Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis

stimation of learning curves is ubiquitously based on 

 constant within the moving windows, which may often not be the case. In the present study

 lead to systematic errors in the analysis of learning curves

 explore the dependency of these errors on window size, different statistical models, and 

subject data, we propose adequate statistical methods for the estimation of learning curves and construction of confidence intervals

Applied to data from an avoidance learning experiment with rodents, these methods revealed performance changes occurring

scales across training sessions. Our work 

proper assessment of the behavioral dynamics of learning at high temporal resolution can highlight specific learning processes, thus allow

 further disambiguates the interpretation of neurophysiological signal changes recorded during training in relation to learning.

Learning, the acquisition of knowledge through experience, manifests as behavioral changes 

 on a multitude of neural and cognitive processes 

 act on different spatial and temporal scales [1–3]; however, many of these processes are not experimentally accessible. Therefore, any 

learning experiment is influenced by numerous uncontrolled variable

. This entails a certain degree of unaccountable variability 

 as well as between subjects [4]. As a consequence, 

single behavioral responses of individual subjects are difficult 

 behavioral performance is analyzed in moving trial windows. At any time, performance is quantified by the success probability, 

 is estimated as the proportion of correct trials among all trials of a window. The window size is adjusted according to the particular interest of the experimenter

. Learning is then identified from the time course of the resulting learning curve as 

, we frame the learning curve analysis as the statistical problem of estimating the Bernoulli probability of a correct response in a trial 

the observed responses in a small (n &lt; 200) trial window. We propose different statistical methods designed for 

 these success probabilities from small samples, 

 also provides more appropriate confidence intervals than conventional analysis. We 

conventional analysis. For this purpose, we utilize

 simulated and experimental data derived from an exemplary learning experiment

way active avoidance paradigm in a shuttle-box [4, 12]. Our evaluation 

moving window estimation of learning curves can be prone to various random and systematic errors.

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