Evidence accumulation modelling in the wild: understanding safety-critical decisions
DOI: 10.1016/j.tics.2022.11.009
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Summary
This review article addresses the application of evidence accumulation models (EAMs) to safety-critical decision-making in complex, real-world environments, a shift from their traditional use in simple, controlled laboratory tasks. The authors argue that while EAMs have successfully explained latent cognitive processes in basic psychology, their utility in applied Human Factors research remains underutilized. The paper aims to demonstrate how EAMs provide a coherent theoretical framework for understanding how human operators adapt to task demands, such as automation, time pressure, and cognitive load, in domains like air-traffic control, driving, maritime surveillance, and medical diagnosis. The authors review recent empirical studies that employ EAMs, specifically the diffusion decision model (DDM) and linear ballistic accumulator (LBA), to analyze response times and choices in representative simulations of these work tasks. The methodology involves decomposing performance into psychologically meaningful parameters: accumulation rate (information processing efficiency and attention), threshold height (response caution), bias, and nondecision time (encoding and motor processes). By fitting these models to data from tasks with longer timescales (e.g., 2–10 seconds), the authors assess whether standard single-process EAMs remain valid for naturalistic decisions. They also examine how operators allocate cognitive resources when facing concurrent tasks, such as prospective memory loads or detection response tasks, and how they adjust strategies under varying stimulus discriminability and complexity. The findings reveal that EAMs effectively identify "red zones" where task demands exceed operator capacity. In air-traffic control and maritime surveillance, concurrent prospective memory tasks drained cognitive resources, lowering accumulation rates and increasing errors in primary conflict detection or ship classification tasks. Similarly, distracted driving reduced braking response accumulation rates, impairing hazard detection. The models distinguished between resource allocation and strategy changes; for instance, under high time pressure, operators diverted resources from secondary tasks to primary ones but often sacrificed processing quality, leading to slower, less accurate decisions. Furthermore, experts in medical diagnosis showed higher accumulation rates than novices, and stimulus complexity directly impaired processing efficiency. The review confirms that standard EAMs provide robust fits for longer-timescale decisions, allowing researchers to disentangle effects of capacity limits from strategic adaptations. The significance of this work lies in its potential to improve human operator training, work design, and the development of automated support tools. By identifying whether errors stem from interface design, suboptimal strategies, or excessive workload, practitioners can target interventions more precisely. The authors conclude that wider adoption of computational cognitive modeling in Human Factors research offers reciprocal benefits: applied research gains enhanced measurement of latent mechanisms, while basic research tests the generalizability of cognitive theories to complex, representative work tasks.
Key finding
Evidence accumulation models provide a unified theoretical framework that accurately characterizes latent cognitive processes and resource allocation strategies underlying human performance in complex, safety-critical work domains.
Methodology
review
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 | — | — | 5 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
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| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| enrich | success | semantic_scholar | — | — | 3 | 2026-06-15 |
| 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, theory or model