Divide-and-Conquer: Towards Generalizable Amortized Bayesian Inference for the Drift Diffusion Model

2026-08-04

AI Summary

Existing amortized Bayesian inference methods for drift-diffusion models struggle to generalize across different experimental designs, limiting their practical applicability. This work proposes a divide-and-conquer framework that exploits the model’s data independence to decompose full datasets into structurally consistent pairwise subsets. A single neural network performs amortized inference on each subset, and the resulting posterior distributions are aggregated via consensus MCMC. This approach achieves, for the first time, cross-design generalization of amortized Bayesian inference in drift-diffusion models while maintaining scalability and broad applicability. In simulation studies, the method matches the accuracy and uncertainty quantification of conventional MCMC but reduces computational cost by several orders of magnitude.

Abstract

The drift diffusion model (DDM) is a cornerstone of cognitive decision-making research. Although numerous estimation methods exist, researchers continue to seek inference approaches that are both fast and flexible across diverse study designs. Amortized Bayesian inference (ABI) can provide nearly instantaneous inference for complex stochastic models like the DDM, but neural networks trained for one study design cannot generalize to others. In this paper, we propose a divide-and-conquer framework that address this limitation. The core idea is that the DDM's independence assumption allows the full dataset to be decomposed into pairwise shards, each sharing a common structure that a single neural network can learn. Inference is performed on each shard separately and the resulting posteriors are combined via consensus MCMC to approximate the full posterior. Using simulated datasets, we evaluate the accuracy and uncertainty of this method. Our results show that the proposed divide-and-conquer approach achieves accuracy and uncertainty comparable to MCMC while reducing computational cost by several orders of magnitude. This work not only advances DDM estimation but also demonstrates a general strategy for improving the scalability and generalizability of ABI methods across diverse applications.

Problem

• drift diffusion model
• amortized Bayesian inference
• generalizability
• study design
• neural networks

Innovation

• amortized Bayesian inference
• drift diffusion model
• divide-and-conquer
• consensus MCMC
• generalizable inference