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Increasing semantic relationships with old information accelerates learning and enhances memory for new information and increases the likelihood of both old and new information being recalled together.

A novel deep-learning-based computational method using object recognition to quantify visual context in naturalistic, multimodal stimuli demonstrates that a concept's perceived abstractness or concreteness dynamically depends on its visual context.

The minimal twister sister (TS)-like self-cleaving ribozymes, revealed by deep mutational scanning, represent some of the very few human/primate ribozymes.

Interactions between attention systems and the default mode network are linked to individual differences in trait and state-level experience and cognition.

Human intracranial electroencephalography recordings across 177 participants and four diverse episodic memory experiments demonstrate how the anterior insula node of the salience network orchestrates dynamics of large-scale brain networks.