Looking for bias in all the right places: Incentive-driven optimism and pessimism
Bibliographic record
Abstract
What motivates people to choose biased evidence over more accurate sources? Psychological models of motivated information search have typically explained this by appealing to specific directional motives such as the desire to preserve self-esteem (Beauregard & Dunning, 1998) or signal ingroup membership (Flynn et al., 2017). Although this proliferation of directional accounts helps highlight specific incentives (e.g., self-enhancement, social pressure) that may motivate individuals to prefer biased information, what’s missing is a more general understanding of how any large possible incentive might lead people to prefer biased information in the first place. My thesis offers a parsimonious model of how the mere possibility of any large reward or loss might lead people to prefer biased information through three general processes– a “rational” account in which people prefer biased information based on the increased reward or mitigated losses that can be gained from acting on that information; a trust-based account in which people prefer biased information because trusting it leads to better consequences, and a congeniality bias account in which people prefer biased information because it justifies their belief in how they should act. In chapter 2, I formalize each of these processes and highlight how each predicts that the possibility of large incentives should moderate preference for biased information, such that the possibility of large rewards for making correct decisions would increase preference for optimistic information, while the possibility of large losses for making incorrect decisions would increase preference for pessimistic information. In chapter 3, I provide empirical demonstration of this moderation in three studies. Finally, in chapter 4, I show in a final study how all three of these processes can significantly account for individual differences in biased information search. Together, this work demonstrates that the mere presence of large incentives can lead to preference for biased information through three general motivational processes. Thus, even in lieu of more specific directional accounts of an individual’s motivations, biased information search can be understood through knowledge of the consequences an individual stands to face by acting on that information.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".