Decreases loom larger than increases: A perceptual account for loss aversion
Bibliographic record
Abstract
Loss aversion is a cognitive bias where the pain of losing monetary value is greater than the pleasure of gaining the same value. Despite decades of research in loss aversion, the underlying mechanism remains elusive. Here we propose that loss aversion can be explained by a perceptual bias where decreases are overestimated than increases of the same magnitude. To test this account, we showed participants two displays of dots in succession. The first display contained 100 dots. The second display contained 20 to 90 dots by increments of 10, or 110 to 180 dots by increments of 10. After seeing the two displays, participants estimated the number of dots that changed. In our analysis, we paired a decreasing trial (e.g., from 100 to 20) with an increasing trial (from 100 to 180) based on the same magnitude of change. We aimed to examine perceived numerosity change when the number of dots decreased or increased for the same amount. Participants overestimated the number of dots that changed in decreasing trials than that in increasing trials, despite identical changes in both directions (Exp1). We replicated this finding by presenting the two displays simultaneously side by side (Exp2), by expanding the numerosity range from 100 to 900 dots (Exp3), by reversing the positions of the two displays (Exp4), when the language of increasing and decreasing was framed differently (Exp5), and when the positions of the two displays were randomized (Exp6). These results reveal a robust perceptual asymmetry where observers perceive a larger change in decreasing patterns than in increasing patterns despite an equal change in magnitude. This perceptual asymmetry can explain loss aversion where losses are overweighted than gains of the same magnitude. The current findings contribute to the broader question of how perceptual biases underlie cognitive biases.
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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 | 0.001 | 0.008 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".