Differences in Learning Across the Lifespan Emerge via Resource-Rational Computations
Bibliographic record
Abstract
Learning accurate beliefs about the world is computationally demanding but critical for adaptive behavior across the lifespan. Here, we build on an established framework formalizing learning as predictive inference and examine the possibility that age differences in learning emerge from efficient computations that consider available cognitive resources differing across the lifespan. In our formalization of this idea, beliefs are updated through a sampling process that stops after reaching a criterion level of accuracy. The amount of sampling navigates a trade-off between belief accuracy and computational cost, with more samples favoring belief accuracy and fewer samples minimizing costs. Maximizing the accuracy-cost ratio would require a more frugal sampling policy when cognitive resources are limited or costly, leading to systematically biased beliefs. Data from two lifespan studies (N = 129 and N=90) show that children and older adults display biases characteristic of a more frugal sampling policy, including (i) more frequent perseveration when required to update from previous beliefs and (ii) a stronger anchoring bias when updating beliefs from an externally generated value. Across age groups, individuals who perseverated most when updating from previous beliefs also had the largest anchoring biases when updating beliefs from an externally generated value, corroborating our model's assumption that these biases originate from sampling. Our model and results provide a unifying perspective on perseverative and anchoring biases, show that they can jointly emerge from efficient belief-updating computations in different age groups, and suggest that resource-rational adjustments of sampling computations can explain age-related changes in adaptive learning.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".