Computational Models of How Humans Learn Patterns and Abstractions from Sequential Data
A thesis proposes that chunking and abstraction are fundamental computational principles enabling humans to learn structured knowledge from sequential sensory data. The research combines behavioral experiments in serial reaction time tasks with computational models that learn hierarchical patterns, from concrete sequences to abstract symbolic structures. Understanding these mechanisms has implications for artificial intelligence, learning theory, and how cognitive systems generalize and transfer knowledge.
The research investigates how cognition breaks down high-dimensional sensory streams into meaningful parts through chunking—a process of segmenting sequences into reusable primitives. The first part examines factors influencing chunking in human serial reaction time tasks, showing that humans adaptively balance speed and accuracy while learning underlying chunks. The author developed computational models that learn chunks and parse sequences hierarchically, proposing chunking as a rational strategy for discovering recurring patterns and nested structures. The second part extends this to abstract sequences, proposing a non-parametric hierarchical variable model that learns both concrete chunks and abstract variables to uncover invariant symbolic patterns. The thesis demonstrates that these models exhibit similarities to human learning and provides comparisons with large language models, suggesting that chunking and abstraction as simple principles enable efficient knowledge acquisition across hierarchical levels of complexity.
What's missing
The abstract does not specify the empirical validation metrics, sample sizes in behavioral experiments, or quantitative comparisons with baseline models and large language models. The specific datasets used for testing hierarchical learning and the computational complexity of the proposed models are not detailed.
What different sources said
- arXiv cs.LGCenter
Learning Patterns and Abstractions from Perceptual Sequences
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