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Description: Abstract- There are many monitoring environments, such as railway control, in which lapses of attention can have tragic consequences. Problematically, sustained monitoring for rare targets is difficult, with more misses and longer reaction times over time. What changes in the brain underpin these “vigilance decrements”? We designed a multiple-object monitoring (MOM) paradigm to examine how the neural representation of information varied with target frequency and time performing the task. Behavioural performance decreased over time for the rare target (monitoring) condition, but not for a frequent target (active) condition. This was mirrored in neural decoding using Magnetoencephalography: coding of critical information declined more during monitoring versus active conditions along the experiment. We developed new analyses that can predict behavioural errors from the neural data more than a second before they occurred. This facilitates pre-empting behavioural errors due to lapses in attention and provides new insight into the neural correlates of vigilance decrements.

License: CC-By Attribution 4.0 International

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The task was to avoid collisions of relevant moving dots with the central object by pressing the space bar if the dot passed a deflection point in a v...

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MEG data in MATLAB .mat format

Below you can see and download the preprocessed (band-pass filtered 0.03–200 Hz and notch-filtered at 50 Hz), epoched (around the stimulus onset/appea...

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