| Abstract/Results: | ABSTRACT:
This precognition study builds on previous research suggesting that free-response protocols with subject-optimization procedures produce higher psi-scores than protocols with no such procedures. Our study aimed to assess the value of optimisation procedures when coupled with a forced-choice protocol, involving multiple trials per session. A second objective was to assess whether trial-by-trial feedback improves scoring over no-feedback conditions.
A pre-set total of 3000 binary choice trials were collected over a 7-week period, from 82 participants, including 26 experienced meditation practitioners. Each participant contributed 20 or 40 trials, based on pre-set scoring criteria. The task was set in an immersive environment coupled with a display of spheres emerging out of an animated starfield. For each trial, subjects had to guess which spheres contained an image; the program would then randomly determine whether or not it indeed contained the image, and whether or not feedback would be shown. For hits, a face would emerge and grow, staring directly at the participant, while for misses it withdrew into the starfield; for no-feedback trials it faded out with no hit/miss information.
Overall results were nonsignificant. Secondary analyses revealed some encouraging trends.
1. When examined across all 150 20-trial series, results show a significant within-series incline in scoring (p=.04, two-tailed), suggesting participants may have progressively found strategies to improve scoring.
2. While not attaining significance (p=0.1 two-tailed), scoring in feedback trials was superior to no-feedback trials; this may suggest that feedback can be useful for learning
3. The 26 meditators' scores were suggestively high (p=.09, one-tailed), while the 14 most experienced meditators showed a significant effect (p=.012, one-tailed).
While overall results were not significant, the finding of a within session incline effect suggests that multiple-trial immersive protocols may help improve psi scoring over time. At the same time, results also suggest that future optimization research should focus on promising subpopulations, like meditators, rather than unselected volunteers.
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