Generating structure from experience: A retrieval-based model of language processing.
Bibliographic record
Abstract
Standard theories of language generally assume that some abstraction of linguistic input is necessary to create higher level representations of linguistic structures (e.g., a grammar). However, the importance of individual experiences with language has recently been emphasized by both usage-based theories (Tomasello, 2003) and grounded and situated theories (e.g., Zwaan & Madden, 2005). Following the usage-based approach, we present a formal exemplar model that stores instances of sentences across a natural language corpus, applying recent advances from models of semantic memory. In this model, an exemplar memory is used to generate expectations about the future structure of sentences, using a mechanism for prediction in language processing (Altmann & Mirković, 2009). The model successfully captures a broad range of behavioral effects-reduced relative clause processing (Reali & Christiansen, 2007), the role of contextual constraint (Rayner & Well, 1996), and event knowledge activation (Ferretti, Kutas, & McRae, 2007), among others. We further demonstrate how perceptual knowledge could be integrated into this exemplar-based framework, with the goal of grounding language processing in perception. Finally, we illustrate how an exemplar memory system could have been used in the cultural evolution of language. The model provides evidence that an impressive amount of language processing may be bottom-up in nature, built on the storage and retrieval of individual linguistic experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".