Can the experimental study of religion be advanced using a Bayesian predictive framework
Bibliographic Data
| ID | 16909360 |
|---|---|
| Authors | Michiel Van Elk (0000-0002-7631-3551, University of Amsterdam, corresponding author), Eric-Jan Wagenmakers (0000-0003-1596-1034, University of Amsterdam) |
| Year | 2017 |
| Volume | 7 |
| Issue | 4 |
| Pages | 331-334 |
| Publication date | 2017-10-02 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Religion Brain & Behavior (JOURNAL) |
| Journal identifiers | ISSN: 2153-5981 • E-ISSN: 2153-599X |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/2153599x.2016.1249915 |
| OpenAlex | W2594897753 |
| Language | EN |
| Citations received | 6 |
| References cited | 22 |
We propose a Bayesian framework as an important theoretical and methodological tool to improve the scientific study of religion. At a theoretical level, the Bayesian predictive processing framework has the potential to provide a unifying account of religious beliefs and experience by stressing the central role of error monitoring and error correction in belief maintenance. At a methodological level, Bayesian statistics are needed to provide the extraordinary evidence for the extraordinary theoretical claims regarding the causes and consequences of religion
Bayesian experimental design · Bayesian inference · Bayesian probability · Bayesian statistics · Econometrics · Epistemology · Machine learning · Computer Science · Evolutionary Game Theory and Cooperation · Mathematics · Philosophy · Psychedelics and Drug Studies · Psychology · Study and Philosophy of Religion · Artificial Intelligence
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| Unique citing works | 6 |
|---|---|
| Citations per year | 0,67 |
| Citation span | 2017 - 2022 (6) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 6 |