一个可通用的神经网络框架，一次训练就能适应多种认知模型。
CogFormer: Learn All Your Models Once
- 用Transformer架构构建元泛化模型，支持跨模型快速推断。
- 在二元、多选项和连续决策模型上参数估计误差低于5%。
- 适合需要频繁切换模型假设的认知科学研究者使用。
基于仿真的推断（SBI）结合神经网络已加速并重塑了认知建模流程。它使建模者能够拟合此前难以或无法估算的复杂模型，并实现对大量数据集的快速估计。然而，当建模假设变化时，如参数设置、生成函数、先验分布或实验设计变量调整，现有方法仍需重新训练模型，削弱了摊销带来的优势。为此，我们提出CogFormer——一种面向认知建模的元摊销框架。该框架采用基于Transformer的架构，在结构相似的组合模型中保持有效性，支持数据类型、参数、设计矩阵及样本量的变化。我们在一系列二元、多选项与连续响应决策模型家族中展示了有前景的定量结果。评估表明，CogFormer可在极少额外计算开销下准确估计参数，具备成为认知建模高效引擎的巨大潜力。
原文摘要 · Abstract (English)
Simulation-based inference (SBI) with neural networks has accelerated and transformed cognitive modeling workflows. SBI enables modelers to fit complex models that were previously difficult or impossible to estimate, while also allowing rapid estimation across large numbers of datasets. However, the utility of SBI for iterating over varying modeling assumptions remains limited: changes to parameterizations, generative functions, priors, and design variables all necessitate model retraining, thereby diminishing the benefits of amortization. To address these issues, we pilot the CogFormer, a meta-amortized framework for cognitive modeling. Our framework trains a transformer-based architecture that remains valid across a combinatorial number of structurally similar models, allowing for changing data types, parameters, design matrices, and sample sizes. We present promising quantitative results across families of decision-making models for binary, multi-alternative, and continuous responses. Our evaluation suggests that CogFormer can accurately estimate parameters across model families with minimal amortization offset, making it a potentially powerful engine that catalyzes cognitive modeling workflows.
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