Neuromorphic Human-Computer Interaction (HCI) is a theoretical approach to designing better user experiences (UX) motivated by advances in the understanding of the neurophysiology of the brain. Inspired by the neuroscientific theory of Active Inference, Interactive Inference is a first example of such an approach. It offers a simplified interpretation of Active Inference that allows designers to more readily apply this theory to design and evaluation. The basic premise in Interactive Inference is that the user predicts a result prior to performing a task. User behaviour is modeled as Bayesian inference on progress and goal distributions that predicts the next action. The difference between the observed result and the prediction is what is processed by the brain. This error between goal and progress distributions, or Bayesian surprise, can be modeled as a simple mean square error of the signal-to-noise ratio (SNR) of a task. The problem is that the user's capacity to process Bayesian surprise follows the logarithm of this SNR. This means errors rise quickly once average capacity is exceeded. Our model allows the quantitative analysis of performance and error using one framework that can provide real-time estimates of the mental load in users that needs to be minimized by design. We show how three basic laws of HCI, Hick's Law, Fitts' Law and the Power Law can be expressed using our model. We then test the validity of the model by empirically measuring how well it predicts human performance and error in a car following task. Results suggest that driver processing capacity indeed is a logarithmic function of the SNR of the distance to a lead car. This result provides initial evidence that Interactive Inference can be useful as a new theoretical design tool.

翻译：神经形态人机交互（HCI）是一种基于脑神经生理学理解进展来设计更优用户体验（UX）的理论方法。受神经科学中的"主动推断"理论启发，"交互推断"是该方法的首个范例。它提供了主动推断的简化解释，使设计者能更便捷地将该理论应用于设计与评估。交互推断的基本前提是：用户在执行任务前会预测结果。用户行为被建模为对进度和目标分布进行贝叶斯推断，从而预测下一步行动。观察结果与预测之间的差异由大脑处理。目标分布与进度分布之间的误差（即贝叶斯惊奇度）可建模为任务信噪比（SNR）的简单均方误差。问题在于，用户处理贝叶斯惊奇度的能力遵循该信噪比的对数规律。这意味着一旦平均处理能力被超出，错误会迅速增加。我们的模型使用统一框架实现了对绩效和错误的量化分析，并能实时估计用户的心智负荷——这种负荷需要通过设计加以最小化。我们展示了如何用该模型表达人机交互的三大基本定律：希克定律、菲茨定律和幂定律。随后，通过实证测量模型在车辆跟随任务中预测人类绩效和错误的准确性，验证了模型的有效性。结果表明，驾驶员处理能力确实是与前车距离信噪比的对数函数。这一发现为"交互推断"作为新型理论设计工具的有效性提供了初步证据。