Modeling dual-task performance: do individualized models predict dual-task performance better than average models?
Year: 2017
Type: dissertation
Abstract: Understanding multitasking can be a complicated venture. The goal of this paper is to see whether\nusing individual parameters for modeling dual-task will lead to better predictions of individual\nperformance compared to using the global total average of all participants. It is expected that\nmodeling individual skill will lead to more accurate models because individual parameters will lead\nto closer fits between individual data points and their models. Therefore, using individual\nparameters might provide a better explanation of the adaption of different strategies among\nparticipants. Data from a study involving 12 participants performing a phone-driving task has been\nused. The model consists of a driving and dialing model. The results of individualized performance\nshow that using individualized parameters don't necessarily provide more accurate model\npredictions than using global parameters. This implies that factoring individual skill might not be\nvery useful when modeling dual-tasking performance, however it does tell us an interesting story\nabout the whether there are other individual factors we should take into account. The implication\nis that it's still too simplistic to look at just average performance to explain multitasking behavior,\nso it could be interesting to take a closer look at other individual factors in future research.
Author: Weidan Cao
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2756000807