A feasibility pilot study on using unitization to circumvent relational memory impairments in schizophrenia
Bibliographic record
Abstract
Relational memory (RM), the ability to associate individual items together or with a given context (Öngür et al., 2006), is disproportionately impaired in schizophrenia (Lepage et al., 2015; Öngür et al., 2006). The unitization strategy has been proven effective to circumvent RM-impairment in amnesic cases (D'Angelo et al., 2015; Ryan et al., 2013) and older adults (D’Angelo et al., 2016) when learning pair-relationships in the transverse-patterning (TP) task. The TP task requires learning relations between items, in which the value of an item depends on the item it is paired with (D'Angelo et al., 2016; Hanlon et al., 2005; Moses et al., 2008). Some studies have shown deficits in the relational-binding dependent TP task in schizophrenia (Rowland et al., 2007; Rowland et al., 2010; Spieker et al., 2013). Unitization, by combining disparate pieces of information into a single larger unit, is thought to support TP learning independently from RM-related processes, relying on semantic memory instead (Ryan et al., 2013).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".