Dr. Polyn’s mental time travel machine

Photo by Lucy Wimmer

By Joseph Marasciullo

Staff writer

On March 6, Dr. Sean M. Polyn, Lab Director at the Vanderbilt Computational Memory Lab, came to Sewanee to present his findings on mental time travel, the method by which we recall past events. The audience included a crowd of 16 high schoolers, a handful of very interested Sewanee students, and me, a journalist who thought that a presentation with time travel in the name couldn’t not be cool.

This event was a part of Sewanee’s second annual Brain Awareness week, running March 6-10.  Other events for the week included a screening of Eternal Sunshine of the Spotless Mind, a student research colloquium, and the “Motor Cortex Mocha,” a limited edition drink special from Stirling’s Coffee House.

Promotional beverages aside, the mental time travel presentation was extremely well done. Polyn explained that the term “mental time travel” entails no time machines or faster than light (FTL) travel, but instead is the process of searching the brain for information. Polyn hopes to discover “why we remember certain things, and how.”

In his experiment, Polyn flashed a series of common images to a participant and asked the participant to recall them all in no particular order. Polyn postulated that the subjects would experience what is called the “Recency Effect,” in which subjects remember the most recent items shown first. The idea did have merit but wasn’t always true.

This is where the idea of “Mental Time Travel” appeared. Participants should have remembered the most recent objects first, but this did not always occur. To solve this cranial conundrum, Polyn employed a computational algorithm that measured the electron activity in the brain. Using this, he and his colleagues determined multiple “classifiers,” electron patterns in the brain that would correspond with a specific category of image being shown.

For his experiment, the three categories were famous people, common landmarks, and household items. When asked to repeat what they saw, participants remembered items in “category clusters,” i.e. groups of celebrities, groups of famous landmarks, and groups of household items. This information was fed into the first algorithm, and the resulting predictor algorithm uses these spikes in brain activity to more accurately predict what people are thinking about.

“Memory retrieval is never spontaneous,” Polyn explained. “What you get out is consistent with what you put in, and the queue in which it is put in.”

This predictor algorithm became the focus of Polyn’s latest experiment, in which he uses the program to predict a person’s thoughts. Although we still don’t know for sure why and how memories are recalled or lost, the predictor algorithm, formally named the “neutrally informed model,” is successfully predicting memory patterns.  This model searches for the same signals described in the second experiment, using them to predict someone’s next idea. For example, if someone’s brain lights up in a way consistent with the “famous landmarks” classifier, then the algorithm predicts that a famous landmark will be remembered next.  

As with all science, there is still much more to be explored and learned of the human brain. Eventually, Polyn hopes that his findings will help people suffering from seizures and brain damage. That being said, he acknowledges that such findings about memory will may be exploited by corporations for commercial purposes. Nevertheless, Polyn continues to see the human brain as a puzzle, a puzzle that he and his colleagues hope to one day solve.