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Record W4387001831 · doi:10.1111/sjop.12961

Examining the relationship between metacognitive trust in thinking styles and supernatural beliefs

2023· article· en· W4387001831 on OpenAlexaboutno aff
Valerie van Mulukom, Adam Baimel, Everton de Oliveira Maraldi, Miguel Farias

Bibliographic record

VenueScandinavian Journal of Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicParanormal Experiences and Beliefs
Canadian institutionsnot available
FundersFundação BialJohn Templeton Foundation
KeywordsPsychologyParanormalMetacognitionCognitive stylePsychicStyle (visual arts)Social psychologyScale (ratio)Magical thinkingTask (project management)CognitionCognitive psychology

Abstract

fetched live from OpenAlex

Conflicting findings have emerged from research on the relationship between thinking styles and supernatural beliefs. In two studies, we examined this relationship through meta-cognitive trust and developed a new: (1) experimental manipulation, a short scientific article describing the benefits of thinking styles: (2) trust in thinking styles measure, the Ambiguous Decisions task; and (3) supernatural belief measure, the Belief in Psychic Ability scale. In Study 1 (N = 415) we found differences in metacognitive trust in thinking styles between the analytical and intuitive condition, and overall greater trust in analytical thinking. We also found stronger correlations between thinking style measures (in particular intuitive thinking) and psychic ability and paranormal beliefs than with religious beliefs, but a mixed-effect linear regression showed little to no variation in how measures of thinking style related to types of supernatural beliefs. In Study 2, we replicated Study 1 with participants from the United States, Canada, and Brazil (N = 802), and found similar results, with the Brazilian participants showing a reduced emphasis on analytical thinking. We conclude that our new design, task, and scale may be particularly useful for dual-processing research on supernatural belief.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.139
GPT teacher head0.410
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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