Shared Features Dominate the Number-Of-Features Effect
Bibliographic record
Abstract
When asked to list semantic features for concrete concepts, participants list many features for some concepts and few for others.Concepts with many semantic features have been reported to be processed faster in lexical decision, naming, and semantic decision tasks (Pexman, Holyk, & Monfils, 2003;Pexman, Lupker, & Hino, 2002).Using a much larger and better controlled set of items, we replicated the numberof-features (NoF) effect in both lexical and semantic decision (Experiment 1).We then investigated the relationship between NoF and feature distinctiveness.Shared features are those which appear in many concepts ( ) whereas distinctive features appear in few concepts ( ).Keeping total NoF constant, decision latencies were shorter for concepts with many shared features versus those with few shared features in lexical and semantic decision, with a larger difference obtaining in semantic decision (Experiment 2).Manipulating shared or distinctive features to create low versus high levels of NoF revealed a larger NoF advantage for concepts with many shared features than for those with many distinctive features (Experiment 3).It is concluded that shared features play a dominant role in the NoF effect, at least in lexical and semantic decision tasks.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.001 |
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".