Hedonic nondurability revisited: A case for two types.
Bibliographic record
Abstract
Hedonic durability refers to the extent to which the hedonic impact of a change lasts, that is, how long the unhappiness from a loss (or happiness from a gain) will endure over time. The lesson from previous research on this topic has been that the long-term effect of most changes (e.g., larger incomes, bigger houses, shorter commutes) is negligible. The present research shows something different. Consistent with previous research, we observed a pattern of hedonic nondurability in which the impact of a change did not endure over time. However, we also observed a pattern of hedonic durability in which the impact of a change does endure over time. We demonstrate differential rates of hedonic durability for losses, both across variables (Experiment 1) and within different ranges of the same variable (Experiment 2). We also extend our research to show differential rates for gains (Experiment 3). To explain our results, we propose a distinction between preference types, arguing that comparison-independent (i.e., absolute) preference types are hedonically more durable than comparison-dependent (i.e., relative) preference types. This research offers a method for validating preference-type categorization as well as a novel paradigm for testing hedonic durability in the laboratory. Moreover, it yields theoretical insights for affective forecasting and adaptation as well as practical implications for the hedonic treadmill and the joyless economy. (PsycINFO Database Record
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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 | 0.016 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".