Prior Experience Supports New Learning of Relations in Aging
Bibliographic record
Abstract
This work examined whether semantically relevant schemas could facilitate learning in the transverse patterning (TP) task, which requires participants to learn the value of each stimulus in relation to the stimulus with which it is paired (e.g., A wins over B, B wins over C, C wins over A). Younger and older adults received the standard TP in isolation (alone condition), with additional sessions (practice condition), or with 2 TP sessions, which used familiar stimuli with known relations (e.g., rock-paper-scissors, semantic condition). Accuracy improved when training was provided within the context of a previously known relational framework, beyond the benefits obtained with extended practice with the task. When levels of education and vocabulary scores were considered as covariates, age-related deficits in accuracy were observed in the alone and practice conditions but were eliminated in the semantic condition. Extended practice and appealing to prior knowledge improved explicit awareness for the stimulus contingencies for each age-group. Thus, age-related deficits in learning relations among items may be remediated using existing relational information within semantic memory as an analog for new learning.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".