The influence of rewards on (sub-)optimal interleaving

Christian P Janssen, Emma Everaert, Heleen M A Hendriksen, Ghislaine L Mensing, Laura J Tigchelaar, Hendrik Nunner

Research output: Contribution to journalArticleAcademicpeer-review


We investigate how the rewards of individual tasks dictate a priori how easy it is to interleave two discrete tasks efficiently, and whether people then interleave efficiently. Previous research found that people vary in their ability to interleave efficiently. Less attention has been given to whether it was realistic to expect efficient interleaving, given the reward rate of each of the involved tasks. Using a simulation model, we demonstrate how the rewards of individual tasks lead to different dual-task interleaving scenarios. We identify three unique dual-task scenarios. In easy scenarios, many strategies for time division between tasks can achieve optimal performance. This gives great opportunity to optimize performance, but also leads to variation in the applied strategies due to a lack of pressure to settle on a small set of optimal strategies. In difficult scenarios, the optimal strategy is hard to identify, therefore giving little opportunity to optimize. Finally, constrained scenarios have a well-defined prediction of the optimal strategy. It gives a narrow prediction, which limits the options to achieve optimal scores, yet given the structure people are able to optimize their strategies. These scenarios are therefore best to test people's general capability of optimizing interleaving. We report three empirical studies that test these hypotheses. In each study, participants interleave between two identical discrete tasks, that differ only in the underlying reward functions and the combined result (easy, difficult, or constrained scenario). Empirical results match the theoretical pattern as predicted by simulation models. Implications for theory and practice are discussed.

Original languageEnglish
Pages (from-to)e0214027
Issue number3
Publication statusPublished - 2019


  • Adult
  • Computer Simulation
  • Female
  • Humans
  • Male
  • Models, Psychological
  • Multitasking Behavior
  • Reward
  • Task Performance and Analysis
  • Young Adult

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