Summarize the evidence on presentiment experiments.

Presentiment Experiments: Evidence Summary

Coverage note: The studies below are those currently in the ESP-Nexus library on this question. The library’s share of the published presentiment literature has not been measured, so this is a summary of what the library holds, not a settled account of the field as a whole.

What presentiment research tests

Presentiment experiments look for physiological or neural responses that anticipate a stimulus before it is delivered — and before any information about the upcoming stimulus could be available through normal sensory channels. The most common measures are skin conductance (electrodermal activity), heart rate, pupillary dilation, slow cortical potentials (SCPs), and EEG phase-synchronization. Stimuli are typically drawn from randomized pools of emotional versus calm images, sounds, or other content.

The evidence base

All 17 result rows in the library’s presentiment set point in the same direction (dir=positive across every row), but the metrics are not comparable across studies — the set mixes standardized effect sizes, raw z-scores, and p-only reports — so no single pooled figure describes the whole corpus. The pattern and the individual findings are summarized below.

Meta-analyses and pooled results
Studyk / NKey statisticMetric
Mossbridge (2012)k=26 studiesES = 0.21 (Cohen’s d), CI [0.15, 0.27], z = 6.9, p < 2.7 × 10⁻¹²Standardized effect size
Radin (2004)k=4 exps, N=4,569 trials, 133 participantsES = 0.064 (z/√N), z = 4.04, p = 1.3 × 10⁻⁵Standardized effect size
Radin (2011)k=101 studiesSign test: 85 of 101 studies positive; odds against chance = 1.3 × 10¹²Sign test
Bierman (2000)k=3 datasetsz = 2.748, p = 0.003Stouffer composite
Radin (1997)k=2 experimentsz = 5.0Stouffer composite

The Mossbridge (2012) meta-analysis of 26 studies, using a fixed-effect model, reported the largest pooled standardized effect size in the library’s set. Radin (2011)’s sign-test across 101 studies found 85 trending positive — a pattern that itself yields very long odds against chance under the null, though a sign test does not weight by study quality or size.

Individual experiments
StudyNKey statisticMeasure
Radin (2023)k=10 languagesz = −3.087, p = 0.001Permuted-slope Stouffer Z
Radin (2022)k=10 languagesz = −3.825, p = 0.000065 (one-tail)Sad-day slopes, Stouffer Z
Mossbridge (2023)40 participants, 4,000 trialsp < 1 × 10⁻⁶EEG pre-stimulus prediction
Mossbridge (2017)40 participants, 4,000 trialsp < 2.5 × 10⁻⁶Random-forest classification of EEG
Hamelin (2022)27 participants, 72 sessionsp = 0.00033Pre-choice GSR anticipatory difference
Radin (2009, Exp 1)33 participants, 1,438 trialsz = 3.17, p = 0.0008 (one-tail)Pupillary dilation, emotional vs. calm
Radin (2009, Exp 1 combined)33 participants, 1,438 trialsz = 3.75, p = 9 × 10⁻⁵Combined eye measures (Stouffer Z)
Radin (2009, Exp 2)41 participants, 2,099 trialsz = 1.99, p = 0.05 (two-tail)Pupillary dilation, mismatch vs. match
Radin (2007)13 participants, 1,300 trialsz = 2.72, p = 0.007Female participants, prestimulus SCP
Tressoldi (2010)29 participants, 440 trialsES = 0.11, hit rate = 0.56, z = 2.43High-absorption participants, MCE = .50
Kittenis (2011)20 participantsIMF3, IMF4, IMF5 each p < 0.001Pre-stimulus EEG phase-synchronization
McCraty (2010)13 participantsPositive direction (aggregated)Pre-stimulus physiological, win vs. loss

Tressoldi (2010) found that the absorption personality trait moderated the pre-stimulus anticipatory heart-rate effect — higher-absorption participants showed a hit rate of 0.56 against a 0.50 chance expectation. The Radin (2009) experiments used ocular rather than electrodermal measures, finding significant pre-stimulus pupillary dilation before emotional stimuli in Experiment 1 and a marginal result in Experiment 2 [reported in the structured evidence above]. Kittenis (2011) applied intrinsic-mode function decomposition to EEG data, reporting pre-stimulus phase-synchronization differences across multiple frequency bands.

