Quantifying the informational value of classification images

Reverse correlation is an influential psychophysical paradigm that uses a participant’s responses to randomly varying images to build a classification image (CI), which is commonly interpreted as a visualization of the participant’s mental representation. It is unclear, however, how to statistically quantify the amount of signal present in CIs, which limits the interpretability of these images. In this article, we propose a novel metric, infoVal, which assesses informational value relative to a resampled random distribution and can be interpreted like a z score. In the first part, we define the infoVal metric and show, through simulations, that it adheres to typical Type I error rates under various task conditions (internal validity). In the second part, we show that the metric correlates with markers of data quality in empirical reverse-correlation data, such as the subjective recognizability, objective discriminability, and test–retest reliability of the CIs (convergent validity). In the final part, we demonstrate how the infoVal metric can be used to compare the informational value of reverse-correlation datasets, by comparing data acquired online with data acquired in a controlled lab environment. We recommend a new standard of good practice in which researchers assess the infoVal scores of reverse-correlation data in order to ensure that they do not read signal in CIs where no signal is present. The infoVal metric is implemented in the open-source rcicr R package, to facilitate its adoption

Применение методов управления себестоимостью продукции

Целью данной работы является исследование новых методов управления себестоимостью продукции на предприятии

On Imbalance of Impulse Control and Sensation Seeking and Adolescent Risk

Heterogeneity in development of imbalance between impulse control and sensation seeking has not been studied until now. The present study scrutinized this heterogeneity and the link between imbalance and adolescent risk. Seven-wave data of 7,558 youth (50.71% males; age range from 12/13 until 24/25) were used. Three developmental trajectories were identified. The first trajectory, “sensation seeking to balanced sensation seeking”, included participants with a higher level of sensation seeking than impulse control across all ages. The second trajectory, “moderate dominant control”, included participants showing moderate and increasing impulse control relative to sensation seeking across all ages. The third trajectory, “strong late dominant control”, included participants showing the highest level of impulse control which was about as strong as sensation seeking from early to middle adolescence and became substantially stronger from late adolescence to early adulthood. Although the systematic increase of impulse control in all subgroups is in line with both models, neither of these combined trajectories of control and sensation seeking was predicted by the Dual Systems Model or the Maturational Imbalance Model. Consistent with both models the “sensation seeking to balanced sensation seeking” trajectory showed the highest level of substance use. It can be concluded that, even though both theories adequately predict the link between imbalance and risk,

Cytokine Production by Leukocytes of Military Personnel with Depressive Symptoms after Deployment to a Combat-Zone: A Prospective, Longitudinal Study

Major depressive disorder (MDD) is frequently diagnosed in military personnel returning from deployment. Literature suggests that MDD is associated with a pro-inflammatory state. To the best of our knowledge, no prospective, longitudinal studies on the association between development of depressive symptomatology and cytokine production by peripheral blood leukocytes have been published. The aim of this study was to investigate whether the presence of depressive symptomatology six months after military deployment is associated with the capacity to produce cytokines, as assessed before and after deployment. 1023 military personnel were included before deployment. Depressive symptoms and LPS- and T-cell mitogen-induced production of 16 cytokines and chemokines in whole blood cultures were measured before (T0), 1 (T1), and 6 (T2) months after return from deployment. Exploratory structural equation modeling (ESEM) was used for data reduction into cytokine patterns. Multiple group latent growth modeling was used to investigate differences in the longitudinal course of cytokine production between individuals with (n = 68) and without (n = 665) depressive symptoms at T2. Individuals with depressive symptoms after deployment showed higher T-cell cytokine production before deployment. Moreover, pre-deployment T-cell cytokine production significantly predicted the presence of depressive symptomatology 6 months after return. There was an increase in T-cell cytokine production over time, but this increase was significantly smaller in individuals developing depressive symptoms. T-cell chemokine and LPS-induced innate cytokine production decreased over time and were not associated with depressive symptoms. These results indicate that increased T-cell mitogen-induced cytokine production before deployment may be a vulnerability factor for development of depressive symptomatology in response to deployment to a combat-zone. In addition, deployment to a combat-zone affects the capacity of T-cells and monocytes to produce cytokines and chemokines until at least 6 months after return

Cross-cultural validation of the functional vision questionnaire for children and young people (FVQ_CYP) with visual impairment in the Dutch population: challenges and opportunities

BACKGROUND:To assess cross-cultural validity between Dutch and English versions of the FVQ_CYP, a patient-reported outcome measure developed in the United Kingdom (UK) for children and adolescents with (severe) visual impairment or blindness (VI for brevity) to measure functional vision. METHODS:The 36-item FVQ_CYP was translated and adapted into Dutch using standard guidelines. The questionnaire was administered to Dutch children and adolescents aged 7-17 years (N = 253) with impaired vision (no restrictions regarding acuity). Data were compared to existing UK data of children and adolescents aged 10-15 years (N = 91) with VI (acuity LogMar worse than 0.48). As with the original UK FVQ_CYP validation, a rating scale model (RSM) was applied to the Dutch data. RESULTS:Minor adaptations were needed in translation-rounds. Significant differences in item responses were found between the Dutch and UK data. Item response theory assumptions were met, but fit to the RSM was unsatisfactory. Therefore, psychometric properties of the Dutch FVQ_CYP were analysed irrespective of the original model and criteria used. A graded response model led to the removal of 12 items due to missing data, low information, overlapping content and limited relevance to Dutch children. Fit indices for the remaining 24 items were adequate. CONCLUSIONS:Differences in population characteristics, distribution of responses, non-invariance at the model level and small sample sizes challenged the cross-cultural validation process. However, the Dutch adapted FVQ_CYP showed high measurement precision and broad coverage of items measuring children's functional vision. The underlying reasons for differences between countries in instrument performance are discussed with implications for future studies

Introduction to Bayesian Statistics

In this brief introductory chapter, we sought to inform readers new to Bayesian statistics about the fundamental concepts in Bayesian analyses. The most important take-home messages to remember are that in Bayesian statistics, the analysis starts with an explicit formulation of prior beliefs that are updated with the observed data to obtain a posterior distribution. The posterior distribution is then used to make inferences about probable values of a given parameter (or set of parameters). Furthermore, Bayes Factors allow for comparison of non-nested models, and it is possible to compute the amount of support for the null hypothesis, which cannot be done in the frequentist framework. Subsequent chapters in this volume make use of Bayesian methods for obtaining posteriors of parameters of interest, as well as Bayes Factors

Complexity

To compute the proportion of the parameter space in line with an informative hypothesis by chanc

A Tutorial on Using The Wambs Checklist to Avoid The Misuse of Bayesian Statistics

Chapter 3 guides readers through the steps of the When-to-Worry-and-How-to-Avoid-the-Misuse-of-Bayesian-Statistics checklist (the WAMBS checklist) in order to provide background for other chapters in this book. This chapter supplements the original WAMBS checklist with prior and posterior predictive model checking. We also compare the performance of two popular Bayesian R packages, RStan and rjags. We show why using the Hamiltonian Monte Carlo procedure, available in RStan, is more efficient in small sample sizes. All data and the annotated R code to reproduce the results are available on the Open Science Framework