The structure of prior knowledge enhances memory in experts by reducing interference
Bibliographic record
Abstract
The influence of prior knowledge on memory is ubiquitous, making the specific mechanisms of this relationship difficult to disentangle. Here, we show that expert knowledge produces a fundamental shift in the way that interitem similarity (i.e., the perceived resemblance between items in a set) biases episodic recognition. Within a group of expert birdwatchers and matched controls, we characterized the psychological similarity space for a set of well-known local species and a set of less familiar, nonlocal species. In experts, interitem similarity was influenced most strongly by taxonomic features, whereas in controls, similarity judgments reflected bird color. In controls, perceived episodic oldness during a recognition memory task increased along with measures of global similarity between items, consistent with classic models of episodic recognition. Surprisingly, for experts, high global similarity did not drive oldness signals. Instead, for local birds memory tracked the availability of species-level name knowledge, whereas for nonlocal birds, it was mediated by the organization of generalized conceptual space. These findings demonstrate that episodic memory in experts can benefit from detailed subcategory knowledge, or, lacking that, from the overall relational structure of concepts. Expertise reshapes psychological similarity space, helping to resolve mnemonic separation challenges arising from high interitem overlap. Thus, even in the absence of knowledge about item-specific details or labels, the presence of generalized knowledge appears to support episodic recognition in domains of expertise by altering the typical relationship between psychological similarity and memory.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".