A Principled Method to Identify Individual Differences and Behavioral Shifts in Signaled Active Avoidance

This is a preprint to a previous version of the manuscript. There may be deviations from the final paper. Please refer to the main article for the list of authors and their affiliations. ## MAIN ABSTRACT ## Signaled active avoidance (SigAA) is the key experimental procedure for studying the acquisition of instrumental responses towards conditioned threat cues. Traditional analytic approaches (e.g. general linear model) often obfuscate important individual differences. However, individual differences models (e.g. latent growth curve modeling) typically require large samples and onerous computational methods. Here, we present an analytic methodology that enables the detection of individual differences in SigAA performance at a high accuracy based at the n=1 level. We further show an online software that enables the easy application of our method to any SigAA data set.