Simulation Modeling of Policies Directed at Youth Sugar-Sweetened Beverage Consumption

Childhood obesity is a significant public health problem requiring innovative solutions. While recent reviews indicate that some policies show promise, there is a lack of information regarding which policies, and policy combinations, work best. Low-nutrition, energy-dense foods and beverages such as sugar-sweetened beverages (SSBs) have been identified as a major contributor to the problem. The purpose of this paper is to use simulation modeling to show how changes in three categories of SSB policies—school nutrition, school-based education, and taxes—impact SSB and other food consumption. The model shows that policies directed at SSBs, particularly tax hikes, could lead to substantial reductions in the number of calories consumed by youth. The estimates, however, are subject to a high degree of uncertainty. Estimates from school-based nutrition and school-based education policies, while also helping to reduce caloric intake, generally show smaller effects than tax policies and considerable variation around parameter estimates for individual and combined policies. We conclude with a discussion of the limits of the model, and suggest where additional information is needed. Limitations notwithstanding, simulation modeling is a promising methodology that can help advance our understanding of policy effects, thereby helping policymakers to better formulate effective policies to reduce obesity prevalence and the associated social harms.     

Crystal growth and defect study of BiSbTe3

BiSbTe₃ single crystals have been grown by the Bridgman technique. Microscopic observations of the as-grown crystals reveal typical features, such as striations on the top free surface, which are attributed to the effect of growth conditions. A nitric-acid-based reagent capable of revealing dislocations has been developed and tested. Etch pits are produced at the dislocation sites, but some discrepancies have been observed on matched cleavage surfaces. The structural difference between the matched cleavage surfaces is discussed.     

The Effects of Cultural Transmission Are Modulated by the Amount of Information Transmitted

Information changes as it is passed from person to person, with this process of cultural transmission allowing the minds of individuals to shape the information that they transmit. We present mathematical models of cultural transmission which predict that the amount of information passed from person to person should affect the rate at which that information changes. We tested this prediction using a function‐learning task, in which people learn a functional relationship between two variables by observing the values of those variables. We varied the total number of observations and the number of those observations that take unique values. We found an effect of the number of observations, with functions transmitted using fewer observations changing form more quickly. We did not find an effect of the number of unique observations, suggesting that noise in perception or memory may have affected learning.     

Robot studies on saccade-triggered visual prediction

Three robot studies on visual prediction are presented. In all of them, a visual forward model is used, which predicts the visual consequences of saccade-like camera movements. This forward model works by remapping visual information between the pre- and postsaccadic retinal images; at an abstract modeling level, this process is closely related to neurons whose visual receptive fields shift in anticipation of saccades. In the robot studies, predictive remapping is used (1) in the context of saccade adaptation, to reidentify target objects after saccades are carried out; (2) for a model of grasping, in which both fixated and non-fixated target objects are processed by the same foveal mechanism; and (3) in a computational architecture for mental imagery, which generates “gripper appearances” internally without real sensory inflow. The robotic experiments and their underlying computational models are discussed with regard to predictive remapping in the brain, transsaccadic memory, and attention. The results confirm that visual prediction is a mechanism that has to be considered in the design of artificial cognitive agents and the modeling of information processing in the human visual system.     

Perfectionism and coping with daily failures: positive reframing helps achieve satisfaction at the end of the day

Differentiating perfectionistic strivings and perfectionistic concerns, the present study examined how perfectionism predicts what coping strategies people use, when dealing with failures, and how perfectionism and coping influence people's satisfaction. A sample of 149 students completed daily reports for 3–14 days, reporting the most bothersome failure they experienced during the day, what strategies they used to cope with the failure, and how satisfied they felt at the end of the day. Multilevel regression analyses showed that perfectionistic concerns predicted more frequent use of self-blame, less frequent use of active coping and acceptance, and higher satisfaction at the end of the day, whereas perfectionistic strivings predicted less frequent use of self-blame and higher satisfaction. Although positive reframing, acceptance, and humor predicted higher satisfaction for all students, further analyses showed that positive reframing coping was particularly helpful for students high in perfectionistic concern. The findings suggest that accommodative coping strategies are generally helpful in dealing with personal failures, with positive reframing being a coping strategy that works particularly well for people high in perfectionistic concerns (who are prone to dissatisfaction) to achieve higher satisfaction at the end of the day.     

