Determining the Effectiveness of Human Interaction in Human-in-the-Loop Systems by Using Mental States

A self-adaptive software is developed to predict the stock market. It’s Stock
Prediction Engine functions autonomously when its skill-set suffices to achieve its goal,
and it includes human-in-the-loop when it recognizes conditions benefiting from more
complex, expert human intervention. Key to the system is a module that decides of
human participation. It works by monitoring three mental states unobtrusively and in real
time with Electroencephalography (EEG). The mental states are drawn from the
Opportunity-Willingness-Capability (OWC) model. This research demonstrates that the
three mental states are predictive of whether the Human Computer Interaction System
functions better autonomously (human with low scores on opportunity and/or
willingness, capability) or with the human-in-the-loop, with willingness carrying the
largest predictive power. This transdisciplinary software engineering research
exemplifies the next step of self-adaptive systems in which human and computer benefit from optimized autonomous and cooperative interactions, and in which neural inputs
allow for unobtrusive pre-interactions.