Conceptual relatedness promotes memory generalization at the cost of detailed recollection
Bibliographic record
Abstract
An adaptive memory system is one that allows us to both retrieve detailed memories as well as generalize knowledge about our past, the latter termed memory generalization and is useful for making inferences about new situations. Research has indicated that memory generalization relies on forming knowledge structures by integrating experiences with shared encountered elements. Whether memory generalization occurs more readily when experiences also have elements that share established (conceptual) information is less clear. It is also unclear if engaging in memory generalization during learning comes at the cost of retrieving detailed memories, the other function of episodic memory. To address these two knowledge gaps, we paired a modified version of the acquired equivalence task with a recognition memory test. Across three experiments, participants first learned a series of overlapping object-scene pairs (A-X, B-X and A-Y) in which half of the overlapping pairs contained conceptually-related objects (e.g., A-pencil; B-scissors; conceptual condition) and the other half contained unrelated objects (neutral condition). Participants ability to generalize to new overlapping object-scene pairs (B-Y) as well as not-learned but semantically-related objects was measured. Finally, participants completed a recognition memory test that included the encoded objects, perceptually similar lures or new foil objects. Across all experiments, we found higher rates of generalization but reduced detailed memory (indexed by increased false alarms to lure objects) for information learned in the conceptual than neutral condition. These results suggest the presence of conceptual knowledge biases an individual towards a generalization function of memory, which comes at the expense of detailed recollection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".