Brain Nebula

Reducing attention blink with brain stimulation

As usual, getting the email with the latest papers appearing in the Human Factors journal is a great part of my day. The January announcement was no different. You know that I am interested in brain stimulation and often share advances from the neuroscience journals that I read. It was very rewarding to see one in Human Factors. And the extra benefit is that it is already focused on human factors applications so describing its usefulness is much easier.is interrupted.

The authors determine whether transcranial direct current stimulation (tDCS) can reduce resumption time when an ongoing task is interrupted. Interruptions are common and disruptive. Working memory capacity has been shown to predict resumption lag (i.e., time to successfully resume a task after interruption). Given that tDCS applied to brain areas associated with working memory can enhance performance, tDCS has the potential to improve resumption lag when a task is interrupted.

The study by a team from the Human Factors and Applied Cognition Lab at George Mason found that brain stimulation to the left and right dorsolateral prefrontal cortex (DLPFC) reduces the lag time needed to switch between tasks, without any reduction in accuracy. This lag time is often referred to as attention blink. Based on the authors’ review of past research, they infer that this is due to the gain in working memory capacity. If you can keep the original task in working memory and still have enough room for the secondary task, then when it is time to switch back you don’t have to review and reactivate as much from the original. It is still there.

They also found a stronger effect for people with longer baseline lag times. People with longer baselines, perhaps because of lower than average working memory capacity, get the largest boost from the brain stimulation. They also found that the brain stimulation has a greater effect on task switching performance than it does for individual task performance, perhaps because task switching efficiency is so dependent on working memory.

My Take

It makes sense that increasing working memory capacity would facilitate task switching. When you have to switch between tasks, you need to inhibit the first one, activate the second one, then inhibit the second one and reactivate the first one. If you have greater working memory, you can keep them both there simultaneously. Maybe not all of the necessary information, but every little bit helps. This also explains why more similarity between the two tasks makes switching harder. The schema of the two tasks overlap, so they would interfere with each other. Keeping them both in working memory could even be counterproductive.

It also explains why alerts to incoming interruptions are helpful. The user can rehearse the pieces of the first task that will be needed to reactivate it later and make the working memory trace stronger. Then when the interruption comes, they are more prepared for it. When it comes time to switch back, more of it will be left. Perhaps not fully, but at least enough residual activation to prime the recovery.

It is a common finding in the training and augcog literatures that it is easier to boost people who are below the mean on a skill up to mean performance than it is to boost people at or above the mean up to superior performance. So I am not surprised that this study found the same thing. I suppose that this is a good thing – better to create a level playing field than it is to create superhuman users.

Your Turn

There are some other very useful findings in the study, so your first assignment is to go read the full paper. Teaser – how long do you think the effect of stimulation lasts? Does it work better for verbal or spatial tasks? I would also be very interested in your thoughts on the main effect that I have discussed here. Ready to wear an electrode-filled headset at work?

Image Credit: Ivan

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