Researchers at Sandia National Lab have shown that, using a traditional electroencephalography (EEG) cap, they were able to detect when study participants effectively remembered what they were learning. Repeatedly testing a group of people as they were learning a set of words, the team developed baseline readings that indicate how well something is being stored into memory. Having people study under EEG, an algorithm was able to compare their brainwaves to the established baselines and successfully predict whether they’ll remember the words given.
The researchers hope that the technology will find use in helping tailor different learning methods for individual students and further aid our understanding of how the brain controls memory.
About 90 volunteers spent nine to 16 hours over five weeks in testing for the memory training techniques study. Their first session developed a baseline for how well they remembered words or images. Most then underwent memory training for three weeks and were retested.
A control group received no training. A second group practiced mental imagery strategy, thinking up vivid images to remember words and pictures. The final group went through “working memory” training to increase how much information they could handle at a time. Matzen said that averages about seven items, such as digits in a phone number.
Each volunteer, shut into a sound-proof booth, watched a screen that flashed words or images for one second, interrupted with periodic quizzes on how well the person remembered what was shown.
Each section tested a different type of memory. The first, middle and last sections consisted of single nouns. During quizzes, volunteers hit buttons for yes or no, indicating whether they’d seen the word before. The other two sections combined adjectives and nouns or pairs of unrelated drawings, with volunteers again tested on what they remembered. The image section tested associative memory — memory for two unrelated things.
Press release: Sandia shows monitoring brain activity during study can help predict test performance