BibTeX

@article{2406.14742v2,
Author        = {Ti-Fen Pan and Jing-Jing Li and Bill Thompson and Anne Collins},
Title         = {Latent Variable Sequence Identification for Cognitive Models with Neural
  Network Estimators},
Eprint        = {2406.14742v2},
DOI           = {10.3758/s13428-025-02794-0},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
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.},
Year          = {2024},
Month         = {Jun},
Note          = {Behav Res 57, 272 (2025)},
Url           = {http://arxiv.org/abs/2406.14742v2},
File          = {2406.14742v2.pdf}
}