Adaptive rescheduling of error monitoring in multitasking

The concurrent execution of temporally overlapping tasks leads to considerable interference between the subtasks. This also impairs control processes associated with the detection of performance errors. In the present study, we investigated how the human brain adapts to this interference between task representations in such multitasking scenarios. In Experiment 1, participants worked on a dual-tasking paradigm with partially overlapping execution of two tasks (T1 and T2), while we recorded error-related scalp potentials. The error positivity (Pe), a correlate of higher-level error evaluation, was reduced after T1 errors but occurred after a correct T2-response instead. MVPA-based and regression-based single-trial analysis revealed that the immediate Pe and deferred Pe are negatively correlated, suggesting a trial-wise trade-off between immediate and postponed error processing. Experiment 2 confirmed this finding and additionally showed that this result is not due to credit-assignment errors in which a T1 error is falsely attributed to T2. For the first time reporting a Pe that is temporally detached from its eliciting error event by a considerable amount of time, this study illustrates how reliable error detection in dual-tasking is maintained by a mechanism that adaptively schedules error processing, thus demonstrating a remarkable flexibility of the human brain when adapting to multitasking situations. Significance StatementMultitasking situations are associated with impaired performance, as the brain needs to allocate resources to more than one task at a time. This also makes it more difficult to detect ones own performance errors in such complex scenarios. In two experiments, we recorded error-related electroencephalographic (EEG) activity and found that the commonly assumed fixed temporal succession of control processes in error monitoring can be strategically interrupted. Individual processes of error detection can be temporally rescheduled to after completion of competing tasks. This reduces interference between the neural task representations and supports a more efficient execution of concurrent tasks in multitasking.