One challenge of such an ambitious project is that different people remember in different ways. Much of memory is based on the prioritization of our experiences — especially those that scare, surprise or give us pleasure.
“Our brains are actively sorting through our experiences and preferentially reorganizing memories to emphasize the things that are significant to us,” Ranganath says. Thankfully, he explains, we don’t have to pay attention to most of what is happening around us because of the heavy lifting our brains carry out subconsciously.
To gather data for a model, Ranganath and his colleagues give people things to learn and examine how differences in brain activity affect memory. In the lab, fMRI and EEG machinery captures rapid snapshots of changes in brain activity as a subject experiences an event, such as watching a movie.
During the experiment, a computer records the individual signals of hundreds of separate points of brain activity as a subject is taking in information. After the movie, researchers test the participant’s ability to recall things from the film, while monitoring brain activity during their responses.
With enough data, Ranganath’s team hopes to design a computer model that can predict, based on brain activity patterns, whether or not people will remember something they are learning. This model would be a giant step forward in developing better ways to learn — using approaches designed precisely with the brain’s wiring in mind.
For Ranganath, a predictive model could serve as a litmus test for determining the most effective ways to communicate and retain new information, such as during educational instruction and workforce training.
“By reverse engineering the brain’s circuits, we can come up with ways to learn better and devise new technologies to help people with memory disorders,” he says.