A Distributional and Sensorimotor Analysis of Noun and Verb Fluency
Bibliographic record
Abstract
Category verbal fluency tasks, where participants are asked to produce words according to a semantic category, are typically noun-based (e.g., animals). While insights about the integrity and retrieval of semantic knowledge have been obtained by analyzing the ordinal variances of word production in these noun-based fluency tasks, focusing exclusively on noun categories ignores the semantic knowledge contributed by other semantic/grammatical categories, especially verbs. To better understand the representational differences of nouns and verbs within the mental lexicon, the current study conducted and contrasted different noun- and verb-based fluency tasks. By analyzing the use of different lexical information sources, including word frequency, and context and order similarity derived from a computational model of lexical semantics (BEAGLE; Jones & Mewhort, 2007), and the use of perceptual information derived from the recently released sensorimotor norms (Lynott, Connell, Brysbaert, Brand, & Carney, 2020), it was found that these information sources consistently distinguished noun and verb retrieval, signaling the underlying distributional and sensorimotor representational differences for these two semantic/grammatical categories. The results demonstrate the essential and integral role that distributional and grounded/embodied models play in understanding language and cognition, and highlight the usefulness of verbal fluency tasks in exploring theoretical questions in memory and psycholinguistics.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".