical group of patients (n = 143) who were diagnosed as suffering from ICAE, and a control group (n = 170). Both groups were equated in gender, age, socio-economic and educational level. In view of the comparative study of the analytical procedures, we recommend classification trees as the best choice, as it was the most accurate for the individuals in the clinical group, a simple data analysis and a meaningful clinical interpretation. The predictive validity analysis of the MCMI-II allowed the construction of a reduced version made up of 9 personality scales from the 22 scales in the original version. Thus, we could identify the patients with a higher probability of suffering from ICAE, and additionally, generate an empirical model comprising seven and five personality profiles associated, respectively, with the increase and the decrease of the probability of suffering from ICAE.     

Designing Organizations: Does Expertise Matter?

From an organizational cognition standpoint, we approach organizational design as an ongoing creative sensemaking process. This study examined the role of expertise in the cognitive problem-solving patterns underlying design processes and the resulting organizational forms. The simulated problem elicited the mental models applied by naives, novices, and experts in designing an organization. The thinking-aloud protocol analysis revealed quantitative and qualitative expert/nonexpert differences in problem-solving strategies, the time spent on problem representation, and the justifications and difficulties expressed in the course of the design process. In addition, our results showed that naives created organizations consistent with mechanistic structures, while novices and experts created organizations consistent with organic structures. We discuss the implications of these findings for the understanding of the cognitive basis of organizational design and the development of effective training programs.