The project is organised with 7 workpackages:

WP1 will 1) analyze the preexisting literature on fixation errors in order to characterize specific precursors, with the final aim of 2) creating various scenarios that trigger fixation errors. These scenarios will be used to 3) conduct field experiments to collect data on fixation errors in close to real life simulation environments.

1. Theoretical characterization (M0-3, LISN)
2. Experimental design and scenarios (M2-6, LISN, all)
3. Implementation of the scenarios on the CLESS platform (M4-10, UCBL) and on the SCHEMAX platform (M4-10, ONERA)
4. Data collection (M10-16, UCBL and ONERA)

• Review of the literature at M3
• First version of the scenarios at M4 for pretests
• Implementation of the four scenarios in the two simulation environments (SCHEMAX at ONERA and CLESS at UCBL) at M10
• 30% of the data collection completed at M12
• 100% of the data collection completed at M16.

• D1.1 Review of the literature characterizing factors for fixation errors in the two domains (M12, LISN & IRBA)
• D1.2 Data from the experimentation (M16, UCBL & ONERA)

WP1 will collect three types of data during the simulations: behavioral data, physiological data and subjective data. The objectives of WP2 will be 1) to analyze the data following both a human factors methodology (i.e., task analysis) and a cognitive psychology methodology (task 1 and 2), and 2) to build a logic-based model of the situation for the future AI model (tasks 3 and 4).

1. Ergonomic task analysis and xAPI model of the task (M10-M24, IRBA and UCBL)
2. Cognitive psychology characterization of fixation errors (M10-M24, LISN and ONERA)
3. Data analysis for fixation error detection (M18-M24, ONERA)
4. Formal model of the task and mental states (M18-M24, LMF and LISN)

• Characterization of the behavioral markers of fixation errors at M18
• Task model at M20 and xAPI model at M24
• Formal model of the situation at M24
• Result of data analysis for AI modeling at M24

• D2.1 Report on characterizing factors of fixation errors based on participant’s data (M24, LISN and ONERA)
• D2.2 Report on human-factor analysis of the task and xAPI model (M24, IRBA and UBCL)
• D2.3 Logic-based model of the tasks for all scenarios and statistical model for data analysis (M24, LMF and ONERA).

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