Evidence map›Paper›PMID 23646991›Full record

ArticlePsychological methods2013

A method for efficiently sampling from distributions with correlated dimensions.

Brandon M Turner, Per B Sederberg, Scott D Brown, Mark Steyvers

Abstract read
In one paragraph

Article in Psychological methods, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
106citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

106 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Review
  7. Model-based analysis of stop-signal data reveals robust neural and clinical correlates of evidence accumulation but not inhibition.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
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46 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Brandon M TurnerDepartment of Psychology.
Per B Sederberg
Scott D Brown
Mark Steyvers

Funding

Analyzing Complex Stochastic Models using Approximate Bayesian ComputationF32GM103288 · NIGMS · STANFORD UNIVERSITY · PI TURNER, BRANDON · 2012 to 2013
$101k
NIGMS NIH HHS F32 GM103288NIGMS NIH HHS F32GM103288
6 · The paper itself

Abstract

Bayesian estimation has played a pivotal role in the understanding of individual differences. However, for many models in psychology, Bayesian estimation of model parameters can be difficult. One reason for this difficulty is that conventional sampling algorithms, such as Markov chain Monte Carlo (MCMC), can be inefficient and impractical when little is known about the target distribution--particularly the target distribution's covariance structure. In this article, we highlight some reasons for this inefficiency and advocate the use of a population MCMC algorithm, called differential evolution Markov chain Monte Carlo (DE-MCMC), as a means of efficient proposal generation. We demonstrate in a simulation study that the performance of the DE-MCMC algorithm is unaffected by the correlation of the target distribution, whereas conventional MCMC performs substantially worse as the correlation increases. We then show that the DE-MCMC algorithm can be used to efficiently fit a hierarchical version of the linear ballistic accumulator model to response time data, which has proven to be a difficult task when conventional MCMC is used.

Indexed as

AlgorithmsBayes TheoremMonte Carlo MethodPsychometricsHumansLinear ModelsSampling Studies

Identifiers

PMID23646991
PMCPMC4140408

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.