A default Bayesian hypothesis test for correlations and partial correlations

Wetzels, Ruud; Wagenmakers, Eric‐Jan

doi:10.3758/s13423-012-0295-x

Cited by 471 publications

(454 citation statements)

References 30 publications

Supporting

Mentioning

435

Contrasting

Unclassified

Order By: Relevance

“…Adapted from Wetzels and Wagenmakers (2012) We found decisive evidence for an association of gender with baseline cognitive performance (b = 0.45, p < .001, BF H1 > 100), indicating that male individuals started training at a higher level of performance. Further, there was substantial evidence that age was negatively associated with baseline cognitive performance (b = − 0.32, p = .002, BF H1 = 5.69), indicating that within the older age group, younger individuals showed higher baseline cognitive performance.…”

Section: Individual Differences Predicting Baseline Cognitivementioning

confidence: 85%

“…BFs range from 0 to infinity, with higher values indicating stronger evidence for the alternative model. BFs are evaluated according to an adapted version of Wetzels and Wagenmakers (2012) to facilitate verbal interpretation (see Table 3). For example, a BF H1 of 3 indicates that the data is three times more likely to occur under the alternative hypothesis.…”

Section: Bayes Factorsmentioning

confidence: 99%

See 1 more Smart Citation

Do Individual Differences Predict Change in Cognitive Training Performance? A Latent Growth Curve Modeling Approach

2017

View full text Add to dashboard Cite

Cognitive training interventions have become increasingly popular as a potential means to cost-efficiently stabilize or enhance cognitive functioning across the lifespan. Large training improvements have been consistently reported on the group level, with, however, large differences on the individual level. Identifying the factors contributing to these individual differences could allow for developing individually tailored interventions to boost training gains. In this study, we therefore examined a range of individual differences variables that had been discussed in the literature to potentially predict training performance. To estimate and predict individual differences in the training trajectories, we applied Latent Growth Curve models to existing data from three working memory training interventions with younger and older adults. However, we found that individual differences in demographic variables, real-world cognition, motivation, cognitionrelated beliefs, personality, leisure activities, and computer literacy and training experience were largely unrelated to change in training performance. Solely baseline cognitive performance was substantially related to change in training performance and particularly so in young adults, with individuals with higher baseline performance showing the largest gains. Thus, our results conform to magnification accounts of cognitive change.Keywords Working memory training . Individual differences . Latent growth curve modeling Over the past decade, there has been an exploding interest in computer-based commercial Bbrain training^programs and in scientific evidence relating to the effectiveness of such interventions, triggered by promising results of working memory (WM) training gains generalizing to previously untrained cognitive abilities such as intelligence in both younger (e.g., Jaeggi et al. 2008) and older adults (e.g., Borella et al. 2010). Although the idea of improving general cognitive functioning within a few weeks is enticing, there is also accumulating evidence against a generalized effect of WM training (e.g., Clark et al. 2017;De Simoni and von Bastian 2017;Guye and von Bastian 2017;Sprenger et al. 2013). Even on the meta-analytic level, evidence is mixed regarding the effectiveness of cognitive training in both younger and older adults (e.g., Au et al. 2015;Dougherty et al. 2016;Karbach and Verhaeghen 2014;Kelly et al. 2014;Lampit et al. 2014;Melby-Lervåg and Hulme 2013;Melby-Lervåg et al. 2016;Schwaighofer et al. 2015;Soveri et al. 2017). Aside from design and methodological choices potentially explaining the diverging findings (e.g., Noack et al. 2009;Shipstead et al. 2012), many authors increasingly articulated the potentially important influence of individual differences on cognitive training trajectories and outcomes (e.g., Buitenweg et al. 2012;Guye et al. 2016;Könen and Karbach 2015;Shah et al. 2012;von Bastian and Oberauer 2014 Individual differences in cognitive functioning (e.g., Ackerman and Lohman 2006) and learning potential (e.g., Stern ...

show abstract

Section: Individual Differences Predicting Baseline Cognitivementioning

confidence: 85%

Section: Bayes Factorsmentioning

confidence: 99%

Do Individual Differences Predict Change in Cognitive Training Performance? A Latent Growth Curve Modeling Approach

