Mnemonic Demand Reconfigures the Neural Architecture of Memory Encoding
Memory encoding must accommodate increasingly demanding loads, yet it remains unclear whether the underlying brain state is preserved or adaptively reconfigured as load increases. Here, we recorded EEG from 111 participants performing a visual memory task in which they encoded lists of real images ranging from 1 to 128 items, spanning low to high mnemonic demands, followed by a recognition test.…
A recent study sheds light on how the brain's neural architecture adapts to increasing demands on memory encoding. Researchers recorded EEG readings from 111 participants while they engaged in a visual memory task involving lists of images ranging from 1 to 128 items, representing varying levels of cognitive load. Following the encoding phase, participants underwent a recognition test.
The key finding was that as the list size increased, the similarity of the set-size-specific interelectrode correlation patterns to a low-load reference configuration decreased steadily. This pattern shift was most pronounced for set sizes of 8 or higher, where the neural configuration diverged significantly from the low-load configuration. A single rotating eigenvector was identified as capturing this transition, revealing a low-dimensional trajectory from low- to high-load neural geometry.
Notably, the smaller the rotation angle from the low-load template, the better the memory performance at higher loads. This evidence suggests that memory encoding undergoes adaptive reconfiguration as cognitive demands rise, following a structured neural trajectory. The findings provide valuable insights into how the brain dynamically adjusts its encoding architecture to accommodate increasing mnemonic demands.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
