<ul><li>A neuro-inspired approach is proposed to compress temporal sequences into context-tagged chunks representing recurring structural units.</li><li>Tags generated during an offline sleep phase serve as compact references to past experiences for learners.</li><li>Temporal chunking shows promise in enhancing learning efficiency in resource-constrained settings according to preliminary results.</li><li>A small-scale human study using a Serial Reaction Time Task supports the idea of structural abstraction and transfer of learned context tags across related tasks.</li></ul>

Temporal Chunking Enhances Recognition of Implicit Sequential Patterns

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