2017

View full text Add to dashboard Cite

show abstract

“…Over time, participants made increasingly better choices (see Figure 2a), as indicated by a significant correlation between the average score (over participants) and trial number, r = 0.74, p < 0.01. Using a Bayesian test for correlations (Wetzels & Wagenmakers, 2012), we found a Bayes factor of BF 10 = 6.01 when comparing the correlation to a mean of 0. 27 out 47 participants had a significantly positive correlation between trial numbers and score at α = 0.05.…”

Section: Resultsmentioning

confidence: 99%

Putting bandits into context: How function learning supports decision making.

Schulz¹,

Konstantinidis

Speekenbrink³

2018

Journal of Experimental Psychology: Learning, Memory, and Cogni

View full text Add to dashboard Cite

We introduce the contextual multi-armed bandit task as a framework to investigate learning and decision making in uncertain environments. In this novel paradigm, participants repeatedly choose between multiple options in order to maximise their rewards. The options are described by a number of contextual features which are predictive of the rewards through initially unknown functions. From their experience with choosing options and observing the consequences of their decisions, participants can learn about the functional relation between contexts and rewards and improve their decision strategy over time. In three experiments, we explore participants' behaviour in such learning environments. We predict participants' behaviour by context-blind (mean-tracking, Kalman filter) and contextual (Gaussian process and linear regression) learning approaches combined with different choice strategies. Participants are mostly able to learn about the context-reward functions and their behaviour is best described by a Gaussian process learning strategy which generalizes previous experience to similar instances. In a relatively simple task with binary features, they seem to combine this learning with a "probability of improvement" decision strategy which focuses on alternatives that are expected to lead to an improvement upon a current favourite option. In a task with continuous features that are linearly related to the rewards, participants seem to more explicitly balance exploration and exploitation. Finally, in a difficult learning environment where the relation between features and rewards is non-linear, some participants are again well-described by a Gaussian process learning strategy, whereas others revert to context-blind strategies.

show abstract

“…Liang, Paulo, Molina, Clyde, & Berger, 2008;Overstall & Forster, 2010). It underlies most Bayes factor development in psychology for regression and ANOVA models Rouder, Morey, Speckman, & Province, 2012;Rouder, Speckman, Sun, Morey, & Iverson, 2009;Wetzels & Wagenmakers, 2012). In this context, we follow the ANOVA development by as follows:…”

Section: Additional Specificationsmentioning

confidence: 99%

Developing constraint in bayesian mixed models.

Haaf¹,

Rouder²

2017

Psychological Methods

117

View full text Add to dashboard Cite

Model comparison in Bayesian mixed models is becoming popular in psychological science. Here we develop a set of nested models that account for order restrictions across individuals in psychological tasks. An order-restricted model addresses the question "Does everybody," as in "Does everybody show the usual Stroop effect," or "Does everybody respond more quickly to intense noises than subtle ones?" The crux of the modeling is the instantiation of 10s or 100s of order restrictions simultaneously, one for each participant. To our knowledge, the problem is intractable in frequentist contexts but relatively straightforward in Bayesian ones. We develop a Bayes factor model-comparison strategy using Zellner and Siow's default g-priors appropriate for assessing whether effects obey equality and order restrictions. We apply the methodology to seven data sets from Stroop, Simon, and Eriksen interference tasks. Not too surprisingly, we find that everybody Stroops-that is, for all people congruent colors are truly named more quickly than incongruent ones. But, perhaps surprisingly, we find these order constraints are violated for some people in the Simon task, that is, for these people spatially incongruent responses occur truly more quickly than congruent ones! Implications of the modeling and conjectures about the task-related differences are discussed.

show abstract

A default Bayesian hypothesis test for correlations and partial correlations

Cited by 471 publications

References 30 publications

Do Individual Differences Predict Change in Cognitive Training Performance? A Latent Growth Curve Modeling Approach

Do Individual Differences Predict Change in Cognitive Training Performance? A Latent Growth Curve Modeling Approach

Putting bandits into context: How function learning supports decision making.

Developing constraint in bayesian mixed models.

Contact Info

Product

Resources

About