A direct replication and extension of Popp and Serra (2016, experiment 1): better free recall and worse cued recall of animal names than object names, accounting for semantic similarity
Bibliographic record
Abstract
Introduction: Free recall tends to be better for names of animate concepts such as animals than for names of inanimate objects. In Popp and Serra's 2016 article, the authors replicated this "animacy effect" in free recall but when participants studied words in pairs (animate-animate pairs intermixed with inanimate-inanimate pairs) and were tested with cued recall, performance was better for inanimate-inanimate pairs than for animate-animate pairs ("reverse animacy"). We tested the replicability of this surprising effect and one possible explanation for the effect (semantic similarity). Methods: = 150), we manipulated the within-category semantic similarity of animal and object wordlists. Results: AIn Experiment 1, just as in Popp and Serra, we observed an animacy effect for free recall and a reverse animacy effect for cued recall. Unlike Popp and Serra, we found that controlling for interference effects rendered the reverse animacy effect non-significant. We took this as evidence that characteristics of the stimulus sets (e.g., category structure, within-category similarity) may play a role in animacy and reverse animacy effects. In Experiment 2, in three out of our four samples, we observed reverse animacy effects when within-category similarity was higher for animals and when within-category similarity was equated for animals and objects. Discussion: Our results suggest that the reverse animacy effect observed in Popp and Serra's 2016 article is a robust and replicable effect, but that semantic similarity alone cannot explain the effect.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".