Sequential dependencies in recognition memory are decision based
Bibliographic record
Abstract
Decision perseveration is consistently observed in recognition tests, such that judgements tend to repeat (e.g., "old" responses tend to follow "old" responses) across trials. This effect has been found across a range of testing styles, including old/new judgements, judgements of frequency, and confidence, and has been interpreted as reflecting the transfer of mnemonic information between trials. However, an alternative explanation that response repetition is rather the product of motor action perseveration has not yet been fully evaluated. Despite the range of response styles used across studies, repeat decisions have consistently been confounded with repeat motor responses. Across three experiments, the present study divorces decision repetition from motor priming, to determine whether decision perseveration maintains. Experiments 1 and 2 found that when participants switch hands between trials, decisions are still more likely to repeat than switch. Similarly, Experiment 3 found no difference in the influence of Previous Decision when mouse paths were able to repeat between trials compared with when they could not. In addition, all experiments show a speed advantage for repeating decisions that cannot be attributed to motor priming. We conclude that decision carryover during recognition tests is ultimately a decision-based effect. The results are discussed in terms of mnemonic models of information transfer.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 0.001 |
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".