On the assumptions behind metacognitive measurements: Implications for theory and practice

Theory of visual confidence has largely been grounded in the gaussian signal detection framework. This framework is so dominant that people could be rather ignorant of idiosyncratic consequences from this distributional assumption. By contrasting gaussian and logistic signal detection models, this paper systematically evaluates the consequences of auxiliary distributional assumptions in the measurement of metacognitive accuracy and its theoretical implications. We found that these models can lead to opposing conclusions regarding the efficiency of confidence rating relative to objective decision (whether meta-d’ is larger or smaller than d’) as well as the metacognitive efficiency along the internal evidence continuum (whether meta-d’ is larger or smaller for higher levels of confidence). These demonstrations may call for reconsideration of hitherto established theories of metacognition that are critically dependent on auxiliary modeling assumptions. We deem there is no instant solution for this matter as our quantitative model comparisons on a large dataset did not decide on a clear victor between gaussian and logistic metacognitive models. Yet, being aware of the hidden modeling assumptions and their systematic consequences would facilitate cumulative development of the science of metacognition.

Principles of Model Specification in ANOVA Designs

ANOVA---the workhorse of experimental psychology--seems well understood in that behavioral sciences have agreed-upon contrasts and reporting conventions. Yet, we argue this consensus hides considerable flaws in common ANOVA procedures, and these flaws become especially salient in the within-subject and mixed-model cases. The main thesis is that these flaws are in model specification. The specifications underlying common use are deficient from a substantive perspective, that is, they do not match reality in behavioral experiments. The problem, in particular, is that specifications rely on coincidental rather than robust statements about reality. We provide specifications that avoid making arguments based on coincidences, and note these Bayes factor model comparisons among these specifications are already convenient in the BayesFactor package. Finally, we argue that model specification necessarily and critically reflects substantive concerns, and, consequently, is ultimately the responsibility of substantive researchers. Source code for this project is at github/PerceptionAndCognitionLab/stat_aov2

Behavioral Implications in COVID-19 Spread and Vaccinations

COVID-19 vaccinations have been administered quickly in the USA. However, a surprisingly large number of Americans are unwilling to get vaccinated. Without enough people getting vaccinated, the pandemic will not end. The longer the pandemic persists, the more opportunities exist for more virulent strains to emerge. This model looks at the effects of people’s behavior in containing and ending the COVID-19 pandemic in the USA. Human behavior adds several feedback loops to the standard SEIR model. Comparisons are made between cases with and without behavior loops, with reduced adherence to the recommended or mandated masks and social distancing, with and without the vaccine, and the effects of an early mask mandate termination. The results suggest human behavior must be accounted for in epidemiology models and that removing masks before enough vaccine are administered not only puts those vaccinated at risk, but allows the disease to readily spread again.

Association of interleukin 6 -174 G/C polymorphism with coronary artery disease and circulating IL-6 levels: a systematic review and meta-analysis

Introduction Circulating IL-6 levels and at least one polymorphic form of IL6 gene (IL6 -174 G/C, rs1800795) have been shown to be independently associated with coronary artery disease (CAD) by several investigators. Despite more than 12 published meta-analyses on this subject, association of -174 G/C with CAD, especially amongst distinct ancestral population groups remain unclear. We, therefore, conducted a systematic review and an updated meta-analysis to comprehensively ascertain the association of IL6 -174 G/C with CAD and circulating IL-6 levels. Materials and methods Relevant case–control/cohort studies investigating association of -174 G/C with CAD and circulating IL-6 levels were identified following a comprehensive online search. Association status for CAD was determined for the pooled sample, as well as separately for major ancestral subgroups. Association status for circulating IL-6 levels was assessed for the pooled sample, as well as separately for CAD cases and CAD free controls. Study-level odds ratios (OR) and 95% confidence intervals (CI) were pooled using random/fixed-effects model. Results Quantitative synthesis for the CAD endpoint was performed using 55 separate qualifying studies with a collective sample size of 51,213 (19,160 cases/32,053 controls). Pooled association of -174 G/C with CAD was found to be statistically significant through dominant (OR 1.15; 95% CI 1.05–1.25, p = 0.002) as well as allelic genetic model comparisons (OR 1.13, 95% CI 1.06–1.21, p = 0.0003). This effect was largely driven by Asian and Asian Indian ancestral subgroups, which also showed significant association with CAD in both genetic model comparisons (OR range 1.29–1.53, p value range ≤ 0.02). Other ancestral subgroups failed to show any meaningful association. Circulating IL-6 levels were found to be significantly higher amongst the ‘C’ allele carriers in the pooled sample (Standard mean difference, SMD 0.11, 95% CI 0.01–0.22 pg/ml, p = 0.009) as well as in the CAD free control subgroup (SMD 0.10, 95% CI 0.02–0.17 pg/ml, p = 0.009), though not in the CAD case subgroup (SMD 0.17, 95% CI = − 0.02 to 0.37, p = 0.12). Conclusions The present systematic review and meta-analysis demonstrate an overall association between IL6 -174 G/C polymorphism and CAD, which seems to be mainly driven by Asian and Asian Indian ancestral subgroups. Upregulation of plasma IL-6 levels in the ‘C’ allele carriers seems to be at least partly responsible for this observed association. This warrants further investigations with large, structured case–control studies especially amongst Asian and Asian Indian ancestral groups.

Bayes Factor Model Comparisons Across Parameter Values for Mixed Models

Nested data structures, in which conditions include multiple trials and are fully crossed with participants, are often analyzed using repeated-measures analysis of variance or mixed-effects models. Typically, researchers are interested in determining whether there is an effect of the experimental manipulation. These kinds of analyses have different appropriate specifications for the null and alternative models, and a discussion on which is to be preferred and when is sorely lacking. van Doorn et al. (2021) performed three types of Bayes factor model comparisons on a simulated data set in order to examine which model comparison is most suitable for quantifying evidence for or against the presence of an effect of the experimental manipulation. Here, we extend their results by simulating multiple data sets for various scenarios and by using different prior specifications. We demonstrate how three different Bayes factor model comparison types behave under changes in different parameters, and we make concrete recommendations on which model comparison is most appropriate for different scenarios.