Do Deductive and Probabilistic Reasoning Abilities Decline in Older Adults?
Bibliographic record
Abstract
This present study investigated whether older adults' ability to accurately discriminate between deductive and probabilistic reasoning tasks declines with age, and whether this ability correlates with cognitive ability as measured by the Montreal Cognitive Assessment (MoCA) test. Seventy‐eight adults (65–92 years) were tested for their abilities to carry out deductive and probabilistic reasoning. Pearson correlations were conducted to determine the relationships among age, MoCA, deductive reasoning, probabilistic reasoning, and overall discrimination ability. Separate single‐factor analyses of variance were used to determine differences across age groups (65–74, 75–84, 85–94) on the MoCA, deductive and probabilistic reasoning, and overall discrimination ability. Ability to discriminate between the two tasks did not decline with age, nor did they correlate with scores of cognitive ability as measured by the MoCA. Furthermore, those with MoCA scores showing mild cognitive impairment appeared to retain all of these abilities. This leads to the conclusion that reasoning abilities may be retained while general cognitive skills decline. This in turn supports the notion that reasoning, both deductive and probabilistic, may be more domain specific than they are often considered to be.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".