Predicting and Improving Recognition Memory Using Multiple Electrophysiological Signals in Real Time.

Although people are capable of storing a virtually infinite amount of information in memory, their ability to encode new information is far from perfect. The quality of encoding varies from moment to moment and renders some memories more accessible than others. Here, we were able to forecast the likelihood that a given item will be later recognized by monitoring two dissociable fluctuations of the electroencephalogram during encoding. Next, we identified individual items that were poorly encoded, using our electrophysiological measures in real time, and we successfully improved the efficacy of learning by having participants restudy these items. Thus, our memory forecasts using multiple electrophysiological signals demonstrate the feasibility and the effectiveness of using real-time monitoring of the moment-to-moment fluctuations of the quality of memory encoding to improve learning.