期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2016
卷号：113
期号：52
页码：14898-14903
DOI：10.1073/pnas.1605155114
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：SignificanceThe human brain continuously and automatically processes information concerning its internal and external context. We demonstrate the elicitation and subsequent detection and decoding of such "automatic interpretations" by means of context-sensitive probes in an ongoing human-computer interaction. Through a sequence of such probe-interpretation cycles, the computer accumulates responses over time to model the operators cognition, even without that person being aware of it. This brings human cognition directly into the human-computer interaction loop, expanding traditional notions of "interaction." The concept introduces neuroadaptive technology--technology which automatically adapts to an estimate of its operators mindset. This technology bears relevance to autoadaptive experimental designs, and opens up paradigm-shifting possibilities for human-machine systems in general. The effectiveness of todays human-machine interaction is limited by a communication bottleneck as operators are required to translate high-level concepts into a machine-mandated sequence of instructions. In contrast, we demonstrate effective, goal-oriented control of a computer system without any form of explicit communication from the human operator. Instead, the system generated the necessary input itself, based on real-time analysis of brain activity. Specific brain responses were evoked by violating the operators expectations to varying degrees. The evoked brain activity demonstrated detectable differences reflecting congruency with or deviations from the operators expectations. Real-time analysis of this activity was used to build a user model of those expectations, thus representing the optimal (expected) state as perceived by the operator. Based on this model, which was continuously updated, the computer automatically adapted itself to the expectations of its operator. Further analyses showed this evoked activity to originate from the medial prefrontal cortex and to exhibit a linear correspondence to the degree of expectation violation. These findings extend our understanding of human predictive coding and provide evidence that the information used to generate the user model is task-specific and reflects goal congruency. This paper demonstrates a form of interaction without any explicit input by the operator, enabling computer systems to become neuroadaptive, that is, to automatically adapt to specific aspects of their operators mindset. Neuroadaptive technology significantly widens the communication bottleneck and has the potential to fundamentally change the way we interact with technology.
