Using scanpaths as a learning method for a conflict detection task of multiple target tracking

Ziho Kang, Steven J. Landry

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


Objective: The objective was to determine whether the scanpaths of air traffic controllers (ATCs) could be used to improve the performance of novices in a conflict detection task. Background: Studies in other domains show that novice performance can be improved by exposure to experts' scanpaths. Whether this effect can be found for an aircraft conflict detection task is unknown. Method: Scanpaths of 25 professional ATCs ("experts") were recorded using a medium-fidelity air traffic control simulation with realistic scripted traffic that included aircraft pairs that would lose separation. A total of 20 novices were exposed to experts' scanpaths ("treatment"), and their performance (for both loss of separation detection rates and false alarm rates) was compared to that of 20 novices given no treatment or instructions ("control") and 20 novices who were verbally instructed to attend to altitude ("instruction-only"). Interviews were held about the helpfulness of the exposure. The scanpaths were analyzed to find pattern differences among the three groups. Results: Chi-square tests showed significant differences for false alarm rates across the three groups (p = .001). Pairwise Mann-Whitney tests showed that the number of false alarms for the treatment group was significantly lower than that for the control group (p = .005), and trended lower than the instructiononly group (p = .08). Treatment group participants responded that experts' scanpaths helped. Analysis of scanpaths showed an increased tendency of the scanpath treatment group to follow the experts' scanpath. Conclusion: The scanpath training intervention improved novice performance by reducing false alarms. Application: Implementing experts' scanpaths into novices' active learning process shows promise in enhancing training effectiveness and reducing training time.

Original languageEnglish (US)
Pages (from-to)1150-1162
Number of pages13
JournalHuman Factors
Issue number6
StatePublished - Sep 2014

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Applied Psychology
  • Behavioral Neuroscience


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