An Information and Control Framework for Optimizing User-Compliant Human–Computer Interfaces

We consider a general framework for a human–computer interface whereby the human's knowledge is represented as a point in Euclidean space, the intention of the human is signaled to the computer over a noisy channel, and the computer queries the human in a manner that is amenable to human operation. With these constraints at hand, we demonstrate a class of systems that are nonetheless information-theoretically optimal in that the computer very rapidly hones in on the intent of the human. Much recent work on feedback information theory has been dedicated to the exploration of methods by which optimal feedback may be derived for the purpose of expediting the communication of a message point between an inanimate encoder and decoder. Our framework not only takes advantage of previous work to demonstrate its communication optimality from this perspective as well as from an information-theoretic perspective but also contributes two distinct advantages. First, our framework provides a simplified method based on optimal transport theory to generate optimal feedback signals between the computer and human in high dimension, while still preserving communication optimality. Second, our framework specifically lends itself to the integration of a human user by attempting to moderate the difficulty of the task presented to the user, while still preserving optimality. We demonstrate applications of our framework within the context of multi-agent brain-computer interfaces.