Confabulation in healthy aging is related to poor encoding and retrieval of over-learned information
Bibliographic record
Abstract
Normal aging is characterized by deficits that cross multiple cognitive domains including episodic memory and attention. Compared to young adults (YA), older adults (OA) not only show reduction in true memories, but also an increase in false memories. In this study we aim to elucidate how the production of confabulation is influenced by encoding and retrieval processes. We hypothesized that in OA, compared to YA, over-learned information interferes with the recall of specific, unique past episodes and this interference should be more prominent when a concurrent task perturbs the encoding of the episodes to be recalled. We tested this hypothesis using an experimental paradigm in which a group of OA and a group of YA had to recall three different types of story: a previously unknown story, a well-known fairy tale (Snow White), and a modified well-known fairy tale (Little Red Riding Hood is not eaten by the wolf), in three different experimental conditions: (1) free encoding and free retrieval; (2) Divided attention (DA) at encoding and free retrieval; and (3) free encoding and DA at retrieval. Results showed that OA produced significantly more confabulations than YA, particularly, in the recall of the modified fairy tale. Moreover, DA at encoding markedly increased the number of confabulations, whereas DA at retrieval had no effect on confabulation. Our findings reveal the implications of two phenomena in the production of confabulation in normal aging: the effect of poor encoding and the interference of strongly represented, over-learned information in episodic memory recall.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".