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

Abstract

Signaled active avoidance (SigAA) is the key experimental procedure for studying the acquisition of instrumental responses toward conditioned threat cues. Traditional analytic approaches (e.g., general linear model) often obfuscate important individual differences, although individual differences in learned responses characterize both animal and human learning data. 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, even when a single animal is included in the data set (i.e., n = 1 level). We further show an online software that enables the easy application of our method to any SigAA data set.