Linear Deterministic Accumulator Models of Simple Choice
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Summary
This paper addresses the modeling of simple choice behavior, specifically focusing on deterministic race models where between-choice noise dominates over within-choice noise. The authors, Heathcote and Love, aim to develop a computationally tractable model within this class that improves upon existing frameworks like the Linear Ballistic Accumulator (LBA). The motivation stems from the need for models that are mathematically simple yet capable of accurately describing response time (RT) distributions and choice frequencies in tasks such as lexical decision. The study introduces the Lognormal Race (LNR) model, a new exemplar of linear deterministic accumulation. Unlike the LBA, which assumes a uniform distribution for start-point noise and a normal distribution for rate noise, the LNR assumes that the logarithm of the accumulation rate follows a normal distribution, resulting in a Lognormal distribution for the rate itself. This assumption ensures that all accumulation rates are positive, eliminating the possibility of non-responses that can occur in the LBA. The LNR is derived from the work of Ulrich and Miller (1993) on "continuous flow" systems, where the product of independent Lognormal variables remains Lognormal, allowing for tractable likelihood calculations even when accumulator inputs are correlated. The authors test the LNR against the LBA by fitting both models to behavioral data from a lexical-decision task reported by Wagenmakers et al. (2008), which included conditions emphasizing either speed or accuracy. The fitting process explored various parameterizations to explain speed-accuracy trade-offs, including changes in start-point boundaries, rate means, and rate variability. The results indicate that the LNR provides an accurate description of the frequency of each choice and its associated RT distribution in the lexical-decision data. While the LBA model provided a slightly better statistical fit, both models supported similar psychological conclusions regarding the mechanisms of choice. Specifically, both models demonstrated that speed-accuracy trade-offs can be explained not just by changes in response caution (boundary height), but also by shifts in evidence accumulation rates or start-point biases. The LNR’s Lognormal assumption allows it to capture the positive skew of RT distributions effectively, matching the performance of the ExGaussian distribution, a common descriptive model. The paper also discusses the implications of the LNR’s structure, noting that it can accommodate correlations between accumulator inputs without significant computational cost, a limitation of the standard independent race equation used in the LBA. The significance of this work lies in the development of a more flexible and computationally efficient framework for modeling simple choices. By establishing the LNR as a viable alternative to the LBA, the authors provide researchers with a tool that is easier to apply and extend to more complex scenarios, such as contingent choices or correlated inputs. The findings reinforce the utility of deterministic, between-choice noise models in explaining human decision-making, offering a robust method for analyzing speed-accuracy trade-offs in cognitive psychology and neuroscience.
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 | 2026-08-22 |
| extract | success | cached | — | — | 3 | 2026-08-23 |
| 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 | success | — | — | — | 1 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.8-27b-gittensor | summ-v5 | 2 | 2026-08-23 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
Summary generated by qwen3.8-27b-gittensor on 2026-08-23; verification: pending re-verification.
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