Discovery Lab Answers

Julia A. Mossbridge, PhD — Experiments and Data

Julia A. Mossbridge, PhD is a neuroscientist and parapsychologist whose research spans presentiment, precognitive remote viewing, and anomalous cognition more broadly. Her work moves across physiological laboratory protocols, large-scale online testing, case studies, and theoretical review. Her profile page is at Julia A. Mossbridge, PhD.

Experiments

Predictive Anticipatory Activity (PAA) meta-analysis. In 2012, Mossbridge led a meta-analysis synthesizing data from seven independent laboratories on whether human physiology can apparently anticipate randomly delivered future stimuli 1–10 seconds before they occur ^[6]. This was co-authored with Jessica M. Utts and Patrizio Tressoldi and published in Frontiers in Psychology. The meta-analysis established PAA as a measurable phenomenon while also identifying expectation bias and multiple-comparisons problems as key methodological challenges.

Laboratory presentiment studies. From her own laboratory, Mossbridge conducted at least three presentiment experiments measuring physiological responses — EEG (electroencephalography), and at least two other physiological dependent variables — in advance of randomly delivered stimuli ^[6]. One study examined whether a left vs. right motor response press could be predicted from EEG activity prior to stimulus presentation.

Online precognitive remote viewing (PRV) studies. With Kirsten Cameron and Mark Boccuzzi, Mossbridge ran two batches of forced-choice PRV trials delivered through an online platform, examining state, trait, and target parameters associated with accuracy ^[4]. Funding came from the Bial Foundation.

Moon phases and online precognition. A follow-up letter examined whether participant trial-initiation behavior and precognitive accuracy in the online PRV data varied with lunar phase across two data batches ^[5], with a subsequent erratum correcting copy-paste errors in reported statistics ^[1].

Smartphone-based psi testing. With Dean I. Radin, Mossbridge co-authored a study using three iOS tasks available from 2017 to 2020, collecting data from thousands of participants on micro-psychokinesis and precognition tasks. The study applied a “SEARCH” (Systematically Exploring Associations between Characteristics and Results in Humans) approach to identify demographic and personality moderators of performance.

Precognitive lucid dreaming case study. With Dave Green, Christopher C. French, Alan Pickering, and Damon Abraham, Mossbridge published a 2025 case study examining whether a self-identified precognitive lucid dreamer (co-author Dave Green) could produce above-chance dream-target matches, using both human judging and AI text-embedding scoring methods ^[2].

Theoretical and empirical review. In 2023, Mossbridge published a solo empirical review and theoretical discussion in the Journal of Anomalous Experience and Cognition examining precognition at its boundaries — distinguishing presentiment (unconscious, physiological, short lead times) from precognitive remote viewing (perceptual and cognitive, longer lead times) and arguing for distinct mechanisms ^[6].

Nonspeaker cognition and psi. With Maria Welch and Jeff Tarrant, Mossbridge co-authored a 2025 Mindfield bulletin piece exploring whether nonspeaking individuals may possess psi-related capacities, framing the inquiry around controlled laboratory research on exceptional human performers ^[3].

Methodology

Physiological presentiment protocols. Mossbridge’s laboratory presentiment studies measured continuous physiological signals (EEG and others) while participants were exposed to randomly selected stimuli, looking for pre-stimulus differentiation between signal types. The paradigm requires careful control of expectation bias — the tendency for participants’ anticipatory physiology to track their conscious predictions rather than the stimulus itself ^[6].

Expectation bias control. A methodological contribution Mossbridge developed with collaborators (Dalkvist, Mossbridge, and Westerlund, 2014, cited within ^[6]) is a recommended strategy for removing expectation bias from presentiment and similar experiments. This addresses a recognized confound in PAA research: if participants can guess what stimulus type is coming next, their physiology may respond to that expectation rather than to any anomalous anticipation.

Online platform design. The PRV online studies used a forced-choice design in which participants viewed a graph profile and selected which of several target images matched it, before the target was revealed. Practice and test trials were distinguished, participants who contributed to both data batches were treated as independent (a pre-specified analytical choice), and the NIST randomness test suite was applied to verify target randomization ^[4]. Pre-registration is noted for some aspects; the erratum ^[1] acknowledges a copy-paste error that passed undetected before publication, suggesting the pre-registration infrastructure was not sufficient to catch all data-entry errors.

Case study with pre-registration. The precognitive lucid dreaming study used a pre-registered judging method alongside two exploratory methods (unskilled human judges and AI text-embedding models), clearly labeling which analyses were confirmatory and which were exploratory ^[2]. The pre-registered method is described as “flawed,” and the exploratory AI-scoring method is presented as a proof of concept rather than a confirmatory test.

SEARCH approach. The smartphone-based study with Radin used a systematic demographic and personality moderator search rather than a single pre-specified hypothesis, explicitly acknowledging the exploratory nature and using separate training and test datasets to limit overfitting.

