Evidence accumulation in a complex task: Making choices about concurrent multiattribute stimuli under time pressure.
DOI: 10.1037/xap0000074
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This study investigates whether standard evidence accumulation models, specifically the diffusion model and the linear ballistic accumulation (LBA) model, can accurately describe decision-making in complex, applied settings. While these models have successfully explained rapid choices (typically under 1 second) involving simple stimuli, their applicability to complex tasks characterized by multiattribute stimuli, concurrent targets, and significant time pressure remains unclear. The authors aim to determine if these standard models can provide a coherent psychological explanation for performance in such environments without requiring task-specific parameterization. To test this, the researchers utilized a simulated unmanned aerial vehicle (UAV) operator task. This task required participants to detect heterogeneous multiattribute targets while simultaneously managing a UAV navigation task, creating realistic interruptions and workload demands. Unlike simple laboratory tasks, this scenario involved responses taking more than 2 seconds, complex classification rules, and the need to prioritize information processing among several concurrently present potential targets. The experimental design manipulated decision uncertainty (sensory difficulty of discriminations) and workload (time pressure) to observe their effects on model parameters, specifically the rate of evidence accumulation and the decision threshold. The results demonstrated that both the diffusion and LBA models performed well descriptively, fitting the data from the complex UAV task despite the longer response times and realistic complications. The models provided a coherent psychological account of how decision uncertainty and workload manipulations influenced performance. Specifically, the models successfully indexed the quality of information available to the decision-maker (via accumulation rates) and strategic variations in response caution (via thresholds). The findings indicated that standard accumulate-to-threshold models could approximate the fine-grained details of behavior, including the full distribution of choice response times and accuracy, even in tasks with multiple simultaneous stimuli and asynchronous stimulus availability. The significance of these findings lies in supporting the wider application of standard evidence accumulation models to applied decision-making settings. By demonstrating that these models do not require task-specific inputs for parameterization, the study suggests they can be used to diagnose the sources of errors in complex operational environments. For instance, the models can distinguish whether errors arise from poor information quality provided by an interface or from operators’ strategic adjustments in response caution. This capability offers important practical implications for human factors engineering, allowing for more precise interpretations of performance in fields such as air-traffic control and UAV operations.
Key finding
Standard evidence accumulation models, including the diffusion and linear ballistic accumulation models, effectively describe and explain performance in complex, time-pressured applied tasks involving multiattribute stimuli and concurrent workload demands.
Methodology
simulator
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 |
| 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 | qwen3.6-27b-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.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.