abstract = "An essential component of augmented cognition (AC) is
developing robust methods of extracting reliable and
meaningful information from physiological measures in
real-time. To evaluate the potential of skin
conductance (SC) and pupil diameter (PD) measures, we
used a dual-axis pursuit tracking task where the
control mappings repeatedly and abruptly rotated
90degree throughout the trials to provide an immediate
and obvious challenge to proper system control. Using
these data, a model-building technique novel to these
measures, genetic programming (GP) with scaled symbolic
regression and Age Layered Populations (ALPS), was
compared to traditional linear discriminant analysis
(LDA) for predicting tracking error and control-mapping
state. When compared with traditional linear modelling
approaches, symbolic regression better predicted both
tracking error and control mapping state. Furthermore,
the estimates obtained from symbolic regression were
less noisy and more robust.",
notes = "1 Department of Psychology and Communication Studies.
2 Department of Computer Science University of Idaho
Moscow,
ID