The Effect of Task Interruption on Working Memory Performance

Abstract

Objective This study used electroencephalography to explore the behavioral and electrophysiological effects of task interruption on performance. Background Task interruption is known to harm work performance, especially on working memory-related tasks. However, most studies pay little attention to cognitive processes by exploring brain activity and ignore the cumulative effect of sequential interruptions. Method Thirty-four healthy participants performed a spatial 2-back in three conditions: (1) interruptions with simple math questions, (2) suspensions with prolonged fixation cross, and (3) a pure 2-back. The measured outcomes comprise performance data, ERP amplitudes, EEG power, and subjective workload. Results Work performance decreased in the resumption trials, and cumulative interruptions had a more destructive effect on performance. EEG results showed that the P2 and P3 amplitudes induced by the 2-back task significantly increased after interruptions; theta and alpha power increased after interruptions. The P3 amplitude and alpha power induced by interruptions were significantly higher than that induced by suspensions. Conclusion Behavioral data revealed the disruptive effect of interruptions on postinterruption performance and the cumulative effect of interruptions on accuracy. Changes in ERP amplitudes and EEG power indicate the mechanisms of attention reallocation and working memory during interruptions. Larger P3 amplitudes and alpha power after interruptions than after suspensions suggested the inhibition of irrelevant information. These results may support the memory for goals model and improve the understanding of the effects of interruption on working memory. Application Focusing upon the mechanisms at play during the interruption process can support interruption management to ensure work safety and efficiency.

Effect of Interruptions and Cognitive Demand on Mental Workload: A Critical Review

“…Attending to notifications during a complex sensorimotor task negatively impacts primary task performance, regardless of the modality used to present the notification [94]. Task interruption disrupted post-interruption performance and accuracy, with larger P3 amplitudes and alpha power after interruption than after suspension [102]. A two-stage warning system enhanced situation awareness reduced MWL, and improved takeover performance compared with a single-stage warning system [88].…”

“…More time is needed to resume the primary visual-manual assembly performance after interrupting a comparable activity [76]. Increased P3 amplitudes and alpha power in the interruption tasks suggest that the interference of irrelevant information has a stronger effect on resumption lag [102]. The Theory of Memory for Goals has been put out to explain how interruptions affect people's ability to execute goal-directed activity and recall information.…”

“…Participants who demonstrated a more vital ability to use cues also demonstrated less performance loss after interruptions [97]. Interruptions increased the WML, as evidenced by an increase in theta power [102]. Emotions are cognitive states triggered by neurophysiological shifts; they are linked differently to ideas, sensations, behavioral reactions, and levels of pleasure or discomfort [107].…”

“…The fundamental assumption behind ICA is that the EEG signals recorded at different scalp electrodes represent linear mixtures of the underlying neural activities originating from various brain regions. A high-pass filter (cutoff > 0.5 Hz) was used to eliminate artifacts from gradual signal changes, sweat, and electrode drifts [74], [77], [83], [102]. The data were downsampled to 256 Hz for further calculations [73], [86].…”

“…The eyetracking analysis models used were MATLAB (eye-tracking metrics) [89] and ClearView [79]. The different cognitive signal analysis models used were the BrainVision Analyzer 2.1 [73], Fast Fourier transformation [83], EEGLAB toolbox of MATLAB R2020a [74], [102], Modified Beer-Lambert Law (fNIRS) [90], and machine learning [77].…”