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It has been recently suggested that research on human multitasking is best organized according to three research perspectives, which differ in their focus on cognitive structure, flexibility, and plasticity. Even though it is argued that the perspectives should be seen as complementary, there has not been a formal approach describing or explaining the intersections between the three perspectives. With this theoretical note, we would like to show that the explicit consideration of individual differences is one possible way to elaborate in more detail on how and why the perspectives complement each other. We will define structure, flexibility, and plasticity; describe what constitutes individual differences; will outline selected empirical examples; and raise possible future research questions helping to develop the research field. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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., (Copyright © 2022. Published by Elsevier B.V.)

This study examined the effect of instructions and feedback on the integration of two tasks. Task-integration of covarying tasks are thought to help dual-task performance. With complete task integration of covarying dual tasks, a dual task becomes more like a single task and dual-task costs should be reduced as it is no longer conceptualized as a dual task. In the current study we tried to manipulate the extent to which tasks are integrated. We covaried a tracking task with an auditory go/no-go task and tried to manipulate the extent of task-integration by using two different sets of instructions and feedback. A group receiving task-integration promoting instructions and feedback (N = 18) and a group receiving task-separation instructions and feedback (N = 20) trained on a continuous tracking task. The tracking task covaried with the auditory go/no-go reaction time task because high-pitch sounds always occurred 250 ms before turns, which has been demonstrated to foster task integration. The tracking task further contained a repeating segment to investigate implicit learning. Results showed that instructions, feedback, or participants' conceptualization of performing a single task versus a dual task did not significantly affect task integration. However, the covariation manipulation improved performance in both the tracking and the go/no-go task, exceeding performance in non-covarying and single tasks. We concluded that task integration between covarying motor tasks is a robust phenomenon that is not influenced by instructions or feedback.

In this study we investigated the effect of predictability on dual-task performance in two experiments. In the first experiment 33 participants separately practiced a continuous tracking task and an auditory reaction time task. Both tasks had a repeating element that made them predictable; in the tracking task this was a repeating segment, and in the auditory task this was an auditory sequence. In addition, one group obtained explicit knowledge about the repeating sequence in the tracking task while the other group trained implicitly. After training, single- and dual-task performance was tested at a post test and retention test. Results showed that predictability only improved performance in the predictable tasks themselves and dual-task costs disappeared for the tracking task. To see whether the task-specific effect of predictability was the results of task prioritization, or because task representations did not have much chance to interact with each other, we conducted a second experiment. Using the same tasks as in Experiment 1, 39 participants now trained both tasks simultaneously. Results largely mirrored those of the first experiment, demonstrating that freed-up resources due to predictability in one task could not be re-invested to improve in the other task. We conclude that predictability has a positive but task-specific effect on dual-task performance., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2021 The Author(s).)

The aim of this study was to examine the impact of predictability on dual-task performance by systematically manipulating predictability in either one of two tasks, as well as between tasks. According to capacity-sharing accounts of multitasking, assuming a general pool of resources two tasks can draw upon, predictability should reduce the need for resources and allow more resources to be used by the other task. However, it is currently not well understood what drives resource-allocation policy in dual tasks and which resource allocation policies participants pursue. We used a continuous tracking task together with an audiomotor task and manipulated advance visual information about the tracking path in the first experiment and a sound sequence in the second experiments (2a/b). Results show that performance predominantly improved in the predictable task but not in the unpredictable task, suggesting that participants did not invest more resources into the unpredictable task. One possible explanation was that the re-investment of resources into another task requires some relationship between the tasks. Therefore, in the third experiment, we covaried the two tasks by having sounds 250 ms before turning points in the tracking curve. This enabled participants to improve performance in both tasks, suggesting that resources were shared better between tasks.

