The recent proliferation of commercial “brain-training” services that promise to enhance intelligence and cognitive functioning is understandable: Who wouldn’t want more working memory, attention, and inhibitory control? However, the effectiveness of these services has been questioned, particularly with respect to how well their training generalizes to the real world. My students Lauren Kahn and Junaid Merchant and I recently published a study using neuroimaging to explore what happens in the brain during a training to improve inhibitory control and to explain why that training might not transfer to new contexts. We found that training caused activity in parts of the brain network associated with inhibitory control to shift earlier in time, proactively engaging before control was needed. This shift improved performance on the training task itself because proactive control is more efficient than reactive control, but there’s a catch: with training, the brain activity became linked to specific cues that predicted when inhibitory control might be needed. This result is important because it explains how brain training improves performance on a given task and also why the performance boost doesn’t generalize beyond that task. Following replication of our result, a compelling next step is to develop an intervention that features cues from real environments where inhibitory control is desirable.
We trained inhibitory control using the stop-signal task, which models inhibitory control as a race between a “go” process and a “stop” process. A faster stop process (relative to the go process) indicates more efficient inhibitory control. In each of a series of trials, participants were given a “go” signal—an arrow pointing left or right. The instruction was simply to press a key corresponding to the direction of the arrow as quickly as possible, launching the go process. However, on a minority of trials, a beep sounded after the arrow appeared, signaling participants to try to withhold their button press, launching the stop process. On beep trials, we inferred that the go process won if participants pressed a button, and that the stop process won if they didn’t press one. By varying the time between the arrow and the beep, and then measuring the proportion of successful stops, we obtained a measure of inhibitory control performance for each participant at each training session.
Participants practiced either the stop-signal task or a control task that didn’t involve inhibitory control every other day for three weeks. Performance improved more in the training group than in the control group. We also measured participants’ neural activity during the stop-signal task before and after using functional magnetic resonance imaging. Activity in the inferior frontal gyrus and the anterior cingulate cortex, which monitor for and trigger inhibitory control, decreased during inhibitory control but increased immediately before it in the training group more than in the control group. These data provide evidence that inhibitory control training causes a proactive shift, but we note that we focused exclusively on inhibitory control; we cannot tell whether these results extend to other kinds of executive function (e.g., working memory).
For further editorializing, see Elliot’s post on this paper on his blog at Psychology Today.
Citation info: Berkman, E.T., Kahn, L.E., & Merchant, J.S. (in press). Training-induced changes in inhibitory control network activity. Journal of Neuroscience. [pdf]