Unsettled signals — what the evidence does not settle

Several questions within this literature are not resolved by the studies the library holds:

  • Small individual samples. Most individual experiments involve fewer than 50 participants. Effect sizes in this range (Cohen’s d around 0.21 from Mossbridge 2012) are detectable in meta-analyses but underpowered in single studies, meaning that individual replications frequently return null results that do not themselves falsify the pooled estimate.
  • Moderators are inconsistently measured. Tressoldi (2010) identified absorption as a mediator; Radin (2007) found the effect significant in females but did not report a significant result for males in the same dataset. Whether gender, trait absorption, or other individual-difference variables reliably moderate the effect across labs has not been established from the library’s holdings.
  • Metric heterogeneity. The studies use different physiological channels (skin conductance, heart rate, pupil dilation, EEG phase, slow cortical potentials), different stimulus categories, and different analysis pipelines. The Mossbridge (2012) Cohen’s d of 0.21 is a standardized summary across that heterogeneous set; what it measures in mechanistic terms is not specified by the statistic itself.
  • Independent replication outside original research groups. The library’s holdings are heavily weighted toward a small number of research groups. Whether the effect replicates with the same parameters when run by independent laboratories outside those groups is not established from the evidence rows above.
  • The 2023 Radin results use a corpus-linguistic design (slopes across 10 languages) rather than a standard physiological paradigm — a methodologically distinct approach whose relationship to the classic pre-stimulus physiological paradigm is not addressed within the library’s holdings.
Skeptical critiques

What critics argue. A standing methodological concern in presentiment research — raised across the broader parapsychology literature, including in the replication-and-meta-analysis debates covered at https://esp-nexus.org/foundations/skeptical-critiques/replication-and-meta-analysis-debates/ — is that small-sample studies with flexible analysis pipelines can produce false positives through multiple comparisons and post-hoc selection of the most favorable physiological channel or time window. In presentiment specifically, the concern is that the pre-stimulus analysis window can be chosen after the fact in ways that inflate apparent effects.

What the experimental data show. Mossbridge (2012) addressed the multiple-comparisons issue by pooling across 26 studies under a fixed-effect model and still reported a significant pooled effect. Radin (2011)’s sign test, while not controlling for study quality, found 85 of 101 studies directionally positive — a pattern that does not depend on any single window or channel choice. Tressoldi (2010) pre-specified absorption as a moderator and replicated a prior result with it. However, the library holds no fully pre-registered, adversarially designed replication run by a skeptical independent laboratory under agreed-upon protocols; that artifact is absent from the holdings.

Analysis. Mossbridge (2012) and Radin (2011) represent the most systematic attempts to aggregate the literature available in the library, and both report results inconsistent with a pure null. Radin (2009) [reported in the evidence rows] found Experiment 2 yielded a result at p = 0.05 two-tailed — a marginal outcome that sits at the conventional threshold rather than clearly beyond it. The moderator findings (absorption in Tressoldi; sex in Radin 2007) have not been independently replicated across multiple groups in the library’s holdings. The methodological exchange over analysis flexibility and pre-registration in this literature has not concluded.

For broader context on how meta-analytic evidence is evaluated in parapsychology, including the role of effect-size estimation versus vote-counting, the Replication standards and meta-analysis in parapsychology page on Jessica M. Utts’s section of the site is directly relevant.

