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Toward a principled Bayesian workflow in cognitive science.
Open accessAbstract
Experiments in research on memory, language, and in other areas of cognitive science are increasingly being analyzed using Bayesian methods. This has been facilitated by the development of probabilistic programming languages such as Stan, and easily accessible front-end packages such as brms. The utility of Bayesian methods, however, ultimately depends on the relevance of the Bayesian model, in particular whether or not it accurately captures the structure of the data and the data analyst's domain expertise. Even with powerful software, the analyst is responsible for verifying the utility of their model. To demonstrate this point, we introduce a principled Bayesian workflow (Betancourt, 2018) to cognitive science. Using a concrete working example, we describe basic questions one should ask about the model: prior predictive checks, computational faithfulness, model sensitivity, and posterior predictive checks. The running example for demonstrating the workflow is data on reading times with a linguistic manipulation of object versus subject relative clause sentences. This principled Bayesian workflow also demonstrates how to use domain knowledge to inform prior distributions. It provides guidelines and checks for valid data analysis, avoiding overfitting complex models to noise, and capturing relevant data structure in a probabilistic model. Given the increasing use of Bayesian methods, we aim to discuss how these methods can be properly employed to obtain robust answers to scientific questions. All data and code accompanying this article are available from https://osf.io/b2vx9/. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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“Appendix S3 in the online Supporting Information presents the prior predictive checks (Schad, Betancourt, & Vasishth, 2021).”
From Effects of Availability, Contingency, and Formulaicity on the Accuracy of English Grammatical Morphemes in Second Language Writing (2022) · cites this paper as Schad, Betancourt, & Vasishth, 2021
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“We followed a Bayesian workflow for both computational models and conventional analyses [19, 20].”
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Lissón, P., Pregla, D., Paape, D., Burchert, F., Stadie, N., & Vasishth, S. (2021) · doi:10.31234/osf.io/nfg7u
Self-citationVasishth, S., & Engelmann, F. (2021) · Cambridge University Press eBooks · doi:10.1017/9781316459560.016
Self-citationThe Core ACT-R-Based Model of Retrieval Processes
Vasishth, S., & Engelmann, F. (2021) · Cambridge University Press eBooks · doi:10.1017/9781316459560.008
Self-citation
Metadata
- Authors
- Schad, D. J., Betancourt, M., & Vasishth, S.
- Affiliations
- University of Potsdam (DE)
- Venue
- Psychological Methods
- Published
- 2021-02
- Volume
- 26
- Issue
- 1
- Pages
- 103-126
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W2941987401
- Cited by
- 252
- References
- 51
- Citations by year
- 2026: 32 · 2025: 34 · 2024: 40 · 2023: 38 · 2022: 34 · 2021: 58
- FWCI
- 24.54
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- cc-by
- Locations
- Psychological Methods (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- publish.UP (University of Potsdam) (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (317633480—SFB 1287)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Keywords
- model checking, posterior predictive checks, model sensitivity, prior distributions, reading time, relative clauses, overfitting, probabilistic programming
- MeSH terms
- Bayes Theorem, Humans, Models, Psychological, Models, Statistical, Cognitive Science, Workflow
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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