Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive Models
This work addresses robustness issues in cognitive modeling for researchers, though it is incremental as it builds on existing amortized Bayesian inference methods.
The study tackled the problem of parameter estimation in cognitive models being sensitive to outliers by improving the robustness of amortized Bayesian inference through data augmentation with a Cauchy contamination distribution, resulting in significantly higher breakdown points and bounded influence functions.
Contaminant observations and outliers often cause problems when estimating the parameters of cognitive models, which are statistical models representing cognitive processes. In this study, we test and improve the robustness of parameter estimation using amortized Bayesian inference (ABI) with neural networks. To this end, we conduct systematic analyses on a toy example and analyze both synthetic and real data using a popular cognitive model, the Drift Diffusion Models (DDM). First, we study the sensitivity of ABI to contaminants with tools from robust statistics: the empirical influence function and the breakdown point. Next, we propose a data augmentation or noise injection approach that incorporates a contamination distribution into the data-generating process during training. We examine several candidate distributions and evaluate their performance and cost in terms of accuracy and efficiency loss relative to a standard estimator. Introducing contaminants from a Cauchy distribution during training considerably increases the robustness of the neural density estimator as measured by bounded influence functions and a much higher breakdown point. Overall, the proposed method is straightforward and practical to implement and has a broad applicability in fields where outlier detection or removal is challenging.