Toward a principled Bayesian workflow in cognitive science.

Schad, Daniel J.; Betancourt, Michael; Vasishth, Shravan

doi:10.1037/met0000275

Cited by 252Statistical Methods and Bayesian InferenceBayesian Modeling and Causal InferenceMarkov Chains and Monte Carlo Methods

Toward a principled Bayesian workflow in cognitive science.

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Schad, Daniel J., Betancourt, Michael, Vasishth, Shravan·2021·Psychological Methods

DOI 10.1037/met0000275

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Abstract

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 presents prior predictive checks.
“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
The paper’s “Prior predictive checks” section describes and performs prior predictive checks. It does not establish what Appendix S3 of the citing paper’s online Supporting Information contains, so it supports the methodological reference but not the appendix-specific statement. (LLM, conf=0.82)
Experimental — accuracy not yet measured
The study followed a Bayesian workflow for both computational models and conventional analyses.
“We followed a Bayesian workflow for both computational models and conventional analyses [19, 20].”
From Predicting emotional valence in autism: a preregistered study in the Bayesian Brain framework (2026) · cites this paper as [20]

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Authors: Schad, D. J., Betancourt, M., & Vasishth, S.
Affiliations: University of Potsdam (DE)
ORCID: Schad, Daniel J.
Betancourt, Michael
Vasishth, Shravan
Venue: Psychological Methods
Publisher: American Psychological Association (APA)
Published: 2021-02
Volume: 26
Issue: 1
Pages: 103-126
ISSN: 1082-989X (ISSN-L)
1939-1463
DOI: 10.1037/met0000275
OpenAlex: W2941987401
Cited by: 252
References: 51
Citations by year: 2026: 32 · 2025: 34 · 2024: 40 · 2023: 38 · 2022: 34 · 2021: 58 · 2020: 10 · 2019: 5 · 2018: 1
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

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Toward a principled Bayesian workflow in cognitive science.

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