Testing the bottleneck account for post-error slowing beyond the post-error response

The bottleneck account for post-error slowing assumes that cognitive resources are depleted after errors and thus the processing of subsequent events is delayed. To test this, we used a novel speeded-choice task and recorded behavioral measures and ERP (event-related potential) components on five trials following either an erroneous or correct response. We found that participants were slower and less accurate immediately after making an error and that this reduction of performance decayed on the following trials. Moreover, post-correct versus post-error differences in both the visual N1 and the P3 component were found. However, the difference in the P3 component rapidly diminished over time, whereas the differences in the N1 component were still evident in the fourth trial following the erroneous response. The results lay further support to the bottleneck account for post-error slowing and show a combination of early attentional and higher-order processing changes that occur after erroneous responses

datawizard: an R package for easy data preparation and statistical transformations

The {datawizard} package for the R programming language (R Core Team, 2021) provides a lightweight toolbox to assist in key steps involved in any data analysis workflow: (1) wrangling the raw data to get it in the needed form, (2) applying preprocessing steps and statistical transformations, and (3) compute statistical summaries of data properties and distributions. Therefore, it can be a valuable tool for R users and developers looking for a lightweight option for data preparation

Visual-Spatial Abilities Are NOT Related to the Speed of Mental Rotation

Individuals’ reaction time (RT) slopes in tasks of mental rotation have been found to be related to other measure of visual-spatial abilities, and thus are viewed as a psychometric measure of visual-spatial abilities. The common interpretation of individual RT slopes is as a measure of the speed at which the rotation is carried out. However, EEG studies have found that the process of mental rotation continues after response selection has been carried out, casting doubt on the interpretation of RT slopes as measures of the speed of mental rotation. This study made use of EEG techniques to directly capture individual differences in the speed of mental rotation and assess their association with visual-spatial abilities, revealing that individual differences in mental rotation speed are not related to individual differences in RT slopes. Additionally, a computation model supports an alternative explanation by which RT slopes reflect individual differences in differential tolerances for stimulus identification within mental rotation tasks

RE: Bayesian Benefits for the Pragmatic Researcher

A response to Wagenmakers, Morey & Lee's analysis of the South Park Hypothesis regarding Adam Sandler movies

Extracting, Computing and Exploring the Parameters of Statistical Models using R

The recent growth of data science is partly fueled by the ever-growing amount of data and the joint important developments in statistical modeling, with new and powerful models and frameworks becoming accessible to users. Although there exist some generic functions to obtain model summaries and parameters, many package specific modeling functions do not provide such methods to allow users to access such valuable information

Methods and Algorithms for Correlation Analysis in R

Correlations tests are arguably one of the most commonly used statistical procedures, and are used as a basis in many applications such as exploratory data analysis, structural modelling, data engineering etc. In this context, we present correlation, a toolbox for the R language and part of the easystats collection, focused on correlation analysis. Its goal is to be lightweight, easy to use, and allows for the computation of many different kinds of correlations

performance: An R Package for Assessment, Comparison and Testing of Statistical Models

A crucial part of statistical analysis is evaluating a model's quality and fit, or performance. During analysis, especially with regression models, investigating the fit of models to data also often involves selecting the best fitting model amongst many competing models. Upon investigation, fit indices should also be reported both visually and numerically to bring readers in on the investigative effort. While functions to build and produce diagnostic plots or to compute fit statistics exist, these are located across many packages, which results in a lack of a unique and consistent approach to assess the performance of many types of models. The result is a difficult-to-navigate, unorganized ecosystem of individual packages with different syntax, making it onerous for researchers to locate and use fit indices relevant for their unique purposes. The performance package in R fills this gap by offering researchers a suite of intuitive functions with consistent syntax for computing, building, and presenting regression model fit statistics and visualizations

Indices of Effect Existence and Significance in the Bayesian Framework

Turmoil has engulfed psychological science. Causes and consequences of the reproducibility crisis are in dispute. With the hope of addressing some of its aspects, Bayesian methods are gaining increasing attention in psychological science. Some of their advantages, as opposed to the frequentist framework, are the ability to describe parameters in probabilistic terms and explicitly incorporate prior knowledge about them into the model. These issues are crucial in particular regarding the current debate about statistical significance. Bayesian methods are not necessarily the only remedy against incorrect interpretations or wrong conclusions, but there is an increasing agreement that they are one of the keys to avoid such fallacies. Nevertheless, its flexible nature is its power and weakness, for there is no agreement about what indices of “significance” should be computed or reported. This lack of a consensual index or guidelines, such as the frequentist p-value, further contributes to the unnecessary opacity that many non-familiar readers perceive in Bayesian statistics. Thus, this study describes and compares several Bayesian indices, provide intuitive visual representation of their “behavior” in relationship with common sources of variance such as sample size, magnitude of effects and also frequentist significance. The results contribute to the development of an intuitive understanding of the values that researchers report, allowing to draw sensible recommendations for Bayesian statistics description, critical for the standardization of scientific reporting

Indices of effect existence and significance in the Bayesian framework

Turmoil has engulfed psychological science. Causes and consequences of the reproducibility crisis are in dispute. With the hope of addressing some of its aspects, Bayesian methods are gaining increasing attention in psychological science. Some of their advantages, as opposed to the frequentist framework, are the ability to describe parameters in probabilistic terms and explicitly incorporate prior knowledge about them into the model. These issues are crucial in particular regarding the current debate about statistical significance. Bayesian methods are not necessarily the only remedy against incorrect interpretations or wrong conclusions, but there is an increasing agreement that they are one of the keys to avoid such fallacies. Nevertheless, its flexible nature is its power and weakness, for there is no agreement about what indices of “significance” should be computed or reported. This lack of a consensual index or guidelines, such as the frequentist p-value, further contributes to the unnecessary opacity that many non-familiar readers perceive in Bayesian statistics. Thus, this study describes and compares several Bayesian indices, provide intuitive visual representation of their “behavior” in relationship with common sources of variance such as sample size, magnitude of effects and also frequentist significance. The results contribute to the development of an intuitive understanding of the values that researchers report, allowing to draw sensible recommendations for Bayesian statistics description, critical for the standardization of scientific reporting.Published versio

Phi, Fei, Fo, Fum: Effect Sizes for Categorical Data that Use the Chi-Squared Statistic

R Code to reproduce table and results from this paper