Relations of lexical access to neural implementation and syntactic encoding
Bibliographic record
Abstract
How can one conceive of the neuronal implementation of the processing model we proposed in our target article? In his commentary (Pulvermüller 1999, reprinted here in this issue), Pulvermüller makes various proposals concerning the underlying neural mechanisms and their potential localizations in the brain. These proposals demonstrate the compatibility of our processing model and current neuroscience. We add further evidence on details of localization based on a recent meta-analysis of neuroimaging studies of word production (Indefrey & Levelt 2000). We also express some minor disagreements with respect to Pulvermüller's interpretation of the "lemma" notion, and concerning his neural modeling of phonological code retrieval. Branigan & Pickering discuss important aspects of syntactic encoding, which was not the topic of the target article. We discuss their well-taken proposal that multiple syntactic frames for a single verb lemma are represented as independent nodes, which can be shared with other verbs, such as accounting for syntactic priming in speech production. We also discuss how, in principle, the alternative multiple-frame-multiple-lemma account can be tested empirically. The available evidence does not seem to support that account.
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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 | 0.000 | 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 | 0.000 | 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".