TY  - JOUR
A1  - Schad, Daniel
A1  - Betancourt, Michael
A1  - Vasishth, Shravan
T1  - Toward a principled Bayesian workflow in cognitive science
JF  - Psychological methods
N2  - 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.
KW  - workflow
KW  - prior predictive checks
KW  - posterior predictive checks
KW  - model
KW  - building
KW  - Bayesian data analysis
Y1  - 2021
U6  - https://doi.org/10.1037/met0000275
SN  - 1082-989X
SN  - 1939-1463
VL  - 26
IS  - 1
SP  - 103
EP  - 126
PB  - American Psychological Association
CY  - Washington
ER  -