Visual information and prior knowledge represent two different sources of predictability for tasks which each have been reported to have a beneficial effect on dual-task performance. What if the two were combined? Adding multiple sources of predictability might, on the one hand, lead to additive, beneficial effects on dual-tasking. On the other hand, it is conceivable that multiple sources of predictability do not increase dual-task performance further, as they complicate performance due to having to process information from multiple sources. In this study, we combined two sources of predictability, predictive visual information and prior knowledge (implicit learning and explicit learning) in a dual-task setup. 22 participants performed a continuous tracking task together with an auditory reaction time task over three days. The middle segment of the tracking task was repeating to promote motor learning, but only half of the participants was informed about this. After the practice blocks (day 3), we provided participants with predictive visual information about the tracking path to test whether visual information would add to beneficial effects of prior knowledge (additive effects of predictability). Results show that both predictive visual information and prior knowledge improved dual-task performance, presented simultaneously or in absence of each other. These results show that processing of information relevant for enhancement of task performance is unhindered by dual-task demands., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2020 The Author(s).)

The study examined the impact of visual predictability on dual-task performance in driving and tracking tasks. Participants (N = 27) performed a simulated driving task and a pursuit tracking task. In either task, visual predictability was manipulated by systematically varying the amount of advance visual information: in the driving task, participants drove at night with low beam, at night with high beam, or in daylight; in the tracking task, participants saw a white line that specified the future target trajectory for 200, 400 or 800 ms. Concurrently with driving or tracking, participants performed an auditory task. They had to discriminate between two sounds and press a pedal upon hearing the higher sound. Results show that in general, visual predictability benefited driving and tracking; however, dual-task driving performance was best with highest visual predictability (daylight), dual-task tracking performance was best with medium visual predictability (400 ms). Braking/reaction times were higher in dual tasks compared to single tasks, but were unaffected by visual predictability, showing that its beneficial effects did not transfer to the auditory task. In both tasks, manual accuracy decreased around the moment the foot pressed the pedal, indicating interference between tasks. We, therefore, conclude that despite a general beneficial impact of predictability, the integration of visual information seems to be rather task specific, and that interference between driving and audiomotor tasks, and tracking and audiomotor tasks, seems comparable.

Although multitasking has been the subject of a large number of papers and experiments, the term task is still not well defined. In this opinion paper, we adopt the ideomotor perspective to define the term task and distinguish it from the terms goal and action. In our opinion, actions are movements executed by an actor to achieve a concrete goal. Concrete goals are represented as anticipated sensory consequences that are associated with an action in an ideomotor manner. Concrete goals are nested in a hierarchy of more and more abstract goals, which form the context of the corresponding action. Finally, tasks are depersonalized goals, i.e., goals that should be achieved by someone. However, tasks can be assigned to a specific person or group of persons, either by a third party or by the person or the group of persons themselves. By accepting this assignment, the depersonalized task becomes a personal goal. In our opinion, research on multitasking needs to confine its scope to the analysis of concrete tasks, which result in concrete goals as anticipated sensory consequences of the corresponding action. We further argue that the distinction between dual- and single-tasking is dependent on the subjective conception of the task assignment, the goal representation and previous experience. Finally, we conclude that it is not the tasks, but the performing of the tasks, i.e. the actions that cause costs in multitasking experiments.

Performance decrements in multitasking have been explained by limitations in cognitive capacity, either modelled as static structural bottlenecks or as the scarcity of overall cognitive resources that prevent humans, or at least restrict them, from processing two tasks at the same time. However, recent research has shown that individual differences, flexible resource allocation, and prioritization of tasks cannot be fully explained by these accounts. We argue that understanding human multitasking as a choice and examining multitasking performance from the perspective of judgment and decision-making (JDM), may complement current dual-task theories. We outline two prominent theories from the area of JDM, namely Simple Heuristics and the Decision Field Theory, and adapt these theories to multitasking research. Here, we explain how computational modelling techniques and decision-making parameters used in JDM may provide a benefit to understanding multitasking costs and argue that these techniques and parameters have the potential to predict multitasking behavior in general, and also individual differences in behavior. Finally, we present the one-reason choice metaphor to explain a flexible use of limited capacity as well as changes in serial and parallel task processing. Based on this newly combined approach, we outline a concrete interdisciplinary future research program that we think will help to further develop multitasking research.