Research Program / Claims / automatic-writing-7

Automatic writing content that is clearly superior to the individual's usual standard should be examined carefully; if it contains prophecies, these should be checked against subsequent events whenever possible.

当自动书写内容质量明显优于日常水准时应仔细审查；若含预言，应在可能时核对后续事件。

Concept automatic writing · Domain Belief, Ritual, and Mind–Body Effects · methodological suggestion · interface 3/3 · testability 3/3 · status supported

This program treats the Seth Material as a source of hypotheses, not as doctrine to be validated. Lead dossiers are AI-compiled from the corpus with a book-and-session citation after every statement; literature reviews are AI web-search summaries whose references were all DOI-verified against Crossref; the agenda draft is an AI synthesis. None of it is Seth's original text, and none of it represents scientific consensus. English fields are machine-translated; the Chinese text is authoritative.

Seth sources (book §session)
  • esp-power §2
Adjudicating experiment
Have 100 writers submit all texts continuously for six months. Before outcomes occur, independent personnel code each prophecy's event, deadline, direction, and probability, then seal the records with timestamps. Follow up for another 6–12 months. Compare all scorable predictions with baseline occurrence rates, ordinary writing, and conventional forecasts, using Brier scores and including failed predictions. Separately, have at least three blinded raters compare textual quality with matched baseline texts by the same author.
Suggested paper title
Prospective Accuracy and Blinded Quality Assessment of Automatic Writing: A Preregistered Longitudinal Study
What it would overturn
The recommendation to examine and verify content does not itself overturn any mainstream assumption. Only replicable predictions exceeding baseline performance after information leakage has been excluded would challenge the assumption that prediction is constrained by existing information.
Candidate labs / funders
Quinton Deeley；伦敦国王学院 Cultural and Social Neuroscience Research Group; Ryan L. Boyd；得克萨斯大学达拉斯分校，文本分析研究
Specificity of Seth's formulation
Very low: this is consistent with standard principles of evidence verification. No predictive accuracy, time window, or margin above baseline is specified.
Note
The label supported indicates that the methodological recommendation is supported by scientific methodology, not that automatic writing prophecies are supported. Checking only statements that appear to have been accurate creates selection bias.
Evidence (2, DOI-verified via Crossref)
  • The preregistration revolution — Brian A. Nosek; Charles R. Ebersole; Alexander C. DeHaven; David T. Mellor (2018), Proceedings of the National Academy of Sciences
    方法论支持：预先固定预测和分析可区分真正预测与事后解释；此文不证明自动书写具有预言能力。[论文](https://doi.org/10.1073/pnas.1708274114)
  • Neuroimaging during trance state: a contribution to the study of dissociation — Julio Fernando Peres; Alexander Moreira-Almeida; Leonardo Caixeta; Frederico Leao; Andrew Newberg (2012), PLOS ONE
    直接邻近证据：10名书写者的探索研究报告状态间文本复杂度及脑血流差异，但样本极小，复杂度不等于事实正确或预言准确。[论文](https://doi.org/10.1371/journal.pone.0049360)

Cite: Future Mind Institute, Seth Material Research Agenda, claim automatic-writing-7, https://www.seth.org.cn/en/research/claims/automatic-writing-7