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When switching between different tasks, the initiation of task switches may depend on task characteristics (difficulty, salient cues, etc.) or reasons within the person performing the task (decisions, behavioral variability, etc.). The reasons for variance in switching strategies, especially in paradigms where participants are free to choose the order of tasks and the amount of switching between tasks, are not well researched. In this study, we follow up the recent discussion that variance in switching strategies might be partly explained by the characteristics of the person fulfilling the task. We examined whether risk tendency and impulsiveness differentiate individuals in their response (i.e., switch rates and time spent on tasks) to different task characteristics on a tracking-while-typing paradigm. In detail, we manipulated two aspects of loss prospect (i.e., “payoff” as the amount of points that could be lost when tracking was unattended for too long, and “cursor speed” determining the likelihood of such a loss occurring). To account for between-subject variance and within-subject variability in the data, we employed linear mixed effect analyses following the model selection procedure (Bates, Kliegl, et al., 2015). Besides, we tested whether risk tendency can be transformed into a decision parameter which could predict switching strategies when being computationally modelled. We transferred decision parameters from the Decision Field Theory to model “switching thresholds” for each individual. Results show that neither risk tendency nor impulsiveness explain between-subject variance in the paradigm, nonetheless linear mixed-effects models confirmed that within-subject variability plays a significant role for interpreting dual-task data. Our computational model yielded a good model fit, suggesting that the use of a decision threshold parameter for switching may serve as an alternative means to classify different strategies in task switching.

The goal of this study was to investigate the effect of predictability on dual-task performance in a continuous tracking task. Participants practiced either informed (explicit group) or uninformed (implicit group) about a repeated segment in the curves they had to track. In Experiment 1 participants practices the tracking task only, dual-task performance was assessed after by combining the tracking task with an auditory reaction time task. Results showed both groups learned equally well and tracking performance on a predictable segment in the dual-task condition was better than on random segments. However, reaction times did not benefit from a predictable tracking segment. To investigate the effect of learning under dual-task situation participants in Experiment 2 practiced the tracking task while simultaneously performing the auditory reaction time task. No learning of the repeated segment could be demonstrated for either group during the training blocks, in contrast to the test-block and retention test, where participants performed better on the repeated segment in both dual-task and single-task conditions. Only the explicit group improved from test-block to retention test. As in Experiment 1, reaction times while tracking a predictable segment were no better than reaction times while tracking a random segment. We concluded that predictability has a positive effect only on the predictable task itself possibly because of a task-shielding mechanism. For dual-task training there seems to be an initial negative effect of explicit instructions, possibly because of fatigue, but the advantage of explicit instructions was demonstrated in a retention test. This might be due to the explicit memory system informing or aiding the implicit memory system.