Using Computational Cognitive Modeling to Diagnose Possible Sources of Aviation Error
DOI: 10.1207/s15327108ijap1502_2
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Summary
This paper addresses the systemic nature of aviation errors, specifically focusing on taxi navigation errors and runway incursions. The authors argue that traditional "human error" frameworks are insufficient because they ignore the complex interaction between cognitive factors, environmental constraints, and aircraft dynamics. To diagnose the sources of these errors, the researchers developed a computational cognitive model using the ACT-R architecture. This model simulates a closed-loop system comprising the pilot, the aircraft, the visual scene, and the taxiway environment, aiming to predict how pilots make navigation decisions under time pressure and low visibility. The study was motivated by data from the NASA Ames Research Center’s T-NASA2 simulation, where 18 flight crews performed taxi operations at Chicago O’Hare International Airport. The modeling effort focused on 12 "major errors" (deviations of 50 feet or more from the cleared route) observed in baseline conditions using only paper charts. To construct the pilot model, the authors conducted task analyses and interviews with subject matter experts (SMEs) to identify five distinct decision strategies ranging from cognitively intensive (e.g., recalling clearance details or deriving from map knowledge) to fast, frugal heuristics (e.g., turning toward the gate or minimizing X/Y distance to the destination). The accuracy of these heuristics was empirically validated by having an SME map typical taxi routes for nine major U.S. airports. The ACT-R model integrated these strategies with a simplified aircraft dynamics model that constrained decision horizons based on turn radius and speed limits. The model demonstrated that pilots dynamically select decision strategies based on the time available before reaching an intersection. When decision horizons were short, the model selected robust but less precise heuristics, whereas longer horizons allowed for more accurate, cognitively demanding strategies. The simulation results aligned with the NASA data, revealing that errors occurred most frequently at atypical taxiway geometries or clearance routes where globally robust heuristics failed. This provided empirical support for the hypothesis that pilots rely on fast, frugal heuristics to cope with limited decision time, and that errors arise when these heuristics are mismatched with specific environmental constraints. The significance of this work lies in its demonstration that computational cognitive modeling can effectively diagnose the systemic causes of aviation errors. By showing how environmental factors and cognitive limitations interact to produce specific error patterns, the study challenges the notion of error as solely a human failure. Instead, it highlights the importance of designing systems and interfaces that support robust heuristic decision-making, particularly in complex, time-constrained environments. This approach offers a method for predicting and mitigating errors by understanding the precise mechanisms through which multiple factors conspire to cause operational failures.
Key finding
The computational model successfully replicated observed taxi errors by demonstrating that pilots select decision strategies based on time pressure, with errors occurring most frequently when short decision horizons at atypical geometries forced the use of robust but less accurate heuristics.
Methodology
simulation_modeling
Sample size: 18
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 openalex_author_sweep on 2026-05-07 (3 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-07 |
| archive | success | canonical_url | — | — | 13 | 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 | openalex | — | — | 2 | 2026-05-08 |
| promote | success | — | — | — | 1 | 2026-05-07 |
| 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