Neural underpinnings of within-person variability in cognitive functioning.
Bibliographic record
Abstract
Increased intraindividual variability (IIV), reflecting within-person fluctuations in behavioral performance, is commonly observed in aging as well as in select disorders including traumatic brain injury, schizophrenia, attention-deficit hyperactivity disorder (ADHD), and dementia. Much recent progress has been made toward understanding the functional significance of IIV in cognitive performance (MacDonald, Nyberg, & Bäckman, 2006) and biological information processing (Stein, Gossen, & Jones 2005), with parallel efforts devoted to investigating the links between older adults' deficient neuromodulation and their more variable neuronal and cognitive functions (Bäckman, Nyberg, Lindenberger, Li, & Farde, 2006). Despite these advances in the study of IIV, there has been little empirical examination of underlying neural correlates and virtually no synthesis of extant findings. The present review summarizes the accumulating empirical evidence linking age-related increases in IIV in cognitive performance to neural correlates at anatomical, functional, neuromodulatory, and genetic levels. Computational theories of neural dynamics (e.g., Li, Lindenberger, & Sikström, 2001) are also introduced to illustrate how age-related neuromodulatory deficiencies may contribute to increased neuronal noise and render information processing in aging neurocognitive systems to be less robust. The potential benefits of stochastic resonance and external noise are also discussed with respect to processing subthreshold stimuli (e.g., Li, von Oertzen, & Lindenberger, 2006). We conclude by highlighting important challenges and outstanding research issues that remain to be answered in the study of IIV.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".