Bayesian observer and actor models have provided normative explanations for many behavioral phenomena in perception, sensorimotor control, and other areas of cognitive science and neuroscience. They attribute behavioral variability and biases to interpretable entities such as perceptual and motor uncertainty, prior beliefs, and behavioral costs. However, when extending these models to more naturalistic tasks with continuous actions, solving the Bayesian decision-making problem is often analytically intractable. Inverse decision-making, i.e. performing inference over the parameters of such models given behavioral data, is computationally even more difficult. Therefore, researchers typically constrain their models to easily tractable components, such as Gaussian distributions or quadratic cost functions, or resort to numerical approximations. To overcome these limitations, we amortize the Bayesian actor using a neural network trained on a wide range of parameter settings in an unsupervised fashion. Using the pre-trained neural network enables performing efficient gradient-based Bayesian inference of the Bayesian actor model's parameters. We show on synthetic data that the inferred posterior distributions are in close alignment with those obtained using analytical solutions where they exist. Where no analytical solution is available, we recover posterior distributions close to the ground truth. We then show how our method allows for principled model comparison and how it can be used to disentangle factors that may lead to unidentifiabilities between priors and costs. Finally, we apply our method to empirical data from three sensorimotor tasks and compare model fits with different cost functions to show that it can explain individuals' behavioral patterns.

翻译：贝叶斯观察者与行动者模型为感知、感觉运动控制以及认知科学与神经科学其他领域的诸多行为现象提供了规范性解释。这些模型将行为变异性和偏差归因于可解释的实体，如感知与运动不确定性、先验信念和行为代价。然而，当将这些模型扩展到具有连续动作的更自然化任务时，求解贝叶斯决策问题通常在解析上难以处理。逆向决策——即根据行为数据对此类模型参数进行推断——在计算上更为困难。因此，研究者通常将其模型约束于易于处理的组件（如高斯分布或二次代价函数），或依赖于数值近似方法。为突破这些限制，我们通过无监督方式在广泛参数设置上训练神经网络，实现了对贝叶斯行动者的摊销建模。使用预训练神经网络能够对贝叶斯行动者模型的参数执行高效的基于梯度的贝叶斯推断。我们在合成数据上证明，推断得到的后验分布与现有解析解所得结果高度一致；在无解析解的情况下，我们恢复的后验分布接近真实分布。随后我们展示了该方法如何支持基于原理的模型比较，以及如何用于解耦可能导致先验与代价之间不可辨识性的因素。最后，我们将该方法应用于三项感觉运动任务的实证数据，通过比较不同代价函数的模型拟合度，证明其能够解释个体的行为模式。