Computational Models of How Humans Learn Patterns and Abstractions from Sequential Data
A doctoral thesis published on arXiv presents a computational framework arguing that 'chunking' — breaking sensory streams into recurring parts — and hierarchical abstraction are fundamental mechanisms underlying human sequence learning and generalization. The work combines behavioral experiments, normative modeling, and comparisons with large language models to show how humans exploit pattern redundancies for compression and transfer. The findings suggest these principles could inform the design of more human-like machine learning systems.
The thesis, authored by Shuchen Wu and submitted to arXiv under machine learning, investigates how cognition segments high-dimensional sensory input into structured, reusable units. In the first major project, behavioral experiments using a serial reaction time task demonstrated that humans adapt to underlying chunk structures while balancing speed and accuracy trade-offs. The author then developed computational models capable of learning and parsing sequences chunk by chunk, framing chunking normatively as a rational strategy for discovering recurring patterns and nested hierarchies. A second project extended this framework to abstract sequences, introducing a non-parametric hierarchical variable model that learns both concrete chunks and abstract symbolic variables, capturing invariant patterns across contexts. This model was shown to closely mirror human learning behavior and was benchmarked against large language models. Collectively, the thesis argues that chunking and abstraction — operating from simple to complex and concrete to abstract — constitute sufficient computational principles for structured knowledge acquisition in hierarchically organized sequences.
What's missing
The thesis does not detail the size or demographic diversity of the human participant samples used in behavioral experiments, which limits generalizability claims. Open questions include whether the proposed models scale to truly high-dimensional, naturalistic sensory data and how they perform relative to state-of-the-art sequence models beyond the LLM comparisons mentioned.
What different sources said
- arXiv cs.LGCenter
Learning Patterns and Abstractions from Perceptual Sequences
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