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 approach. It offers a simplified interpretation of Active Inference that allows designers to more readily apply this theory to design and evaluation. In Interactive Inference, user behaviour is modeled as Bayesian inference on progress and goal distributions that predicts the next action. We show how the 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 Interference can be useful as a new theoretical design tool.

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