Data

The sources retrieved for this question do not include a structured evidence block, so no pooled effect sizes or formal meta-analytic statistics are reported here. The figures below come directly from the retrieved primary sources, and the pattern across studies is mixed.

PAA meta-analysis. Mossbridge’s 2012 meta-analysis estimated an effect size for presentiment at approximately 0.21 to 0.28 (two meta-analyses are cited for this range, including Mossbridge et al., 2012 and Duggan & Tressoldi, 2018) ^[6]. The lead time for the physiological pre-response ranges from roughly 0.5 to 15 seconds before the future stimulus.

Laboratory EEG presentiment. In one laboratory study, left vs. right response-press was predictable from left frontal and right temporal-parietal EEG activity approximately 550 ms prior to stimulus presentation, reported at p < 1×10⁻⁶ ^[6]. Prediction of stimulus type using the same method was not successful.

Online PRV — batch results. The two batches of online PRV data showed divergent outcomes ^[4]:

Test trials in batch 2 performed significantly below chance — a psi-missing pattern. The difference between practice and test trials was significant in batch 2 (χ²(1, N = 2429) = 3.9, p < .05), with practice trials more accurate than test trials ^[4]. Mossbridge and colleagues interpret this as an expectation-opposing effect.

Moon phases. In the lunar phase analysis, participants were significantly more likely to initiate trials during last-quarter/waning-crescent phases than during full/waning-gibbous phases, across both batches (p < 1×10⁻⁶ for multiple comparisons) ^[5]. A trend toward better accuracy during the phases when participants were more likely to initiate trials was observed in batch 1 (χ²(2, N = 792) = 5.79, p < .017) and batch 2 (χ²(2, N = 1438) = 6.65, p < .01) ^[5]. The erratum ^[1] corrected copy-paste errors in the reported figures but stated that the conclusions of the original letter were not changed.

Precognitive lucid dreaming case study. The pre-registered human judging method produced 3 hits out of 10 dreams; the unskilled human judges produced 1 hit out of 10; AI text-embedding methods were applied exploratorily ^[2]. The authors describe the evidence as “weak but encouraging” and note that all results require replication.

Forced-choice precognition (general). Mossbridge’s 2023 review notes that forced-choice precognition is replicable but tends to produce very small effect sizes, attributing this partially to participant boredom and to conscious deliberation overriding automatic anticipatory processes ^[6].

Skeptical critiques

What critics argue. Mossbridge’s 2012 PAA meta-analysis and her theoretical framing of presentiment have attracted the standard methodological critique that applies to this research area. Within her own published work, she documents that expectation bias is a critical confound: if participants can consciously or unconsciously predict which stimulus type will appear next, physiological anticipation could track that prediction rather than any anomalous future-knowledge ^[6]. The PAA meta-analysis itself identified this as a key unsettled methodological problem, and Dalkvist, Mossbridge, and Westerlund (2014) published a dedicated strategy paper specifically because the field had not adequately addressed it ^[6].

The online PRV data present an internal challenge to a straightforward precognition interpretation: test-trial performance in batch 2 fell significantly below chance while practice-trial performance did not ^[4]. Mossbridge and colleagues describe this as an “expectation-opposing effect,” but the pattern is consistent with a methodological artifact in which participant behavior changes between practice and test contexts in a way that suppresses any genuine signal.

The precognitive lucid dreaming case study ^[2] was co-authored by the study’s primary participant (Dave Green), which the authors themselves acknowledge. This introduces a potential conflict of interest in data collection and interpretation that the pre-registered design only partially controls for. French and Pickering — who are among the co-authors — are on record elsewhere in the parapsychological literature as emphasizing the importance of skeptical scrutiny in anomalous cognition research, though no separate published critique of this specific study appears in the retrieved sources.

What the experimental data show. Across Mossbridge’s own published record, the pattern is not uniformly positive. The online PRV test-trial data showed below-chance performance in batch 2 ^[4]. The lucid dreaming case study produced results the authors themselves label “weak” ^[2]. The PAA meta-analysis effect size estimates (approximately 0.21–0.28) rest on aggregating across heterogeneous laboratory paradigms and physiological measures, and the meta-analysis explicitly named expectation bias as an unresolved threat to validity ^[6].

Analysis. Mossbridge has been unusually transparent in publishing null and below-chance results alongside positive ones, and in naming methodological limitations (expectation bias, multiple comparisons, participant behavior differences between practice and test trials) within her own papers. The below-chance test-trial result in the second online PRV batch and the “weak” characterization of the lucid dreaming evidence are both documented in her own publications, not surfaced only by outside critics. Independent replication of the online PRV positive signals outside Mossbridge’s group has not been published in the retrieved sources.