Contextual Dynamics in Lexical Encoding across the Aging Spectrum: A Simulation Study
Why this work is in the frame
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Bibliographic record
Abstract
The field of psycholinguistics has recently questioned the primacy of word frequency (WF) in influencing word recognition and production, focusing on the importance of a word’s contextual diversity (CD). WF is operationalized 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 in 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 (see Jones, Johns, & Dye, 2017 for a review). These findings support a cognitive mechanism based on the principle of likely need over the principle of repetition in lexical organization. In the current study, we trained the semantic distinctiveness model of Johns (2021) 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 250,000 participants from the Brysbaert, et al. (2019) norms, consisting of approximately 57,000 words across six age bands ranging from ages 10-60. 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 organization and environmental information across aging.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it