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Intuitive metacognitive processes such as metaperception surprisingly have a lot moreinfluence on the intersectionality between interpersonal relations and health than one thinks. Of the many forms of metaperception, this paper aims to further explore the overblown implications effect (OIE), which is a prevalent metaperception error where one overestimates how much other people think of their successes or failures. We conducted a mixed ANOVA analysis and found further support for the statistically significant discrepancy between actors’ metaperception ratings and observers’ social perception ratings. For the extension, we continue to explore whether a longer rating duration for the actors would result in more accurate predictions of observers’ ratings compared to observers’ actual ratings by running a mixed ANOVA analysis. We hypothesized that increasing the rating duration would decrease the difference between the actors’ metaperception ratings and the observers’ social perception ratings, but our results show that there is no statistical significance between the two variables. However, it is important to note that we could not use the whole data provided due to statistical limitations. Hence, this makes it even more essential that future studies work with more representative samples to further understand the correlative factors that influence the OIE.

Background: The aim of this study is to investigate the effect of Acceptance and Commitment Therapy (ACT) on cognitive fatigue in female patients with multiple sclerosis (MS). Methods: This was a quasi-experimental study with a pre-test and post-test design and three months of follow up performed on three groups of therapy, placebo, and waiting list. Thus, among the female patients with MS referred to the MS Society of Ahwaz, Iran, 41 patients were chosen with the convenience sampling method and divided into therapy (n = 13), placebo (n = 13), and waiting list (n = 14) by random sampling. Findings: All patients completed the Modified Impact of Fatigue Scale (MFIS) in the pretest and posttest stages and after the three-month follow-up period. The results of the mixed analysis of variance (ANOVA) showed that the interaction between time and therapy can reduce the cognitive fatigue in patients with MS (F = 3.53, P = 0.014, effect size = 0.16, statistical power = 0.84) and this reduction will remain and until the follow-up. Conclusion: According to fatigue as a main and pervasive complaint of this group, the results of this study can be used as an appropriate method to reduce cognitive fatigue in patients with MS.

• Correcting for assessor differences can improve estimation of product effects. • ANOVA F-test for some correction methods produces too optimistic p-values. • Permutation approach enables controlling the type I error (false positive) rate. • Mixed assessor model (MAM) is most powerful in detecting product differences. • Unequal reproducibility between assessors affects MAM product effect significance. In sensory data analysis, assessor-dependent scaling effects may hinder the analysis of product differences. Romano et al. (2008) compared several approaches to reduce scaling differences between assessors by their ability to maximise the product effect F-values in a mixed ANOVA analysis. Their study on a sensory dataset of 14 cheese samples assessed by twelve assessors on a continuous scale showed that some of these approaches apparently improved the F-value of the product effect. However, this direct comparison is only legitimate if these F-values originate from the same null distribution. To obtain the null distributions of the different correction methods, we employed a permutation approach on the same cheese dataset also used by Romano et al. (2008) and a random noise simulation approach. Based on the empirically obtained null distributions, we calculated the corrected product effect significance to directly compare the performance of the preprocessing methods. Our results show that the null distributions of some preprocessing methods do not correspond to the expected F-distribution. In particular for the ten Berge method, the null distribution is shifted towards higher F-values. Therefore, an observed increase of the product effect F-value, as compared to the F-value on raw data, does not necessarily lead to increased product effect significance. If p-values are calculated based on such inflated F-values, significance may thus be overestimated. In contrast, calculation of p-values directly from the empirical null distributions obtained by permutation provides a common ground to properly compare method performance. Moreover, we show that differences in reproducibility between assessors, as they exist in real-world sensory datasets, may lead to overestimation of product effect significance by the mixed assessor model (MAM). [ABSTRACT FROM AUTHOR]

This study aimed at testing the efficacy of an integrative intervention to prevent adolescent problem gambling acting on a multidimensional set of factors including gambling related knowledge and misconceptions, economic perception of gambling, and superstitious thinking. A pre- and post-test design was performed with 181 Italian adolescents (64 % boys; Mean age = 15.95) randomly assigned to two groups (Training and No Training). Results revealed that the intervention was effective in improving correct knowledge about gambling and reducing misconceptions, perception of gambling's profitability, and superstitious thinking. Except for misconceptions, these effects were obtained both in participants who were classified as Non-problem and At-Risk/Problem gamblers at the beginning of the intervention. Findings attested also that the training effects were stable over time, and that some changes in gambling behavior were produced. Findings were discussed referring to indications for future research aiming at confirming and extending the present results. [ABSTRACT FROM AUTHOR]