Reducing Retroactive Interference through the Use of Different Encoding Techniques: An Exploration of Pre-Test/Post-Test Analyses
Bibliographic record
Abstract
Retroactive interference (RI) in list learning occurs when the learning of a second list of words interferes with the recall of the first learned list. Having the lists be thematically different can reduce retroactive interference within list learning; however, this study demonstrates how RI can be reduced when the lists contain similar words. Words can be organized by way of encoding (verbally and visually). Interference occurs when two lists are encoded the same way; therefore, encoding two lists in different ways reduces RI. Ninety-three participants were randomly assigned to 1 of 6 conditions. Participants who encoded one list visually and one list verbally retained more words on final recall from list one, than participants who encoded both list the same way. Two control conditions were used to assess highest recall. The results demonstrated that RI can be reduced when two lists are encoded in different ways. A second experiment using modified methods was also conducted with similar results.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".