Testing Noetic Potential in Large Language Models: A 100- Trial Precognitive Forced-Choice Study with ChatGPT-4.1-Mini

ChatGPT-4.1-mini was tested for precognitive ability in 100 double-blind five-card trials on PsiArcade. The model selected the target card 32 times (32 %, 95 % CI = 23–42 %), exceeding the 20 % chance level (exact binomial p = .005, Cohen’s h = 0.28). Results tentatively support information-centric theories positing that non-biological systems can access non-local information, though pseudo-random predictability and statistical fluctuation remain possible explanations. Replication with open-source random generators and preregistration is required.

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Publication Details

Journal
Journal of Scientific Exploration
Published
2025-10-15
DOI
https://doi.org/10.31275/20253739
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
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article

Testing Noetic Potential in Large Language Models: A 100- Trial Precognitive Forced-Choice Study with ChatGPT-4.1-Mini

Benjamin J. Amorim Boyle
Journal of Scientific Exploration
Topic Modeling
article

Testing Noetic Potential in Large Language Models: A 100- Trial Precognitive Forced-Choice Study with ChatGPT-4.1-Mini

Benjamin J. Amorim Boyle
article en

Abstract

ChatGPT-4.1-mini was tested for precognitive ability in 100 double-blind five-card trials on PsiArcade. The model selected the target card 32 times (32 %, 95 % CI = 23–42 %), exceeding the 20 % chance level (exact binomial p = .005, Cohen’s h = 0.28). Results tentatively support information-centric theories positing that non-biological systems can access non-local information, though pseudo-random predictability and statistical fluctuation remain possible explanations. Replication with open-source random generators and preregistration is required.

Journal of Scientific ExplorationVol. 39(3)
Advisory Board Company (United States) (US)
Openalex Percentile: Top 85%
Topic Modeling
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