How history trails and set size influence detection of hostile intentions

Patton, Colleen E.; Wickens, Christopher D.; Clegg, Benjamin A.; Noble, Kayla M.; Smith, C. A. P. · 2022 · Cognitive Research Principles and Implications

DOI: 10.1186/s41235-022-00395-5

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 the cognitive mechanisms underlying the detection of hostile intentions in dynamic maritime environments, specifically addressing the difficulty operators face in identifying threatening vessel behaviors from movement patterns alone. Motivated by the critical need for accurate threat identification in naval scenarios where technological aids may be unreliable, the research explores how working memory constraints and visual aids influence performance. The authors focus on two specific hostile behaviors: "hunting," where a vessel closes in on the target, and "shadowing," where a vessel mirrors the target’s movements. The study aims to determine if reducing working memory load through visual history trails or decreasing the number of tracked objects (set size) can improve detection accuracy. The researchers employed a simulated maritime task called the "ShadowHunt" paradigm, involving 35 participants who controlled a vessel while monitoring six other computer-controlled ships. Participants had to identify which ship was hostile and whether it was hunting or shadowing within 35 moves. Experiment 1 manipulated the presence of "history trails," visual lines connecting the previous nine positions of each ship, to offload spatial working memory demands. Experiment 2 orthogonally manipulated both the presence of history trails and the set size (number of distractor ships). All ship movements included 25% noise to simulate real-world unpredictability. Performance was measured by the accuracy of correctly identifying both the hostile ship and its specific behavior. The results demonstrated that history trails consistently improved detection performance, raising accuracy from 57% to 66% in Experiment 1. This benefit was observed for both hunting and shadowing behaviors and persisted even at greater distances, where detection is typically more difficult. In Experiment 2, reducing the set size of distractor ships also significantly improved detection performance. However, despite these improvements, accuracy remained well below optimal levels. The data indicated that participants did not spend more time or take more steps when history trails were present, suggesting that the visual aid allowed for more efficient accumulation of diagnostic evidence rather than extended observation. Additionally, a bias toward reporting shadowing over hunting was observed in error trials. The findings suggest that working memory capacity is a primary bottleneck in detecting hostile intentions from dynamic spatial data. By externalizing trajectory information through history trails, operators can reduce the cognitive load required to track past positions, thereby freeing resources for intent diagnosis. The study concludes that simple visual aids and reducing the number of tracked objects are effective strategies for mitigating working memory constraints. These insights have significant implications for the design of naval displays and decision-support systems, highlighting the importance of ecological interface design principles that replace memory demands with perceptual cues to enhance operator performance in high-stakes, dynamic environments.

Key finding

History trails consistently improved detection performance for both hunting and shadowing behaviors, and smaller set sizes also enhanced detection accuracy, indicating that working memory constraints significantly limit hostile intent detection.

Methodology

simulator

Sample size: 35

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 verifier_completed on 2026-05-11.

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.

• signal detection theory