This study proposes a neuroscience-inspired approach to compress temporal sequences into context-tagged chunks. Each tag represents a recurring structural unit, or “community,” in the sequence, and is generated during offline sleep. These tags serve as concise references to past experiences, allowing learners to integrate information beyond the immediate input. We evaluate this idea in a controlled synthetic environment designed to expose the limitations of existing neural network-based sequential learners, such as recurrent neural networks (RNNs), when dealing with temporal patterns at multiple time scales. The results are preliminary, but suggest that temporal chunking can significantly improve learning efficiency in resource-constrained environments. A small-scale human pilot study using a chain reaction time task further supports the idea of structural abstraction. Although limited to a synthetic task, this study provides initial evidence that learned context tags can be transferred across related tasks, serving as an early proof-of-concept that offers potential future applications of transfer learning.
