Using retrieval contingencies to understand memory integration and inference
Bibliographic record
Abstract
Past work has yielded mixed insights into how people draw upon their memories to make flexible new inferences. Neuroimaging approaches have shown that memories can be combined during encoding to store never-experienced, inferential associations. By contrast, behavioural research has emphasized a retrieval-based mechanism in which separate, high-quality memories are recombined as inferences are needed. We hypothesized that there might be important individual differences to consider when reconciling these seemingly disparate findings. We set out to quantify these differences by measuring contingencies in people’s memory recall behaviour. In Experiment 1, we first compared the performance of three memory contingency metrics using simulations and data from a task known to induce dependency. In doing so, we developed a correction to remove biases associated with general memory performance to isolate the representational structure of memories, and we selected the highest-fidelity option—corrected Dependency—for subsequent analyses. Experiment 2 tested the sensitivity of our chosen metric: We manipulated the similarity across experiences to encourage integration for half of the memories. Consistent with prior work, we found that increasing the similarity between the two experiences yielded reliable recall dependency. Finally, in Experiment 3, we used memory dependencies to reveal individual differences in inference approaches: While indeed “separators” relied upon high-fidelity individual memories to make inferences, “integrators” made inferences just as accurately and more rapidly than separators, regardless of how well they recalled constituent experience details. Together, these findings highlight the importance of considering individual differences in memory representations when characterizing the mechanisms underlying flexible inference.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".