An Active Inference Model of Mouse Point-and-Click Behaviour
This work addresses spatial pointing modeling for HCI researchers, but it is incremental as it applies an existing Active Inference framework to a specific domain.
The paper tackled the problem of modeling mouse point-and-click behavior in HCI by using an Active Inference agent with continuous spaces and predictive delay compensation, resulting in plausible movements and end-point variance similar to humans without retuning parameters for different target difficulties.
We explore the use of Active Inference (AIF) as a computational user model for spatial pointing, a key problem in Human-Computer Interaction (HCI). We present an AIF agent with continuous state, action, and observation spaces, performing one-dimensional mouse pointing and clicking. We use a simple underlying dynamic system to model the mouse cursor dynamics with realistic perceptual delay. In contrast to previous optimal feedback control-based models, the agent's actions are selected by minimizing Expected Free Energy, solely based on preference distributions over percepts, such as observing clicking a button correctly. Our results show that the agent creates plausible pointing movements and clicks when the cursor is over the target, with similar end-point variance to human users. In contrast to other models of pointing, we incorporate fully probabilistic, predictive delay compensation into the agent. The agent shows distinct behaviour for differing target difficulties without the need to retune system parameters, as done in other approaches. We discuss the simulation results and emphasize the challenges in identifying the correct configuration of an AIF agent interacting with continuous systems.