Hypothesis: Cognitively-Aligned Textual Intelligence via Patom Theory and Minimum Description Length Principles

1. Introduction

Contemporary approaches to natural language understanding (NLU) are dominated by large-scale statistical models, particularly deep learning architectures trained on vast corpora. However, these systems often lack explainability, suffer from generalization errors, and deviate substantially from human cognitive processes. This chapter proposes a hypothesis that seeks to align computational textual intelligence with human cognition by integrating two complementary frameworks: Patom Theory (Ball, 2020) and the Minimum Description Length (MDL) principle (Grünwald, 2007). We posit that semantic understanding can be achieved through the discovery and reuse of compressive patterns that correspond to cognitively plausible semantic representations.

2. Background and Motivation

2.1 Patom Theory

Patom Theory (Ball, 2020) offers a biologically inspired model of brain function in language processing. It suggests that the brain stores, matches, and retrieves meaning through hierarchical, bidirectional sets of patterns called patoms. Unlike symbolic or statistical models, Patom Theory treats language understanding as a process of recognition rather than computation, emphasizing bottom-up and top-down semantic pattern matching through a meaning matcher.

2.2 MDL Principle

The MDL principle (Grünwald, 2007; Rissanen, 1983) is rooted in information theory and formalizes the notion that the best explanation for data is the one that enables its shortest possible description. In the context of textual analysis, MDL can be operationalized through the identification of thematic motifs or compressed representations that minimize the total cost of the hypothesis (L_H) and the data given the hypothesis (L_D). Extensions using algorithmic complexity methods, such as the Block Decomposition Method (Zenil et al., 2019), enable practical estimation of Kolmogorov complexity in structured text.

2.3 Limitations of Current Models

State-of-the-art transformer-based models (Vaswani et al., 2017) excel in benchmarked tasks but remain largely uninterpretable and cognitively implausible. Their reliance on token-level prediction and distributional semantics often leads to brittle reasoning, hallucination, and failures in context-sensitive understanding (Bender et al., 2021).

3. Hypothesis Statement

Human-like textual intelligence can be achieved through a computational framework that integrates Patom Theory’s recognition-based, pattern-linked semantic memory structures with Minimum Description Length principles for discovering and compressing the most semantically efficient representations of meaning. This synthesis enables scalable, explainable, and cognitively grounded natural language understanding that surpasses probabilistic or syntactic-only approaches in fidelity, traceability, and structural generalization.

4. Theoretical Justification

4.1 Pattern-Based Semantics

Both Patom Theory and MDL-driven compression frameworks converge on the idea that understanding is achieved through structural recognition and reuse. Patom Theory’s meaning matcher maps input text to a pre-existing semantic structure via pattern linkage, while MDL selects the representation that compresses the input text most efficiently. In both cases, meaning arises from structure, not from symbol manipulation or statistical correlation.

4.2 Reconstructability and Traceability

A key benefit of this integration is the ability to reconstruct meaning: Patom Theory emphasizes regenerating sentences from stored meaning structures, while MDL-based frameworks support recomputability by design. This property facilitates auditability, transparency, and semantic explainability, essential for domains like law, policy, and regulatory analysis.

4.3 Hierarchical Composition

Patom Theory models cognition using composable, recursive patterns. Similarly, MDL-driven thematic motifs can be recursively structured into higher-level MetaThemes, enabling cross-document, cross-context generalization. This allows the system to scale from sentence-level parsing to document-level discourse modeling.

5. Operational Implications

The integration of Patom Theory and MDL principles enables the construction of a system with the following properties:

  • Context-sensitive semantic retrieval via structural similarity
  • Generative summarization and theme abstraction using compressive motifs
  • Explainable motif inference for human-aligned interaction
  • Cross-context generalization via structural abstraction and recombination

This architecture is particularly suitable for complex text analysis tasks where human reasoning, legal interpretation, or policy synthesis is required.

6. Methodological Considerations

Implementation of the hypothesis requires:

  • A BDM-based complexity estimator (e.g., using the pybdm library)
  • A motif discovery pipeline based on structural compression (Zenil et al., 2019)
  • A meaning matcher framework inspired by Role and Reference Grammar (Van Valin & LaPolla, 1997)
  • A thematic summarization layer with recomputability and version control

Empirical validation will require benchmark comparisons against transformer models and human-annotated thematic summaries.

7. Addendum: Cognitive Alignment with CIv4+5v2.1

The synthesis presented here reinforces and extends the Cybernetic Intelligence v4+5v2.1 hypothesis by adding a neurocognitive justification for its architecture:

  • Module ① (Motif Discovery) aligns with Patom Theory’s pattern recognition and semantic set instantiation processes.
  • Module ② (Thematic Memory Abstraction) corresponds to the compositional memory nets of Patom Theory, where patoms form reusable, meaning-linked structures.
  • Module ③ (Context-Sensitive Navigation) is mirrored in the meaning matcher and bidirectional matching algorithms of Patom.

Moreover, Patom Theory validates our rejection of symbolic or token-level parsing in favor of structurally grounded meaning inference, and supports the cybernetic feedback loop central to CIv4+5 by demonstrating how semantic resolution operates via dynamic pattern interaction.

The result is a more biologically grounded, linguistically rigorous, and computationally viable foundation for building cybernetic intelligence systems.

8. Conclusion

This hypothesis bridges the gap between cognitive neuroscience, linguistic theory, and algorithmic information theory. By unifying Patom Theory’s neurosemantic insights with the MDL principle’s formal rigor, we establish a pathway toward building cognitively-aligned, explainable textual intelligence systems. Future work will involve implementing and validating this integrated framework across diverse domains, beginning with legal and policy documents.

References

  • Ball, J. (2020). How Brains Work: Patom Theory’s Support from RRG Linguistics. Researchers.One. https://www.researchers.one/article/2020-04-04
  • Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. In Proceedings of FAccT ’21.
  • Grünwald, P. D. (2007). The Minimum Description Length Principle. MIT Press.
  • Rissanen, J. (1983). A Universal Prior for Integers and Estimation by Minimum Description Length. Annals of Statistics.
  • Van Valin, R. D., & LaPolla, R. J. (1997). Syntax: Structure, Meaning and Function. Cambridge University Press.
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). Attention is All You Need. Advances in Neural Information Processing Systems.
  • Zenil, H., Kiani, N. A., & Tegnér, J. (2019). Low Algorithmic Complexity Entropy-Deceiving Graphs. Physical Review E, 100(1), 012308.