Individuals and non-individuals in cognition and semantics: The mass/count distinction and quantity representation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Language is a sub-component of human cognition. One important, though often unattained goal for both cognitive scientists and linguists is to explicate how the meanings of words and sentences relate to the more general, non-linguistic, cognitive systems that are used to evaluate whether sentences are true or false. In the present paper, we explore one such relationship: an interface between the linguistic structures referring to individuals and non-individuals (specifically, count-nouns like ‘cows’ and mass-nouns like ‘beef’) and the non-linguistic cognitive systems that quantify and compare number and area. While humans may be flexible in how they use language across contexts, in two experiments using standard psychophysical testing we find that participants evaluate a count-noun sentence via numerical representations and evaluate a corresponding mass-noun sentence via non-numerical representations; consistent with a principled interface between language and cognition for evaluating these terms. This was the case even when the visual display was held constant across conditions and only the noun type was varied, further suggesting an important difference in how area and number, as well as count and mass nouns, are represented. These findings speak to issues concerning the semantics-cognition interface, the mass-count distinction, and the psychophysics of quantity representation.
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.
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 | 0.006 |
| 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 it