Is More Always Better for Verbs? Semantic Richness Effects and Verb Meaning
Bibliographic record
Abstract
We examined how several semantic richness variables contribute to verb meaning, across a number of tasks. Because verbs can vary in tense, and the manner in which tense is coded (i.e., regularity), we also examined how these factors moderated the effects of semantic richness. In Experiment 1 we found that age of acquisition (AoA), valence, arousal and embodiment predicted faster response times in LDT. In Experiment 2 we examined a particular semantic richness variable, verb embodiment, and found that it was moderated by tense and regularity. In Experiment 3a we found that AoA predicted faster response times in verb reading. Finally, in Experiment 3b, semantic diversity predicted response times in a past tense generation task, either facilitating or inhibiting responses for regular or irregular verbs, respectively. These results demonstrate that semantic richness variables contribute to verb meaning even when verbs are presented in isolation, and that these effects depend on several factors unique to verbs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".