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.     

On Learning to Move Randomly

In 2 experiments, the authors examined whether and to what degree young adults can learn to produce random planar motion of the index finger or fingers. Three different types of information feedback were provided to the participants (N = 8 in each experiment) over up to 5 days of practice across the 2 experiments. The results from both experiments revealed that the participants produced a relatively low level of movement randomness in finger motion and that they did not learn through practice to enhance the stochastic properties of their movement under any feedback conditions. The findings provide further evidence that there are relatively tight constraints on the number of dimensions that are regulating single-limb planar motion and that those constraints are not susceptible to change through typical learning protocols.     

Improving Psychological Measurement: Does It Make a Difference? A Comment on Nesselroade and Molenaar (2016)

Nesselroade and Molenaar advocate the use of an idiographic filter approach. This is a fixed-effects approach, which may limit the number of individuals that can be simultaneously modeled, and it is not clear how to model the presence of subpopulations. Most important, Nesselroade and Molenaar's proposal appears to be best suited for modeling long time series on a few variables for a few individuals. Long time series are not common in psychological applications. Can it be applied to the usual longitudinal data we face? These are characterized by short time series (four to five points in time), hundreds of individuals, and dozens of variables. If so, what do we gain? Applied settings most often involve between-individual decisions. I conjecture that their approach will not outperform common, simpler, methods. However, when intraindividual decisions are involved, their approach may have an edge.     

Stimulating performance: A scoping review on transcranial electrical stimulation effects on olympic sports

Transcranial electrical stimulation (tES) is widely used to explore the role of various cortical regions involved in a multitude of motor and cognitive processes. Recently, tES has been discussed as being able to potentially enhance performance in sports and even been suggested as a potential way of boosting performance in competitions. In this scoping review, we systematically investigate the literature on tES-induced performance modulations on Olympic sports. The aim is to create a critical overview of the emerging field of tES-enhanced sports performance. Further, methodological approaches, research desiderata, and potential directions for future research will be identified. The final dataset shows a large variation in methodology and a lack of research into the area. Further, partially sub-optimal choices in study design, methodology, and lacking consistency in reporting procedures may impede valid conclusions and obscured the effects of tES on Olympic sports. Consequently, we outline future directions and areas to improve research.     

Manipulating attentional load in sequence learning through random number generation

Implicit learning is often assumed to be an effortless process. However, some artificial grammar learning and sequence learning studies using dual tasks seem to suggest that attention is essential for implicit learning to occur. This discrepancy probably results from the specific type of secondary task that is used. Different secondary tasks may engage attentional resources differently and therefore may bias performance on the primary task in different ways. Here, we used a random number generation (RNG) task, which may allow for a closer monitoring of a participant's engagement in a secondary task than the popular secondary task in sequence learning studies: tone counting (TC). In the first two experiments, we investigated the interference associated with performing RNG concurrently with a serial reaction time (SRT) task. In a third experiment, we compared the effects of RNG and TC. In all three experiments, we directly evaluated participants' knowledge of the sequence with a subsequent sequence generation task. Sequence learning was consistently observed in all experiments, but was impaired under dual-task conditions. Most importantly, our data suggest that RNG is more demanding and impairs learning to a greater extent than TC. Nevertheless, we failed to observe effects of the secondary task in subsequent sequence generation. Our studies indicate that RNG is a promising task to explore the involvement of attention in the SRT task.     

Finite Mixtures of Hidden Markov Models for Longitudinal Responses Subject to Drop out

Abstract

Drop out is a typical issue in longitudinal studies. When the missingness is non-ignorable, inference based on the observed data only may be biased. This paper is motivated by the Leiden 85+ study, a longitudinal study conducted to analyze the dynamics of cognitive functioning in the elderly. We account for dependence between longitudinal responses from the same subject using time-varying random effects associated with a heterogeneous hidden Markov chain. As several participants in the study drop out prematurely, we introduce a further random effect model to describe the missing data mechanism. The potential dependence between the random effects in the two equations (and, therefore, between the two processes) is introduced through a joint distribution specified via a latent structure approach. The application of the proposal to data from the Leiden 85+ study shows its effectiveness in modeling heterogeneous longitudinal patterns, possibly influenced by the missing data process. Results from a sensitivity analysis show the robustness of the estimates with respect to misspecification of the missing data mechanism. A simulation study provides evidence for the reliability of the inferential conclusions drawn from the analysis of the Leiden 85+ data.     

Estimating Complex Measurement and Growth Models Using the R Package PLmixed

Measurement models, such as factor analysis and item response theory models, are commonly implemented within educational, psychological, and behavioral science research to mitigate the negative effects of measurement error. These models can be formulated as an extension of generalized linear mixed models within a unifying framework that encompasses various kinds of multilevel models and longitudinal models, such as partially nonlinear latent growth models. We introduce the R package PLmixed, which implements profile maximum likelihood estimation to estimate complex measurement and growth models that can be formulated within the general modeling framework using the existing R package lme4 and function optim. We demonstrate the use of PLmixed through two examples before concluding with a brief overview of other possible models.