The studies behind this answer
PaperReported findingEffect / significanceBasis
Radin (2023), World Futures [source]Combined permuted-slope Stouffer Z across all 10 languages.z = -3.087, p = .001k = 10 events/tests
Mossbridge (2023), Journal of Anomalous Experience and Cognition [source]EEG presentiment – pre-stimulus prediction of future button-press response.p < 1 × 10−6N = 4000 trials; 40 participants
Hamelin et al. (2022), Journal of Behavioral and Experimental Finance [source]Pre-choice GSR difference across valid quizzes.p = 3.3 × 10−4N = 72 sessions; 27 participants
Radin (2022), DRAFT (unpublished manuscript)Retrospective: all 10 languages combined, Stouffer Z for sad-day slopes.z = -3.825, p = 6.5 × 10−5k = 10
Mossbridge (2017), Lecture Notes in Artificial Intelligence (AC 2017, Part I, LNAI 10284) [source]Random forest classification: original vs.p < 2.5 × 10−6N = 4000 trials; 40 participants
Mossbridge et al. (2012), Frontiers in Psychology [source]Overall pooled effect – fixed-effect model.ES 0.21, z = 6.9, p < 2.7 × 10−1226 studies
Kittenis (2011), Journal of Parapsychology [source]Pre-stimulus phase-synchronisation difference, New vs Old faces.—N = 20 participants
Radin (2011), AIP Conference Proceedings [source]Two most-recent unconscious classes combined — sign test.—101 studies
Tressoldi et al. (2010), Journal of Scientific Exploration [source]This study – hits of high-Absorption-level groups.ES 0.11, z = 2.43, hit rate 0.56N = 440 trials; 29 participants
McCraty et al. (2010)Win vs loss pre-stimulus physiological difference.—N = 13 participants
Radin et al. (2009), Explore [source]Experiment 1 – Pupillary Dilation: Emotional vs Calm.z = 3.17, p = 8 × 10−4N = 1438 trials; 33 participants
Radin et al. (2007), The Journal of Alternative and Complementary Medicine [source]Females – prestimulus SCP differentiation, flash vs no-flash.z = 2.72, p = .007N = 1300 trials; 13 participants
Radin (2004), Journal of Scientific Exploration [source]All four experiments combined – weighted mean pre-stimulus effect size.ES 0.064, z = 4.04, p = 1.3 × 10−5k = 4; N = 4569 trials; 133 participants
Bierman, Dick J. (2000), Proceedings of the 43rd Annual Convention of the Parapsychological AssociationStouffer composite across the three reanalysed datasets.z = 2.748, p = .0033 studies
Radin (1997), Journal of Scientific Exploration [source]Combined autonomic presponse – before display.z = 5.0k = 2
Source: ESP-Nexus structured study database (15 studies). ESP-Nexus reports what each study found and takes no position on whether the effects are genuine.
References
  1. Radin, D. (2023). Sentiment and Presentiment in Twitter: Do Trends in Collective Mood “Feel the Future”? World Futures, 79(5), 525–535. https://doi.org/10.1080/02604027.2023.2216629
  2. Mossbridge, J. (2023). Precognition at the Boundaries: An Empirical Review and Theoretical Discussion. Journal of Anomalous Experience and Cognition, 3(1), 5–41. https://doi.org/10.31156/jaex.24216
  3. Hamelin, N., & Bonelli, M. I. (2022). Traders’ anticipatory feelings and traders’ profitability: An exploratory study. Journal of Behavioral and Experimental Finance, 36. https://doi.org/10.1016/j.jbef.2022.100743
  4. Radin, D. (2022). Sentiment and presentiment in Twitter: Do trends in collective mood ‘feel the future’? DRAFT (unpublished manuscript).
  5. Mossbridge, J. A. (2017). Characteristic Alpha Reflects Predictive Anticipatory Activity (PAA) in an Auditory-Visual Task. Lecture Notes in Artificial Intelligence (AC 2017, Part I, LNAI 10284), 79–89. https://doi.org/10.1007/978-3-319-58628-1_7
  6. Mossbridge, J., Tressoldi, P., & Utts, J. (2012). Predictive physiological anticipation preceding seemingly unpredictable stimuli: a meta-analysis. Frontiers in Psychology, 3. https://doi.org/10.3389/fpsyg.2012.00390
  7. Kittenis, M. (2011). Anomalous anticipatory event-related EEG activity in a face-recognition memory task. Journal of Parapsychology.
  8. Radin, D. I. (2011). Predicting the Unpredictable: 75 Years of Experimental Evidence. AIP Conference Proceedings, 1408, 204–217. https://doi.org/10.1063/1.3663725
  9. Tressoldi, P. E., Martinelli, M., Scartezzini, L., & Massaccesi, S. (2010). Further Evidence of the Possibility of Exploiting Anticipatory Physiological Signals To Assist Implicit Intuition of Random Events. Journal of Scientific Exploration, 24(3), 411–424.
  10. McCraty, R., Atkinson, M., & Waterman, J. (2010). Stability of Pre-Stimulus Intuition Response: A Repeated Measures Study Using Electrophysical Instrumentation. Institute of HeartMath e-newsletter (heartmath.org), Fall 2010; describes an IHM grant report.
  11. Radin, D., & Borges, A. (2009). Intuition Through Time: What Does the Seer See? Explore, 5, 200–211. https://doi.org/10.1016/j.explore.2009.04.002
  12. Radin, D., & Lobach, E. (2007). Toward Understanding the Placebo Effect: Investigating a Possible Retrocausal Factor. The Journal of Alternative and Complementary Medicine, 13(7), 733–739. https://doi.org/10.1089/acm.2006.6243
  13. Radin, D. I. (2004). Electrodermal Presentiments of Future Emotions. Journal of Scientific Exploration, 18(2), 253–273.
  14. Bierman, D. J. (2000). Anomalous baseline effects in mainstream emotion research using psychophysiological variables. Proceedings of the 43rd Annual Convention of the Parapsychological Association.
  15. Radin, D. I. (1997). Unconscious Perception of Future Emotions: An Experiment in Presentiment. Journal of Scientific Exploration, 11(2), 163–180.
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