Distinguishing familiarity from fluency for the compound word pair effect in associative recognition
Bibliographic record
Abstract
We examined whether processing fluency contributes to associative recognition of unitized pre-experimental associations. In Experiments 1A and 1B, we minimized perceptual fluency by presenting each word of pairs on separate screens at both study and test, yet the compound word (CW) effect (i.e., hit and false-alarm rates greater for CW pairs with no difference in discrimination) did not reduce. In Experiments 2A and 2B, conceptual fluency was examined by comparing transparent (e.g., hand bag) and opaque (e.g., rag time) CW pairs in lexical decision and associative recognition tasks. Lexical decision was faster for transparent CWs (Experiment 2A) but in associative recognition, the CW effect did not differ by CW pair type (Experiment 2B). In Experiments 3A and 3B, we examined whether priming that increases processing fluency would influence the CW effect. In Experiment 3A, CW and non-compound word pairs were preceded with matched and mismatched primes at test in an associative recognition task. In Experiment 3B, only transparent and opaque CW pairs were presented. Results showed that presenting matched versus mismatched primes at test did not influence the CW effect. The CW effect in yes-no associative recognition is due to reliance on enhanced familiarity of unitized CW pairs.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".