Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik
Doi
Abstract
Estimation of learning curves is ubiquitously based on proportions of
correct responses within moving trial windows. In this approach, it is
tacitly assumed that learning performance is constant within the moving
windows, which, however, is often not the case. In the present study we
demonstrate that violations of this assumption lead to systematic errors in
the analysis of learning curves, and we explored the dependency of these
errors on window size, different statistical models, and learning phase. To
reduce these errors for single subjects as well as on the population level,
we propose adequate statistical methods for the estimation of learning curves
and the construction of confidence intervals, trial by trial. Applied to data
from a shuttle-box avoidance experiment with Mongolian gerbils, our approach
revealed performance changes occurring at multiple temporal scales within and
across training sessions which were otherwise obscured in the conventional
analysis. The proper assessment of the behavioral dynamics of learning at a
high temporal resolution clarified and extended current descriptions of the
process of avoidance learning. It further disambiguated the interpretation of
neurophysiological signal changes recorded during training in relation to
learning