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NASA Ames Research Center


As a human factors research intern, I supported a project for the Aerospace Cognitive Engineering lab in the Human Systems Integration Division at NASA Ames Research Center under principle investigator Dorrit Billman, Ph.D. 

This human-in-the-loop experiment focused on creating training for generalized knowledge in complex cognitive work and analyzing how procedure automation software can support this work.

Scope: 6 months

The Research

  • There is much to be explored in the relationship between training a mental model of a human-computer system and transfer of learning to solve novel problems. As NASA missions become longer, the ability to contact mission control will be increasingly difficult. Understanding how a mental model of a device influences problem solving in situations where systems encounter dysregulated preconditions is imperative for safe and successful missions.


The Result

  • In this experiment, simulated habitat systems of the International Space Station were utilized. One group of participants received a training condition that sought to instill a mental model of the carbon dioxide removal system (CDRS) and its related systems. The second group received a training condition that excluded device model knowledge. In this group, users were taught how to execute routine procedures for activating and shutting down the CDRS and its interdependent systems. After training, both groups were presented with the same set of transfer problems. The results comparing performance between device model and no model conditions are analyzed to inform our knowledge of the effectiveness of device model training for optimizing problem solving in human-computer system operation.

  • The team developed two digital training products that examined the influence of a mental model on knowledge integration of complex habitat systems on the International Space Station simulator. 

My Contributions

My responsibilities included contributing to the experimental documentation, iterating on the digital training product with 1 other researcher, recruiting 30 MS and PhD aerospace engineering students from San José State University and Stanford University as study participants, facilitating 15 sessions of the 4.5 hour experiment, and scoring and analyzing qualitative data.

I presented preliminary findings from a portion of the study at the division-wide and Ames-wide intern poster sessions with mentorship and support from principal investigator Dorrit Billman, PhD.

What I Learned

Through being on the team of this aerospace usability and human factors research project, I learned about the importance of effective cognitive engineering as it relates to training in semi-automated human-machine interaction. When operators are trained such a way that their knowledge can be generalized to unexpected situations, there is a favorable decrease in reliance on external support. As NASA aims to go on longer and further missions with lesser ability to contact ground control, generalizable training methodology needs to be engineered to instill a mental model of interdependent, complex systems.

Carbon Dioxide Removal System on the ISS

CDRS schematic diagram used in device model training condition

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