Lexical Preactivation in Basic Linguistic Phrases
Bibliographic record
Abstract
Many previous studies have shown that predictable words are read faster and lead to reduced neural activation, consistent with a model of reading in which words are activated in advance of being encountered. The nature of such preactivation, however, has typically been studied indirectly through its subsequent effect on word recognition. Here, we use magnetoencephalography to study the dynamics of prediction within serially presented adjective-noun phrases, beginning at the point at which the predictive information is first available to the reader. Using corpus transitional probability to estimate the predictability of a noun, we found an increase in activity in the left middle temporal gyrus in response to the presentation of highly predictive adjectives (i.e., adjectives that license a strong noun prediction). Moreover, we found that adjective predictivity and expected noun frequency interacted, such that the response to the highly predictive adjectives (e.g., stainless) was modulated by the frequency of the expected noun (steel). These results likely reflect preactivation of nouns in highly predictive contexts. The fact that the preactivation process was modulated by the frequency of the predicted item is argued to provide support for a frequency-sensitive lexicon.
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How this classification was reachedexpand
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 | 0.001 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".