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Human Brain Compresses Working Memories into Low-Res ‘Summaries’

Posted on by Lawrence Tabak, D.D.S., Ph.D.

Stimulus images are disks of angled lines. A thought bubble shows similar angles in her thoughts
Credit: Adapted from Kwak Y., Neuron (2022)

You have probably done it already a few times today. Paused to remember a password, a shopping list, a phone number, or maybe the score to last night’s ballgame. The ability to store and recall needed information, called working memory, is essential for most of the human brain’s higher cognitive processes.

Researchers are still just beginning to piece together how working memory functions. But recently, NIH-funded researchers added an intriguing new piece to this neurobiological puzzle: how visual working memories are “formatted” and stored in the brain.

The findings, published in the journal Neuron, show that the visual cortex—the brain’s primary region for receiving, integrating, and processing visual information from the eye’s retina—acts more like a blackboard than a camera. That is, the visual cortex doesn’t photograph all the complex details of a visual image, such as the color of paper on which your password is written or the precise series of lines that make up the letters. Instead, it recodes visual information into something more like simple chalkboard sketches.

The discovery suggests that those pared down, low-res representations serve as a kind of abstract summary, capturing the relevant information while discarding features that aren’t relevant to the task at hand. It also shows that different visual inputs, such as spatial orientation and motion, may be stored in virtually identical, shared memory formats.

The new study, from Clayton Curtis and Yuna Kwak, New York University, New York, builds upon a known fundamental aspect of working memory. Many years ago, it was determined that the human brain tends to recode visual information. For instance, if passed a 10-digit phone number on a card, the visual information gets recoded and stored in the brain as the sounds of the numbers being read aloud.

Curtis and Kwak wanted to learn more about how the brain formats representations of working memory in patterns of brain activity. To find out, they measured brain activity with functional magnetic resonance imaging (fMRI) while participants used their visual working memory.

In each test, study participants were asked to remember a visual stimulus presented to them for 12 seconds and then make a memory-based judgment on what they’d just seen. In some trials, as shown in the image above, participants were shown a tilted grating, a series of black and white lines oriented at a particular angle. In others, they observed a cloud of dots, all moving in a direction to represent those same angles. After a short break, participants were asked to recall and precisely indicate the angle of the grating’s tilt or the dot cloud’s motion as accurately as possible.

It turned out that either visual stimulus—the grating or moving dots—resulted in the same patterns of neural activity in the visual cortex and parietal cortex. The parietal cortex is a part of the brain used in memory processing and storage.

These two distinct visual memories carrying the same relevant information seemed to have been recoded into a shared abstract memory format. As a result, the pattern of brain activity trained to recall motion direction was indistinguishable from that trained to recall the grating orientation.

This result indicated that only the task-relevant features of the visual stimuli had been extracted and recoded into a shared memory format. But Curtis and Kwak wondered whether there might be more to this finding.

To take a closer look, they used a sophisticated model that allowed them to project the three-dimensional patterns of brain activity into a more-informative, two-dimensional representation of visual space. And, indeed, their analysis of the data revealed a line-like pattern, similar to a chalkboard sketch that’s oriented at the relevant angles.

The findings suggest that participants weren’t actually remembering the grating or a complex cloud of moving dots at all. Instead, they’d compressed the images into a line representing the angle that they’d been asked to remember.

Many questions remain about how remembering a simple angle, a relatively straightforward memory formation, will translate to the more-complex sets of information stored in our working memory. On a technical level, though, the findings show that working memory can now be accessed and captured in ways that hadn’t been possible before. This will help to delineate the commonalities in working memory formation and the possible differences, whether it’s remembering a password, a shopping list, or the score of your team’s big victory last night.

Reference:

[1] Unveiling the abstract format of mnemonic representations. Kwak Y, Curtis CE. Neuron. 2022, April 7; 110(1-7).

Links:

Working Memory (National Institute of Mental Health/NIH)

The Curtis Lab (New York University, New York)

NIH Support: National Eye Institute


Brain in Motion

Posted on by Dr. Francis Collins

Credit: Itamar Terem, Stanford University, Palo Alto, CA, and Samantha Holdsworth, University of Auckland, New Zealand

Though our thoughts can wander one moment and race rapidly forward the next, the brain itself is often considered to be motionless inside the skull. But that’s actually not correct. When the heart beats, the pumping force reverberates throughout the body and gently pulsates the brain. What’s been tricky is capturing these pulsations with existing brain imaging technologies.

Recently, NIH-funded researchers developed a video-based approach to magnetic resonance imaging (MRI) that can record these subtle movements [1]. Their method, called phase-based amplified MRI (aMRI), magnifies those tiny movements, making them more visible and quantifiable. The latest aMRI method, developed by a team including Itamar Terem at Stanford University, Palo Alto, CA, and Mehmet Kurt at Stevens Institute of Technology, Hoboken, NJ. It builds upon an earlier method developed by Samantha Holdsworth at New Zealand’s University of Auckland and Stanford’s Mahdi Salmani Rahimi [2].