Human attention secrets cracked in bid to avoid digital-age disasters

Full article issued by Macquarie University.

In a world first, ARC-supported researchers at Macquarie University have mapped brain signals in a way that allowed them to predict when someone had lost attention and would therefore miss a crucial moment in a network monitoring task. 

The discovery helps to lay the groundwork for technology that could help avoid potential tragedy when humans fail to notice vital computer errors, for instance when monitoring an air traffic or train network control system.

Hooked up to a Magnetoencephalography (MEG) machine in the KIT-Macquarie Brain Research Laboratory, the 21 subjects monitored several dots moving on trajectories towards a central fixed object, but deflecting before making contact. Their job was to press a button to deflect a moving dot if it instead violated its trajectory and continued towards a collision with the central object.

The experiment showed that the less often the dot violated its trajectory, the more likely the participants were to miss it, showing that attention dropped off dramatically over time when targets were infrequent.

'A computer is most often making the decision about who is going where, and keeping track of where everyone is –  but a human has to watch that, and if that computer makes a mistake, a human has to be ready to jump in to fix the error before you get a tragedy,' explains Anina Rich, Professor of Cognitive Science and head of Macquarie’s Perception in Action Research Centre, whose research is supported by multiple Discovery Projects grants.

Professor Rich says the study has shown proof of concept that it is possible to use the pattern of activity recorded in the brain to actually decode whether someone has lost crucial information about a task.

'We can tell when somebody’s brain doesn’t have the relevant information about the collision coming up. The next step is to see whether we can do that in real time and give them feedback to prevent that error.'

 

Image credit: Getty images.