  Six significant new methodological developments of the previously-presented
"metastimuli architecture" for human learning through machine learning of
spatially correlated structural position within a user's personal information
management system (PIMS), providing the basis for haptic metastimuli, are
presented. These include architectural innovation, recurrent (RNN) artificial
neural network (ANN) application, a variety of atom embedding techniques
(including a novel technique we call "nabla" embedding inspired by
linguistics), ANN hyper-parameter (one that affects the network but is not
trained, e.g. the learning rate) optimization, and meta-parameter (one that
determines the system performance but is not trained and not a hyper-parameter,
e.g. the atom embedding technique) optimization for exploring the large design
space. A technique for using the system for automatic atom categorization in a
user's PIMS is outlined. ANN training and hyper- and meta-parameter
optimization results are presented and discussed in service of methodological
recommendations.
