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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.
Instance memory models, grounded in individual experience, now offer a computational framework for understanding natural language. This approach bridges cognitive psychology and language sciences by revealing how specific experiences shape general language use.
Area of Science:
- Cognitive Psychology
- Computational Linguistics
- Language Sciences
Background:
- Instance theory posits individual experiences as fundamental units of knowledge.
- Previous applications of instance theory to language were limited to small-scale experiments.
- Large-scale computational modeling has enabled broader application of instance theory to natural language.
Purpose of the Study:
- To demonstrate the applicability of instance memory models to natural language processing at scale.
- To provide a mechanistic explanation for usage-based theories of language.
- To highlight the role of specific language experiences in general language behavior.
Main Methods:
- Application of large-scale computational modeling based on instance theory.
- Analysis of natural language data using instance memory frameworks.
- Connecting computational models to usage-based theories of language.
Main Results:
- Successful application of instance models to the complex problem of natural language.
- Demonstration of how general language knowledge can emerge from parallel retrieval of specific experiences.
- Instance models provide a mechanistic basis for usage-based language theories.
Conclusions:
- Instance memory models offer a valuable framework for understanding language as a dynamic, context-embedded process.
- This approach deepens our understanding of the relationship between specific language experiences and general language behavior.
- Instance models serve to bridge the gap between cognitive psychology and the language sciences.
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