Abstract
Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. Thereby, 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 in the analysis of single-subject data 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 an avoidance learning experiment with rodents, these methods revealed performance changes occurring at multiple time scales within and across training sessions which were otherwise obscured in the conventional analysis. Our work shows that the proper assessment of the behavioral dynamics of learning at high temporal resolution can shed new light on specific learning processes, and, thus, allows to refine existing learning concepts. It further disambiguates the interpretation of neurophysiological signal changes recorded during training in relation to learning.
Highlights
Learning, the acquisition of knowledge through experience, manifests as behavioral changes in the course of training
The quality of learning curve estimation relies on the chosen window size, the employed statistical model, the learning phase, and on discontinuities arising from breaks between training sessions
To achieve this— both for single subjects and on the population level—we introduced appropriate statistical methods for estimating Bernoulli success probabilities and their confidence intervals in small trial windows
Summary
The acquisition of knowledge through experience, manifests as behavioral changes in the course of training. Learning behavior relies on a multitude of neural and cognitive processes which act on different spatial and temporal scales [1,2,3]; many of these processes are not accessible experimentally. Any particular learning experiment is influenced by numerous uncontrolled variables. This entails a certain degree of unaccountable variability of behavior across time, within a subject as well as between subjects [4]. Single behavioral responses of individual subjects are difficult to interpret with respect to learning.
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