Perceived Naturalness Biases Objective Behavior in Both Trivial and Meaningful Contexts
Bibliographic record
Abstract
Research shows that perceived naturalness can bias beliefs about the positivity of items such as food, human talent, and vaccines. Yet, this research focuses on self-reports, which leaves open the implications it has for behavior. In four studies ( N = 492), we tested if perceived naturalness impacts trivial and meaningful behaviors. Participants were asked to consume a purported natural/synthetic performance drink (Study 1), test a purported natural/synthetic drug that would be injected (Study 2), eat chocolate containing a purported natural/synthetic cocoa described as causing stomach discomfort (Study 3), or choose a sticker purportedly made with natural/synthetic ink (Study 4). A significant majority of participants (66%–84%) chose and followed through with the natural versus synthetic option. Perceived naturalness guided behavior in contexts involving little (sticker choice) to substantial (drug injection) potential consequences. Self-reports can weakly predict behaviors, but the results revealed that perceived naturalness biases self-reports and behaviors in a similar fashion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".