The effect of contextual and semantic diversity in lexical and conceptual access: evidence from a picture-word semantic congruency task
Bibliographic record
Abstract
Corpus-based models of lexical strength, such as contextual and semantic diversity, challenge traditional word frequency measures as the main organising principle of the lexicon. Diversity models, which capitalise on language usage, consistently outperform word frequency in predicting lexical behaviour. However, most evidence for this theoretical position comes from “shallow” tasks, like lexical decision or naming, with long stimulus presentation times. We conducted exploratory secondary analyses using data from Antal & de Almeida (2024) to investigate the time-course of language use on lexical-semantic access in a semantically “deep” task. We modeled behavioural data from a masked picture-word congruency task with “brief” (60 ms) and “long” (200 ms) presentation durations with contextual and semantic diversity measures from a 55-billion-word corpus from Reddit. Results suggest that lexical and conceptual access are driven by a shared mechanism operating based on word usage context, advancing our understanding of the organisation of conceptual knowledge in semantic memory.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".