Intact Learning of New Relations in Amnesia as Achieved through Unitization
Bibliographic record
Abstract
Hippocampal amnesia is defined by deficits in the binding of relations among items--a deficit captured by the transverse patterning (TP) task. Unitization is a processing mechanism that may allow amnesic patients to compensate for relational memory deficits. Amnesic patient D.A. demonstrated intact TP, and performance was maintained 1 month following training. Successful acquisition of relations occurred only when D.A. fused or integrated objects into a unified representation. D.A. did not acquire relations when he did not generate such integrated scenarios, and acquisition of relations was slowed when integration had to occur for novel stimuli. Amnesic patients K.C. and R.F.R. were tested to provide comparative data; K.C. and R.F.R. did not benefit from unitization, perhaps due to additional cortical damage. We propose that unitization requires visual imagery of multiple items that are fused/integrated; through the benefit of extended on-line maintenance, this fused representation is anchored to existing representations in semantic memory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".