Interference and the Representation of Order within Associations
Bibliographic record
Abstract
In the presence of interference, recall of pairs can critically depend on the diagnostic power of memory of the order of items within the pair. Models of pair memory make different assumptions about whether and how such order information is stored, from convolution-based models, which assume no explicit storage of order, to matrix models and several models that assume a pair is learned by concatenating the representations of the constituent items, which lead to perfect within-pair order memory (given retrieval of the pair). Here we investigate memory for associations and within-pair order by examining the relationship between forward and backward probes of pairs subject to order-dependent associative interference in a double-function list paradigm. Associative interference disrupted the high correlation between forward and backward recall accuracy that is typically observed in standard paired-associate learning, challenging matrix and concatenation-based models. However, participants could overcome some interference due to within-pair order ambiguity, challenging directionally ambiguous convolution-based models. Unexpectedly, the test-retest correlation was reduced for pairs under the influence of interference compared to control pairs. This finding is incompatible with all existing implementations of the model classes we consider. Any model must include an assumption that order encoding (but not retrieval) is unreliable, and the form of this additional mechanism may depend intimately on how a given model is designed. In sum, our findings suggest that within-pair order memory is neither poor nor perfect, pointing to a fallible mechanism for within-pair order learning in verbal association-memory tasks.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".