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Trading off switch costs and stimulus availability benefits: An investigation of voluntary task-switching behavior in
Victor Mittelstädt1, Jeff Miller2, Andrea Kiesel3
1Department of Psychology, University of Freiburg, Engelbergerstraße 41, Freiburg, 79085, Germany. victor.mittelstaedt@psychologie.uni-freiburg.de.
Abstract:
In the present study, we introduce a novel, self-organized task-switching paradigm that can be used to study more directly the determinants of switching. Instead of instructing participants to randomly switch between tasks, as in the classic voluntary task-switching paradigm (Arrington & Logan, 2004), we instructed participants to optimize their task performance in a voluntary task-switching environment in which the stimulus associated with the previously selected task appeared in each trial after a delay. Importantly, the stimulus onset asynchrony (SOA) increased further with each additional repetition of this task, whereas the stimulus needed for a task switch was always immediately available. We conducted two experiments with different SOA increments (i.e., Exp. 1a = 50 ms, Exp. 1b = 33 ms) to see whether this procedure would induce switching behavior, and we explored how people trade off switch costs against the increasing availability of the stimulus needed for a task repetition. We observed that participants adapted their behavior to the different task environments (i.e., SOA increments) and that participants switched tasks when the SOA in task switches approximately matched the switch costs. Moreover, correlational analyses indicated relations between individual switch costs and individual switch rates across participants. Together, these results demonstrate that participants were sensitive to the increased availability of switch stimuli in deciding whether to switch or to repeat, which in turn demonstrates flexible adaptive task selection behavior. We suggest that performance limitations in task switching interact with the task environment to influence switching behavior.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

