Flexibility of individual multitasking strategies in task-switching with preview: are preferences for serial versus overlapping task processing dependent on between-task conflict?

Abstract

The prevalence and the efficiency of serial and parallel processing under multiple task demands are highly debated. In the present study, we investigated whether individual preferences for serial or overlapping (parallel) processing represent a permanent predisposition or depend on the risk of crosstalk between tasks. Two groups (n = 91) of participants were tested. One group performed a classical task switching paradigm, enforcing a strict serial processing of tasks. The second group of participants performed the same tasks in a task-switching-with-preview paradigm, recently introduced by Reissland and Manzey (2016), which in principle allows for overlapping processing of both tasks in order to compensate for switch costs. In one condition, the tasks included univalent task stimuli, whereas in the other bivalent stimuli were used, increasing risk of crosstalk and task confusion in case of overlapping processing. The general distinction of voluntarily occurring preferences for serial or overlapping processing when performing task switching with preview could be confirmed. Tracking possible processing mode adjustments between low- and high-crosstalk conditions showed that individuals identified as serial processors in the low-crosstalk condition persisted in their processing mode. In contrast, overlapping processors split up in a majority adjusting to a serial processing mode and a minority persisting in overlapping processing, when working with bivalent stimuli. Thus, the voluntarily occurring preferences for serial or overlapping processing seem to depend at least partially on the risk of crosstalk between tasks. Strikingly, in both crosstalk conditions the individual performance efficiency was the higher, the more they processed in parallel.

Appendix: Definition of overall dual-tasking performance efficiency (ODTPE)

The ODTPE measure is a straightforward throughput measure. It describes how many of two tasks can be performed correctly in a given time when an individual has to cope with these two tasks concurrently or in close succession compared to a situation where the same tasks can be performed under single-task conditions.³. We refer to overall benefits of dual-tasking if the overall performance of two tasks performed under dual-task conditions is better, that is more tasks are performed correctly, than what would theoretically be expected in case of strictly separated processing of the two component tasks without considering any dual-tasking costs. Costs of dual-tasking are assumed if overall dual-task performance is worse, that is less tasks are performed correctly, than what would be expected from strictly separated processing. This logic can be illustrated by the following Gedankenexperiment:

Assume an individual can correctly solve 90 trials of a letter classification task (task A), and 80 trials of a digit classification task (task B), both in a single-task block of 1 min each. If this individual then has to work on both tasks concurrently and/or in close succession for another 1-min block, we would theoretically expect 45 correct responses to the letter classification task and 40 correct responses to the digit classification task, given that (1) this individual works on the two tasks in a strict serial processing mode, (2) this individual is able to perform the different tasks with the same speed as under single-task conditions, and (3) neither general costs or benefits of dual-tasking arise. Considering the performance of the component tasks separately and comparing it to the respective single-task performance, this might suggest a performance decrement of 50% in the dual-task condition. However, considering the overall performance for both component tasks together, and the fact that the time available per task was cut by 50% in the dual-task condition, neither a loss nor a benefit in performance was produced. Thus, the throughput of tasks has actually remained the same for both conditions. Dual-task benefits would be reflected in any higher throughput, that is a total number of correctly performed tasks (summed across both component tasks) higher than 85. This, for example, might be possible if some overlapping processing of the two tasks takes place. In contrast, overall dual-task costs would be reflected in the fact that an individual would achieve a fewer overall number of correct responses than could have been expected from the single-task performance in the single-task blocks (< 85). This could be due to, for example, costs of task switching or costs related to outcome conflicts.

In our experiments, we worked with single-task blocks of 1 min and dual-task trials of 2 min. If a participant is working strictly serially (without considering any switch and mixing costs) we, thus, would expect the same number of correct responses in single-task and mixed-trials or dual-task blocks, respectively. Following this reasoning, we defined ODTPE formally as follows:

$${\text{ODTPE}} = 100\; \times \;\left[ {\frac{{\left( {{\text{nC}}_{{{\text{A\_dual}}}} + {\text{nC}}_{{{\text{B\_dual}}}} } \right)}}{{\left( {{\text{nC}}_{{{\text{A\_single}}}} + {\text{nC}}_{{{\text{B\_single}}}} } \right)}}} \right] - 100,$$

with nC_{A_single} and nC_{B_sing le} defined as number of correct responses in the respective tasks under single-task conditions, and nC_{A_dual} and nC_{B_dual} defined as the corresponding performance in dual-task conditions. Based on this measure, performance benefits of multitasking as described above are reflected in values of ODTPE > 0. In contrast, costs of multitasking are reflected in values of ODTPE < 0. Note that the consideration of the number of correct responses generates an overall efficiency measure that represents costs and benefits reflected in both, response times and error rates.