State, Trait, and Target Parameters Associated with Accuracy in Two Online Tests of Precognitive Remote Viewing
Bibliographic record
Abstract
Objective. To better characterize the relations between accuracy on precognitive remote viewing (PRV) tasks and potentially relevant trait, state, and target parameters, we gathered PRV data in two online experiments and examined accuracy relative to: sex-at-birth, gender, age, anxiety, unconditional love, and target interestingness. Method. In experiment 1 we used a forced-choice, uncontrolled-time, self-judged PRV task for which 682 unpaid participants contributed a total of 5,432 trials. Experiment 2 used a free-response, controlled-time, independently judged PRV task for which 307 paid participants each contributed a single trial. In neither case were the participants pre-screened for precognition ability. Results. In experiment 1 (forced-choice PRV task), there was no significant target precognition and no effect of age on PRV performance, but we found a complex effect of sex-at-birth. We also found that targets most likely to be correctly predicted were also more likely to be judged as interesting compared to targets most likely to be incorrectly predicted; a pre-registered analysis confirmed this effect. In experiment 2 (free-response PRV task) we found significant target precognition, no effect of age on performance, and a weak and indirect effect of gender. Feelings of unconditional love and anxiety were both correlated with higher accuracy in experiment 2. Again, target interestingness was positively related to accuracy. Conclusion. These results suggest that accuracy on PRV tasks is related to the emotional state of participants and target interestingness, and that task characteristics mitigate overall performance. We provide recommendations for future research based on these observations.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".