The relationship between language experience variables and the time course of spoken word recognition.
Bibliographic record
Abstract
During spoken word recognition, words that are related phonologically (e.g., dog and dot) and words that are related semantically (e.g., dog and bear) are known to become active within the first second of word recognition. The time course of activation and resolution of these competing words changes as a function of linguistic knowledge. This preregistered study aimed to examine how a less commonly used linguistic predictor, percent lifetime language exposure, affects the time course of target and competitor activation in an eye-tracking visual world paradigm. Lifetime exposure was expected to capture variability in the representations and processes that contribute to individual differences in spoken word recognition. Results show that when putting lifetime exposure to French on a scale, more lifetime exposure was related to target fixations and slightly related to early phonological coactivation, but not related to semantic coactivation. These analyses demonstrate how generalized additive mixed models might help examine time course data with more continuous linguistic variables. Exploratory analyses looked at the amount of variance captured by three linguistic experience predictors (lifetime French exposure, recent French exposure, French vocabulary) on indices of target, phonological, and semantic fixations and identified vocabulary size as most frequently explaining significant variance, but the pattern of results did not differ from those of lifetime language exposure. These findings suggest that lifetime language exposure may not fully capture subtle differences in linguistic experience that affect lexical coactivation such as those brought upon by differences in exposure trajectories across the lifetime or differences in the setting of language exposure. (PsycInfo Database Record (c) 2025 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 | 0.001 | 0.012 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".