Latent Variable Sequence Identification for Cognitive Models with Neural Network Estimators

TL;DR

This paper introduces a neural network-based method for extracting time-varying latent variables from complex cognitive models, broadening the scope of models accessible for neural analysis.

Contribution

It extends neural Bayes estimation with recurrent neural networks to infer latent sequences in models with intractable likelihoods, applicable to both continuous and discrete spaces.

Findings

Achieves competitive inference performance in tractable and intractable models.

Generalizes across different cognitive models.

Demonstrates effectiveness on real-world datasets.

Abstract

Extracting time-varying latent variables from computational cognitive models is a key step in model-based neural analysis, which aims to understand the neural correlates of cognitive processes. However, existing methods only allow researchers to infer latent variables that explain subjects' behavior in a relatively small class of cognitive models. For example, a broad class of relevant cognitive models with analytically intractable likelihood is currently out of reach from standard techniques, based on Maximum a Posteriori parameter estimation. Here, we present an approach that extends neural Bayes estimation to learn a direct mapping between experimental data and the targeted latent variable space using recurrent neural networks and simulated datasets. We show that our approach achieves competitive performance in inferring latent variable sequences in both tractable and intractable models. Furthermore, the approach is generalizable across different computational models and is adaptable for both continuous and discrete latent spaces. We then demonstrate its applicability in real world datasets. Our work underscores that combining recurrent neural networks and simulation-based inference to identify latent variable sequences can enable researchers to access a wider class of cognitive models for model-based neural analyses, and thus test a broader set of theories.