Unitization supports lasting performance and generalization on a relational memory task: Evidence from a previously undocumented developmental amnesic case
Bibliographic record
Abstract
Recently, the amnesic case D.A. was shown to circumvent his relational memory impairments, as observed in the transverse patterning (TP) task, using a self-generated unitization strategy, and such performance benefits were maintained over extended delays (Ryan et al., 2013). "Unitization" encourages fusing of distinct items, through an action, into a single unit from which the relations among the items may then be derived. Here, we provide the first documentation of the developmental amnesic case, N.C., who presents with relatively circumscribed lesions to the extended hippocampal system, and with impaired episodic memory. Despite impairments on standard versions of TP, N.C. benefited from unitization, showed evidence of transfer to novel stimuli, and maintained his performance over extended delays. These findings suggest that self-generation is not a requirement for the successful implementation of unitization, and further provides the first evidence of rapid transfer and long-lasting success of a learning strategy in a human amnesic case.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".