DMCfun: Diffusion Model of Conflict (DMC) in Reaction Time Tasks
DOI: 10.32614/cran.package.dmcfun
archive: archived pipeline: cataloged verified
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Summary
The provided text is documentation for the R package `DMCfun`, which implements the Diffusion Model of Conflict (DMC) for analyzing reaction time and error rate data in conflict tasks. The package addresses the need for computational tools to fit the DMC model, originally introduced by Ulrich et al. (2015), which combines standard diffusion model features with superimposed controlled and automatic activation processes. This model explains distributional patterns in behavioral tasks such as the Flanker and Simon tasks. The software facilitates the simulation of theoretical data and the fitting of these models to observed empirical data, as detailed in Mackenzie and Dudschig (2021). The package provides a comprehensive suite of functions for data preparation, simulation, and model fitting. Users can create and manipulate data frames using functions like `createDF` and `addDataDF`, which generate simulated ex-Gaussian reaction time and binary error data. The core functionality involves calculating key metrics such as the Conditional Accuracy Function (CAF) via `calculateCAF` and distributional delta plots via `calculateDelta`. Model fitting is performed using `dmcFit` and `dmcFitDE`, which minimize the discrepancy between theoretical simulations and observed data. These functions support multiple optimization algorithms, including Nelder-Mead (`optim`) and differential evolution (`DEoptim`), and allow users to specify various cost functions, such as root-mean-square error (RMSE), squared percentage error (SPE), or likelihood-ratio chi-square statistics (GS). The fitting process estimates parameters related to automatic and controlled activation, drift rates, and non-decisional components, with options to fix parameters or fit multiple datasets simultaneously. The package includes built-in example datasets, such as `flankerData` and `simonData`, derived from Ulrich et al. (2015), allowing users to validate fits and explore model behavior. The documentation demonstrates how to configure starting values, parameter bounds, and grid searches to optimize fitting efficiency. It also provides extensive plotting and summary functions, such as `plot.dmcfit` and `summary.dmcfit`, to visualize the fit quality and inspect estimated parameters. The software is designed to handle complex experimental designs, supporting multiple subjects, trials, and compatibility conditions. The significance of `DMCfun` lies in its ability to provide researchers with a robust, accessible tool for applying the DMC model to cognitive psychology data. By implementing the DMC in R, the package enables detailed analysis of decision processes in conflict tasks, offering insights into the interplay between automatic and controlled stimulus processing. The availability of multiple fitting algorithms and cost functions enhances the flexibility and reliability of model estimation, supporting rigorous empirical research in cognitive modeling.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-17 |
| archive | success | unpaywall | — | — | 2 | 2026-06-25 |
| extract | success | cached | — | — | 2 | 2026-06-25 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| enrich | success | openalex | — | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-17 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-25 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-25; verification: verified.
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- Theoretical Contribution: computational model