by Erica Moser
Let’s say you typically eat eggs for breakfast but were running late and ate cereal. As you crunched on a spoonful of Raisin Bran, other contextual similarities remained: You ate at the same table, at the same time, preparing to go to the same job. When someone asks later what you had for breakfast, you incorrectly remember eating eggs.
This would be a real-world example of a false memory. But what happens in your brain before recalling eggs, compared to what would happen if you correctly recalled cereal?
In a paper published in Proceedings of the National Academy of Sciences, University of Pennsylvania neuroscientists show for the first time that electrical signals in the human hippocampus differ immediately before recollection of true and false memories. They also found that low-frequency activity in the hippocampus decreases as a function of contextual similarity between a falsely recalled word and the target word.
“Whereas prior studies established the role of the hippocampus in event memory, we did not know that electrical signals generated in this region would distinguish the imminent recall of true from false memories,” says psychology professor Michael Jacob Kahana, director of the Computational Memory Lab and the study’s senior author. He says this shows that the hippocampus stores information about an item with the context in which it was presented.
Researchers also found that, relative to correct recalls, the brain exhibited lower theta and high-frequency oscillations and higher alpha/beta oscillations ahead of false memories. The findings came from recording neural activity in epilepsy patients who were already undergoing invasive monitoring to pinpoint the source of their seizures.
Noa Herz, lead author and a postdoctoral fellow in Kahana’s lab at the time of the research, explains that the monitoring was done through intracranial electrodes, the methodology researchers wanted to use for this study. She says that, compared to scalp electrodes, this method “allowed us to more precisely, and directly, measure the neural signals that were generated in deep brain structures, so the activity we are getting is much more localized.”
Read the full story in Penn Today.
Michael Kahana is the Edmund J. and Louise W. Kahn Term Professor of Psychology in the School of Arts & Sciences and director of the Computational Memory Lab at the University of Pennsylvania. He is a member of the Penn Bioengineering Graduate Group.
Machine learning (ML) programs computers to learn the way we do – through the continual assessment of data and identification of patterns based on past outcomes. ML can quickly pick out trends in big datasets, operate with little to no human interaction and improve its predictions over time. Due to these abilities, it is rapidly finding its way into medical research.
