ArticleBehavior research methods2024
The Seven-parameter Diffusion Model: an Implementation in Stan for Bayesian Analyses.
Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Dependent latent class partial credit models for careless and insufficient effort responding in online survey data.Behavior research methods · 2026Article
- dynConfiR: An R package for sequential sampling models of decision confidence.Behavior research methods · 2026Article
- Associative and categorical priming in a word-picture paradigm: a diffusion model analysis.Psychological research · 2026Article
- Modeling truncated and censored data with the diffusion model in Stan.Behavior research methods · 2026Article
- Bayesian hierarchical cognitive modeling with the EMC2 package.Behavior research methods · 2026Article
- Badminton expertise shapes cue extraction and evidence accumulation during perceptual prediction.Frontiers in psychology · 2026Article
- Lévy Versus Wiener: Assessing the Effects of Model Misspecification on Diffusion Model Parameters.Computational brain & behavior · 2025Article
- Individual differences in speed-accuracy trade-off influence social decision-making in dyads.Proceedings. Biological sciences · 2025Article
- How Implicit Sequence Learning and Explicit Sequence Knowledge Are Expressed in a Serial Response Time Task.Journal of cognition · 2025Article
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5 authors.
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Abstract
Diffusion models have been widely used to obtain information about cognitive processes from the analysis of responses and response-time data in two-alternative forced-choice tasks. We present an implementation of the seven-parameter diffusion model, incorporating inter-trial variabilities in drift rate, non-decision time, and relative starting point, in the probabilistic programming language Stan. Stan is a free, open-source software that gives the user much flexibility in defining model properties such as the choice of priors and the model structure in a Bayesian framework. We explain the implementation of the new function and how it is used in Stan. We then evaluate its performance in a simulation study that addresses both parameter recovery and simulation-based calibration. The recovery study shows generally good recovery of the model parameters in line with previous findings. The simulation-based calibration study validates the Bayesian algorithm as implemented in Stan.
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