Human aging alters Bayesian social inference about others’ changing intentions
Bibliographic record
Abstract
Decoding others’ intentions accurately in order to adapt one’s own behavior is pivotal throughout life. Yet, it is a process that is imbued with uncertainty since others’ intentions are not directly observable and may change over time. In this study, we asked the question of how younger and older adults deal with uncertainty in dynamic social environments. We used an advice-taking paradigm together with biologically plausible hierarchical Bayesian modelling to characterize effects and mechanisms of aging on learning about others’ time-varying intentions. We observed age differences when comparing learning on two levels of social uncertainty: the fidelity of the adviser and the stability of intentions. We found that, prior to having any experience with the adviser, older adults expected the adviser to change his/her intentions more frequently. They also showed higher confidence in such beliefs and were less willing to change their beliefs over the course of the experiment. This led them to update their predictions about observable outcomes (i.e., advice correctness) more quickly. Potentially indicative of stereotype effects, we also observed that older advisers were perceived as more volatile, but at the same time, more faithful than younger advisers. Together these findings offer new insights into the behavioral and algorithmic mechanisms underlying adult age differences in response to social uncertainty, putatively driven by aging-related changes in neuromodulation to be tested in future studies.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".