# Toward a principled Bayesian workflow in cognitive science

## TL;DR

This paper advocates for a structured Bayesian workflow in cognitive science, emphasizing model validation and domain knowledge to ensure meaningful and reliable inferences from probabilistic models.

## Contribution

It introduces a principled Bayesian workflow tailored for cognitive science, guiding model validation, prior setting, and data analysis to improve robustness and relevance.

## Key findings

- Demonstrates Bayesian workflow with reading time data example
- Provides guidelines for model validation and prior selection
- Highlights importance of model checks for reliable inference

## 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 paper are available from https://osf.io/b2vx9/.