Neural signatures of vigilance decrements predict behavioural errors before they occur

There are now many environments in which humans need to monitor moving displays and only rarely act, such as train control and driving autonomous vehicles; lapses of attention in these circumstances can have tragic consequences. Problematically, we know that it is difficult to sustain attention under these monitoring or vigilance conditions and performance drops: when target events are rare, we tend to miss them, or are slower to respond. This rare target effect becomes more marked with longer tasks, known as a vigilance decrement. Despite the importance, we still have limited understanding of how the brain processes information during monitoring, particularly with dynamic stimuli, and how this processing changes when attention lapses. Here, we designed a multiple-object monitoring (MOM) paradigm that required sustained attention to dynamic stimuli, and used multivariate analyses of magnetoencephalography (MEG) data to examine how the neural representation of the information in the display varied with target frequency and time on the task. Behavioural performance decreased over time for the rare target (monitoring) condition, but not for the frequent target (active) condition. This change was mirrored in the neural results: under monitoring conditions, there was weaker coding of the critical distance between objects during time periods when vigilance decrements in performance occurred. There was also weaker informational connectivity between peri-occipital and peri-frontal brain areas in rare versus frequent target conditions. We developed a new analysis which used the strength of information decoding to predict whether the participant was going to miss the target on a given trial. We could predict behavioural errors more than a second before they occurred. This provides a first step in developing methods to predict and pre-empt behavioural errors due to lapses in attention and provides new insight into how vigilance decrements are reflected in information coding in the brain.