Memory in multiple sclerosis: Contextual encoding deficits
Bibliographic record
Abstract
Long-term memory (LTM) is one of the diverse cognitive functions adversely affected by multiple sclerosis (MS). The LTM deficits have often been attributed to failure of retrieval, whereas encoding processes are presumed intact. However, support for this view comes primarily from studies in which encoding and retrieval operations were not investigated systematically. In the current study, we used an encoding specificity paradigm to examine the robustness of encoding in MS and to specifically evaluate the impact of the disease on contextual memory. We hypothesized that persons with MS would exhibit a selective impairment in retrieving items from LTM when required to generate new cue-target associations at encoding, but not when cues held a strong preexisting relationship to the targets. The findings supported the hypotheses. We conclude that the mnemonic deficits associated with MS affect both encoding and retrieval. Specifically, problems with binding of contextual information at encoding impair effective retrieval of memories. Nonetheless, access to these memories can be gained through preexisting associations organized in the semantic network.
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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 | 0.000 | 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 | 0.000 |
| 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".