Bayesian benefits with JASP

Marsman, Maarten; Wagenmakers, Eric‐Jan

doi:10.1080/17405629.2016.1259614

Cited by 249 publications

(219 citation statements)

References 36 publications

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“…In other words, we expected to find support for the null hypothesis, and continued data collection until a BF in support of the null indicated strong evidence for no difference between‐groups (BF H 01 = 6). The use of a BF stopping rule allows the researcher to continue collecting data until a cutoff BF is achieved that signifies the evidence in favor of an alternative or null hypothesis is strong (Marsman & Wagenmakers, ). This means that excess data will not be collected, and the strength of the confidence in favor of the hypothesis can be quantified.…”

Section: Resultsmentioning

confidence: 99%

“…This left a total of 184 participants for analysis: 90 in the letter training group (64 females; M age = 22.97 years; SD age = 3.99) and 94 in the number training group (60 females; M age = 22.63 years; SD age = 3.34). The sample size was calculated using a Bayesian stopping point described below in the results section (Marsman & Wagenmakers, ).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A comes before B, like 1 comes before 2. Is the parietal cortex sensitive to ordinal relationships in both numbers and letters? An fMRI‐adaptation study

Goffin

Vogel

Slipenkyj

et al. 2019

Human Brain Mapping

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How are number symbols (e.g., Arabic digits) represented in the brain? Functional resonance imaging adaptation (fMRI‐A) research has indicated that the intraparietal sulcus (IPS) exhibits a decrease in activation with the repeated presentation of the same number, that is followed by a rebound effect with the presentation of a new number. This rebound effect is modulated by the numerical ratio or difference between presented numbers. It has been suggested that this ratio‐dependent rebound effect is reflective of a link between the symbolic numerical representation system and an approximate magnitude system. Experiment 1 used fMRI‐A to investigate an alternative hypothesis: that the rebound effect observed in the IPS is related to the ordinal relationships between symbols (e.g., 3 comes before 4; C after B). In Experiment 1, adult participants exhibited the predicted distance‐dependent parametric rebound effect bilaterally in the IPS for number symbols during a number adaptation task, however, the same effect was not found anywhere in the brain in response to letters. When numbers were contrasted with letters (numbers > letters), the left intraparietal lobule remained significant. Experiment 2 demonstrated that letter stimuli used in Experiment 1 generated a behavioral distance effect during an active ordinality task, despite the lack of a neural distance effect using fMRI‐A. The current study does not support the hypothesis that general ordinal mechanisms underpin the neural parametric recovery effect in the IPS in response to number symbols. Additional research is needed to further our understanding of mechanisms underlying symbolic numerical representation in the brain.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A comes before B, like 1 comes before 2. Is the parietal cortex sensitive to ordinal relationships in both numbers and letters? An fMRI‐adaptation study

Goffin

Vogel

Slipenkyj

et al. 2019

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…JASP (Version 0.9) and report Bayes Factors (BF) for each of our analyses. We calculated BF relying on default priors as recommended by Marsman and Wagenmakers () and Wagenmakers et al. ().…”

Section: Methodsmentioning

confidence: 99%

The malleability of executive function in early childhood: Effects of schooling and targeted training

Zhang

Wang

Zhao³

et al. 2018

Developmental Science

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Executive function (EF), its importance for scholastic achievement and the question of whether or not EF is malleable, have become a topic of intense interest. Education or schooling is often seen as effective approaches to enhance EF due to the specific school-related requirements as compared to kindergarten or pre-school. However, no study to date has investigated whether targeted training focusing on those domains might be comparable with regular schooling in improving EF and fluid intelligence (Gf). The aim of the present study was to replicate and extend the previously demonstrated schooling effects on EF by using a school-cutoff design, and to further investigate whether a theoretically motivated intervention targeting specific EF, i.e., working memory (WM) or inhibitory control (IC), could achieve comparable effects with schooling in both, WM and IC, as well as Gf. 91 6-year-old kindergarteners and first-graders with similar chronological age participated the study. We compared the performance of a first-grade schooling group with that of two kindergarten training groups as well as a business-as-usual kindergarten control group. Participants were assessed in WM, IC and Gf at baseline, immediately after the intervention (posttest), as well as 3 months after training completion (follow-up). The results showed that the schooling group indeed outperformed the kindergarten groups at baseline in several cognitive tasks. Furthermore, both the WM and IC training showed pronounced gains in the trained tasks, as well as varying degrees of improvement in non-trained outcome measures. Most importantly, both training groups achieved comparable performance with the schooling group, which was especially apparent in Gf at follow-up. Our findings provide further evidence for the malleability of EF demonstrating that both, long-term and short-term interventions can facilitate the acquisition of those important skills, and as such, our work has important implications for educational practice.

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“…The Bayesian Pearson correlation coefficient was calculated to be 0.46, the same as the classical value found previously. The other results may look strange to researchers accustomed to traditional hypothesis testing, but they succinctly summarize a great deal of information, as described to follow [5].…”

Section: Bayesian Analysismentioning

confidence: 99%

An Introduction to Bayesian Data Analysis for Correlations

Nuzzo

2017

PM&R

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Bayesian benefits with JASP

Cited by 249 publications

References 36 publications

A comes before B, like 1 comes before 2. Is the parietal cortex sensitive to ordinal relationships in both numbers and letters? An fMRI‐adaptation study

A comes before B, like 1 comes before 2. Is the parietal cortex sensitive to ordinal relationships in both numbers and letters? An fMRI‐adaptation study

The malleability of executive function in early childhood: Effects of schooling and targeted training

An Introduction to Bayesian Data Analysis for Correlations

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