Examining Spirituality Over Time: Latent Growth Curve and Individual Growth Curve Analyses

SUMMARY

Research on spirituality and religiousness has gained growing attention in recent years; however, most studies have used cross-sectional designs. As research on this topic evolves, there has been increasing recognition of the need to examine these constructs and their effects through the use of longitudinal designs. Beyond repeated-measures ANOVA and OLS regression models, what tools are available to examine these constructs over time? The purpose of this paper is to provide an overview of two cutting-edge statistical techniques that will facilitate longitudinal investigations of spirituality and religiousness: latent growth curve analysis using structural equation modeling (SEM) and individual growth curve models. The SEM growth curve approach examines change at the group level, with change over time expressed as a single latent growth factor. In contrast, individual growth curve models consider longitudinal change at the level of the person. While similar results may be obtained using either method, researchers may opt for one over the other due to the strengths and weaknesses associated with these methods. Examples of applications of both approaches to longitudinal studies of spirituality and religiousness are presented and discussed, along with design and data considerations when employing these modeling techniques.     

Relationships Among Selected Tests of Spatial Orientation Ability

The purpose of this study was to investigate the relationships between selected tests of spatial orientation ability. 2 tests which have been used as predictors of spatial ability (Guilford-Zimmerman Aptitude Survey, Parts V and VI) plus 2 newly developed tests designed to measure that ability were given to 202 Junior high school boys aged 11-15 yr. 3 of these tests were of the paper and pencil type while one was a physical performance (tumbling) test. The paper-and-pencil tests correlated significantly with each other (.62, .61, .44) but the physical performance test did not correlate significantly with any of the other tests.     

Models for the Detection of Deviations from the Expected Processing Strategy in Completing the Items of Cognitive Measures

This paper presents confirmatory factor models with fixed factor loadings that enable the identification of deviations from the expected processing strategy. The instructions usually define the expected processing strategy to a considerable degree. Simplification is a deviation from instructions that is likely to occur in complex cognitive measures. Since simplification impairs the validity of the measure, its identification is important. Models representing simplicity and instruction-based processing strategies were considered in investigating the data of 345 participants obtained by a working memory measure in order to find out whether and how the use of these strategies influences model-data fit. As expected, the consideration of simplicity strategies improved the model-data fit achieved for the instruction-based strategy.     

The Joint Multivariate Modeling of Multiple Mixed Response Sources: Relating Student Performances with Feedback Behavior

The present study concerns a Dutch computer-based assessment, which includes an assessment process about information literacy and a feedback process for students. The assessment is concerned with the measurement of skills in information literacy and the feedback process with item-based support to improve student learning. To analyze students’ feedback behavior (i.e. feedback use and attention time), test performance, and speed of working, a multivariate hierarchical latent variable model is proposed. The model can handle multivariate mixed responses from multiple sources related to different processes and comprehends multiple measurement components for responses and response times. A flexible within-subject latent variable structure is defined to explore multiple individual latent characteristics related to students’ test performance and feedback behavior. Main results of the computer-based assessment showed that feedback-information pages were less visited by well-performing students when they relate to easy items. Students’ attention paid to feedback was positively related to working speed but not to the propensity to use feedback.     

A novel method for assessing rival models of recognition memory

A general comparison is made between the multinomial processing tree (MPT) approach and a strength-based approach for modeling recognition memory measurement. Strength models include the signal-detection model and the dual-process model. Existing MPT models for recognition memory and a new generic MPT model, called the Multistate (MS) model, are contrasted with the strength models. Although the ROC curves for the MS model and strength model are similar, there is a critical difference between existing strength models and MPT models that goes beyond the assessment of the ROC. This difference concerns the question of stochastic mixtures for foil test trials. The hazard function and the reverse hazard function are powerful methods for detecting the presence of a probabilistic mixture. Several new theorems establish a novel method for obtaining information about the hazard function and reverse hazard function for the latent continuous distributions that are assumed in the strength approach to recognition memory. Evidence is provided that foil test trials involve a stochastic mixture. This finding occurred for both short-term memory procedures, such as the Brown–Peterson task, and long-term list-learning procedures, such as the paired-associate task. The effect of mixtures on foil trials is problematic for existing strength models but can be readily handled by MPT models such as the MS model. Other phenomena, such as the mirror effect and the effect of target-foil similarity, are also predicted accurately by the MPT modeling framework.