Age differences in the default network at rest and the relation to self-referential processing
Bibliographic record
Abstract
Older adults show a 'positivity bias' in tasks involving emotion and self-referential processing. A critical network that is involved in self-referencing and shows age-related decline is the default network (DN). The purpose of the current study was to investigate age differences in pre- and post-task DN functional connectivity (FC) and signal variability, and to examine whether they are predictive of the positivity bias in self-referencing. We measured FC and within-subject variability of the DN in resting-state scans preceding and following tasks involving personality judgements on the self and a close other. Older adults endorsed more positive traits than younger adults on both tasks. FC was weaker post-task in older vs younger adults, and younger adults had greater variability than older adults in DN nodes. Younger adults with higher post-task DN variability had more negative self-ratings. For both age groups, greater FC in the DN was associated with more negative self-ratings. Neither FC nor variability was related to other ratings, despite the potential for self-processing when making other judgements. Our findings suggest that ageing leads to reduced FC and variability in the DN, which is most apparent after task, and may be one mechanism underlying the positive bias with age.
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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 | 0.001 |
| 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 | 0.000 |
| 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".