Age differences in strategy selection and risk preference during risk-based decision making.
Bibliographic record
Abstract
Studies of the effects of aging on decision making suggest that choices can be altered in a variety of ways depending on the situation, the nature of the outcome and risk, or certainty levels. To better characterize how aging impacts decision making in rodents, young and aged Fischer 344 rats underwent a series of probabilistic discounting tasks in which reward magnitude and probabilities were manipulated. Young rats tended to choose 1 of 2 different strategies: (a) to press for the large/uncertain reward, regardless of the reward probability; or (b) to continually adapt their behavior according to the odds of winning. The first strategy was adopted by about half of the younger rats, the second by the remaining young animals and the entire group of aged rats. Additionally, we found that when the odds of winning were varied from uncertain to certain during a session, aged rats chose most often the lever associated with the small/certain reward. This is consistent with an interpretation of increased risk aversion. When this behavior was further characterized using a lose-shift analysis, it appears that older rats exhibited an increased sensitivity to negative feedback. In contrast, sensitivity to wins was unaltered in aged rats compared with young, suggesting that aging selectively impacts rat's behavior following losses. In line with some human aging studies, it appears that aged rats are either more risk averse or have a greater certainty bias, which may result from age differences in emotion regulation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".