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.

从计算认知模型中提取变化的潜在变量是模型基础神经分析的关键步骤，我们提出了一种方法，通过递归神经网络和模拟数据集，将神经贝叶斯估计扩展到学习实验数据和目标潜在变量空间之间的直接映射，从而在可处理和难度较大的模型中推断潜在变量序列，该方法适用于不同的计算模型和连续离散潜在空间，并在实际数据集中证明了其适用性。