The role of familiarity in associative recognition of unitized compound word pairs
Bibliographic record
Abstract
This study examined the effect of unitization and contribution of familiarity in the recognition of word pairs. Compound words were presented as word pairs and were contrasted with noncompound word pairs in an associative recognition task. In Experiments 1 and 2, yes-no recognition hit and false-alarm rates were significantly higher for compound than for noncompound word pairs, with no difference in discrimination in both within- and between-subject comparisons. Experiment 2 also showed that item recognition was reduced for words from compound compared to noncompound word pairs, providing evidence of the unitization of the compound pairs. A two-alternative forced-choice test used in Experiments 3A and 3B provided evidence that the concordant effect for compound word pairs was largely due to familiarity. A discrimination advantage for compound word pairs was also seen in these experiments. Experiment 4A showed that a different pattern of results is seen when repeated noncompound word pairs are compared to compound word pairs. Experiment 4B showed that memory for the individual items of compound word pairs was impaired relative to items in repeated and nonrepeated noncompound word pairs, and Experiment 5 demonstrated that this effect is eliminated when the elements of compound word pairs are not unitized. The concordant pattern seen in yes-no recognition and the discrimination advantage in forced-choice recognition for compound relative to noncompound word pairs is due to greater reliance on familiarity at test when pairs are unitized.
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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 | 0.001 | 0.007 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".