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Instance memory models as a general computational framework for exploring language processing: bringing the lexicon
Brendan T Johns1, Randall K Jamieson2, Matthew J C Crump3
1McGill University, Montreal, Canada.
Linguistics Vanguard : Multimodal Online Journal
|July 24, 2026
Summary
Instance memory models, grounded in individual experience, are now scaled for natural language processing. This approach reveals how specific language experiences shape general language behavior, bridging cognitive psychology and language sciences.
Area of Science:
- Cognitive Science
- Computational Linguistics
- Psychology
Background:
- Instance models are established for memory, learning, and categorization.
- Instance theory posits individual experiences as fundamental knowledge units.
- Previous language applications were limited to small-scale experiments.
Purpose of the Study:
- To demonstrate the scalability of instance models for natural language.
- To provide a mechanistic basis for usage-based language theories.
- To explore language as a dynamic, context-embedded process.
Main Methods:
- Large-scale computational modeling of instance theory.
- Application to complex natural language problems.
- Analysis of specific language experience in general behavior.
Main Results:
- Successful large-scale application of instance models to natural language.
- Demonstration of how general language behavior emerges from specific experiences.
- Instance models offer a mechanistic link to usage-based theories.
Conclusions:
- Instance memory models are viable for large-scale natural language.
- This framework illuminates the role of experience in language.
- Instance models bridge cognitive psychology and language sciences.
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