An Integrated Trial-Level Performance Measure: Combining Accuracy and RT to Express Performance During Learning

Florian Sense, Tiffany Jastrzembski, Michael Krusmark, Siera Martinez, Hedderik van Rijn

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Memory researchers have studied learning behavior and extracted regularities describing learning and forgetting over time. Early work revealed forgetting curves and the benefits of temporal spacing and testing for learning. Computational models formally implemented these regularities to capture relevant trends over time. As these models improved, they were applied to adaptive learning contexts, where learning profiles could be identified from responses to past learning events to predict and improve future performance. Often times, past performance is expressed as accuracy alone. Here we explore whether a model's predictions can be improved if past performance is expressed by an integrated measure that combines accuracy and response times (RT). We present a simple, data-driven method to combine accuracy and RT on a trial-by-trial basis. This research demonstrates that predictions made using the Predictive Performance Equation improve when past performance is expressed as an integrated measure rather than accuracy alone.

Original languageEnglish
Title of host publicationProceedings of the 41st Annual Meeting of the Cognitive Science Society
Subtitle of host publicationCreativity + Cognition + Computation, CogSci 2019
PublisherThe Cognitive Science Society
Number of pages6
ISBN (Electronic)9780991196777
Publication statusPublished - 2019
Event41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019 - Montreal, Canada
Duration: 24-Jul-201927-Jul-2019


Conference41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019


  • accuracy
  • cognitive model
  • forgetting
  • integrated measure
  • Learning
  • response time

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