Getting more from accuracy and response time data: Methods for fitting the linear ballistic accumulator
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Summary
This paper addresses the computational and mathematical barriers that limit the widespread application of cognitive process models in experimental psychology. While models like the Ratcliff diffusion model offer superior insights into decision-making by jointly analyzing response time (RT) and accuracy, their complexity often restricts usage to specialists. The authors introduce the Linear Ballistic Accumulator (LBA) model, a computationally tractable alternative that accounts for the same breadth of two-choice RT phenomena as the diffusion model but also extends to multiple-choice decisions. The primary goal is to provide accessible tools for estimating LBA parameters, thereby enabling researchers to move beyond standard ANOVA techniques and analyze the underlying cognitive processes of drift rate, response threshold, and nondecision time. To facilitate this, the authors provide three distinct methods for fitting the LBA to data: a Microsoft Excel worksheet, scripts for the statistical programming language R, and code for the Bayesian sampling software WinBUGS. The Excel method utilizes the Solver function to maximize the log-likelihood of the data, offering an intuitive interface with immediate graphical feedback on model fit. The R scripts implement Quantile Maximum Products Estimation (QMPE), which is robust for smaller sample sizes, as well as maximum-likelihood estimation. The authors demonstrate these methods using simulated data from a two-choice task with varying difficulty levels and a four-choice random-dot kinematogram task. The R code is designed to be flexible, allowing users to adapt the fitting procedures to complex experimental designs where parameters like drift rate or nondecision time vary across conditions. The paper validates these methods through simulation studies exploring parameter recovery and the effects of sample size. Results indicate that the provided tools successfully recover the true parameters used to generate the simulated data, with the LBA predictions closely matching the observed RT distributions for both correct and error responses. The Excel and R methods are shown to be effective for standard two-choice paradigms, while the R code is successfully extended to handle multiple-choice scenarios, accurately estimating drift rates for various response alternatives. The authors also provide scripts to generate graphical summaries, allowing users to visually assess the quality of the fit by comparing observed data histograms with model predictions. The significance of this work lies in its democratization of cognitive modeling. By providing user-friendly, open-source tools, the authors lower the technical threshold for applying sophisticated decision models to behavioral data. This allows experimental psychologists to gain deeper insights into the mechanisms of decision-making, such as distinguishing between changes in evidence accumulation rate and changes in decision caution. The availability of these methods encourages the broader adoption of process models over traditional statistical analyses, facilitating more precise interpretations of speed-accuracy trade-offs and cognitive processes in a wide range of experimental contexts.
Key finding
The authors provide and validate three accessible software implementations (Excel, R, and WinBUGS) that enable researchers to efficiently estimate parameters of the linear ballistic accumulator model from response time and accuracy data.
Methodology
simulation_modeling
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | canonical_url | — | — | 1 | 2026-06-04 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | failed | — | — | — | 4 | 2026-07-02 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Theoretical Contribution: computational model