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 (<has four legs>) whereas distinctive features appear in few concepts (<moos>).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 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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; both teacher heads agree on what is shown here.
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".