Contextual dynamics in lexical encoding across the ageing spectrum: A simulation study
Bibliographic record
Abstract
The field of psycholinguistics has recently questioned the primacy of word frequency (WF) in influencing word recognition and production, instead focusing on the importance of a word's contextual diversity (CD). WF is operationalised by counting the number of occurrences of a word in a corpus, while a word's CD is a count of the number of contexts that a word occurs in, with repetitions within a context being ignored. Numerous studies have converged on the conclusion that CD is a better predictor of word recognition latency and accuracy than frequency. These findings support a cognitive mechanism based on the principle of likely need over the principle of repetition in lexical organisation. In the current study, we trained the semantic distinctiveness model on communication patterns in social media platforms consisting of over 55-billion-word tokens and examined the ability of theoretically distinct models to explain word recognition latency and accuracy data from over 1 million participants from the Mandera et al. English Crowdsourding Project norms, consisting of approximately 59,000 words across six age bands ranging from ages 10 to 60 years. There was a clear quantitative trend across the age bands, where there is a shift from a social environment-based attention mechanism in the "younger" models, to a clear dominance for a discourse-based attention mechanism as models "aged." This pattern suggests that there is a dynamical interaction between the cognitive mechanisms of lexical organisation and environmental information that emerges across ageing.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".