Interactive Inference models user behavior as Bayesian inference on goal/progress distributions, expresses Hick's, Fitts' and Power laws, and reports initial empirical support from a car-following task showing logarithmic processing capacity in SNR.
Anderson, Michael Matessa, and Christian Lebiere
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Interactive Inference: A Neuromorphic Theory of Human-Computer Interaction
Interactive Inference models user behavior as Bayesian inference on goal/progress distributions, expresses Hick's, Fitts' and Power laws, and reports initial empirical support from a car-following task showing logarithmic processing capacity in SNR.