Determining the optimal environmental information for training computational models of lexical semantics and lexical organization.
Bibliographic record
Abstract
Experiential theories of cognition propose that the external environment shapes cognitive processing, shifting emphasis from internal mechanisms to the learning of environmental structure. Computational modelling, particularly distributional models of lexical semantics (e.g., Landauer & Dumais, 1997) and models of lexical organization (e.g., Johns, 2021a), exemplifies this, highlights the influence of language experience on cognitive representations. While these models have been successful, comparatively less attention has been paid to the training materials used to train these models. Recent research has explored the role of social/communicatively oriented training materials on models of lexical semantics and organization (Johns, 2021a, 2021b, 2023, 2024), introducing discourse- and user-centred text training materials. However, determining the optimal training materials for these two model types remains an open question. This article addresses this problem by using experiential optimization (Johns, Jones, & Mewhort, 2019), which selects the materials that maximize model performance. This study will use experiential optimization to compare user-based and discourse-based corpora in optimizing models of lexical organization and semantics, offering insight into pathways towards integrating cognitive models in these areas. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".