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 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) | 0.006 | 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".