Pupil Size Indicates Which Memories We Relive In Dreams: Study

A new study has investigated the link between pupil dilation/contraction and memory processing during sleep. The study titled, “Sleep microstructure organizes memory replay”, published in the journal Nature and conducted by scientists at Cornell University, Ithaca, claims that pupil size while sleeping could indicate the memories you are reliving in your dreams.

Using advanced eye-tracking technology combined with EEG (electroencephalogram), the researchers monitored the sleep patterns of mice to record their brain activity. Notably, the mice were fed new information such as navigating the maze during the day and allowed to sleep at night.

Upon analysing the data, it was found that two substages happened during the NREM (Non-Rapid Eye Movement) sleep. The pupils contracted in one phase, suggesting new memories were being replayed while pupils dilated when the mice were likely processing or reliving past experiences in their dreams. Both phases took place in quick succession.

“It’s like new learning, old knowledge, new learning, old knowledge, and that is fluctuating slowly throughout the sleep,” neuroscientist Azahara Oliva of the Department of Neurobiology and Behaviour told ScienceAlert.

Also Read | The Science Behind A Good Night’s Sleep: Sleep-Wake Cycles Explained

Creating new memories but not at the expense of other

The study helps find answers to why the creation of new memories does not erase the old ones; for example, learning to play an instrument without forgetting to ride a car.

“Our results suggest that the brain can multiplex distinct cognitive processes during sleep to facilitate continuous learning without interference,” the researchers wrote.

“We are proposing that the brain has this intermediate timescale that separates the new learning from the old knowledge.”

One of the key insights from this research is the brain’s ability to separate the two sleep substages which prevents a “catastrophic” forgetting of memories at the expense of previous memories.

“This finding provides a potential solution for the long-standing problem in both biological and artificial neural networks of preventing catastrophic interference while also enabling memory integration,” write the researchers.

The study results have encouraged the scientific community which hopes to see the results on humans that might lead to better memory enhancement techniques and also help train artificial intelligence.