A principled method to identify individual differences and behavioral shifts in signaled active avoidance

Signaled active avoidance (SigAA) is the key experimental procedure for studying theacquisition of instrumental responses towards conditioned threat cues. Traditional analyticapproaches (e.g. general linear model) often obfuscate important individual differences. However,individual differences models (e.g. latent growth curve modeling) typically require large samplesand onerous computational methods. Here, we present an analytic methodology that enables thedetection of individual differences in SigAA performance at a high accuracy based at the n=1level. We further show an online software that enables the easy application of our method to anySigAA data set.