Physically scarce (vs. enriched) environments decrease the ability to tell lies successfully.
Bibliographic record
Abstract
The successful detection of deception is of critical importance to adaptive social relationships and organizations, and perhaps even national security. However, research in forensic, legal, and social psychology demonstrates that people are generally very successful deceivers. The goal of the current research was to test an intervention with the potential to decrease the likelihood of successful deception. We applied findings in the architectural, engineering, and environmental sciences that has demonstrated that enriched environments (vs. scarce ones) promote the experience of comfort, positive emotion, feelings of power and control, and increase productivity. We hypothesized that sparse, impoverished, scarcely endowed environments (vs. enriched ones) would decrease the ability to lie successfully by making liars feel uncomfortable and powerless. Study 1 examined archival footage of an international sample of criminal suspects (N = 59), including innocent relatives (n = 33) and convicted murderers (n = 26) emotionally pleading to the public for the return of a missing person. Liars in scarce environments (vs. enriched) were significantly more likely to reveal their lies through behavioral cues to deception. Study 2 (N = 79) demonstrated that the discomfort and subsequent powerlessness caused by scarce (vs. enriched) environments lead people to reveal behavioral cues to deception. Liars in scarce environments also experienced greater neuroendocrine stress reactivity and were more accurately detected by a sample of 66 naïve observers (Study 3). Taken together, data suggest that scarce environments increase difficulty, and decrease success, of deception. Further, we make available videotaped stimuli of Study 2 liars and truth-tellers.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".