Revisiting the novelty effect: When familiarity, not novelty, enhances memory.
Bibliographic record
Abstract
Reports of superior memory for novel relative to familiar material have figured prominently in recent theories of memory. However, such novelty effects are incongruous with long-standing observations that familiar items are remembered better. In 2 experiments, we explored whether this discrepancy was explained by differences in the type of familiarity under consideration or by differences in the difficulty of discriminating targets from lures, which may lead to source confusion for familiar but not novel targets. In Experiment 1, we directly tested whether previously observed novelty effects were the result of novelty, discrimination demands, or both. We used linguistic materials (proverbs) to replicate the novelty effect but found that it occurred only when familiar items were subject to source confusion. In Experiment 2, to examine better how novelty influences episodic memory, we used experimentally familiar, pre-experimentally familiar, and novel proverbs in a paradigm designed to overcome discrimination demand confounds. Memory was better for both types of familiar proverbs. These results indicate that familiarity, not novelty, leads to better episodic memory for studied items, regardless of whether familiarity is experimentally induced or based on prior semantic knowledge. We argue that proposals that state that information is encoded better if it is novel are based on over-generalizations of effects arising from the distinctiveness of novel materials.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".