The Effect of Task Fidelity on Learning Curves

Frank E. Ritter, Ashley F. McDermott

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

What is the effect of level of simulation fidelity on learning and then on performance in the target task? We consider an example of an electronic maintenance training system with two levels of fidelity: a high fidelity (HiFi) simulation that basically takes as much time as the real-world task and a low fidelity (LoFi) simulation with minimal delays and many actions removed or reduced in fidelity and time. The LoFi simulation initially takes about one quarter of the time, and thus starts out providing about four times as many practice trials in a given time period. The time to perform the task modifies the learning curves for each system. The LoFi curve has a lower intercept and a steeper slope. For a small number of practice trials, this makes a significant difference. For longer time periods, the differences between low and high fidelity get smaller. Learners that move from low to high appear to not be adversely affected. We note factors that could influence this transfer (i.e., subtasks included in each simulation), and how this approach could be extended.

Original languageEnglish (US)
Title of host publicationProceedings of ICCM 2020 - 18th International Conference on Cognitive Modelling
EditorsTerrence C. Stewart
PublisherApplied Cognitive Science Lab, Penn State
ISBN (Electronic)9780998508245
StatePublished - 2020
Event18th International Conference on Cognitive Modelling, ICCM 2020 - Co-located with the 53rd Annual Meeting of the Society for Mathematical Psychology - Virtual, Online
Duration: Jul 20 2020Jul 31 2020

Publication series

NameProceedings of ICCM 2020 - 18th International Conference on Cognitive Modelling

Conference

Conference18th International Conference on Cognitive Modelling, ICCM 2020 - Co-located with the 53rd Annual Meeting of the Society for Mathematical Psychology
CityVirtual, Online
Period7/20/207/31/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization
  • Modeling and Simulation

Cite this