ArticleLearning & memory (Cold Spring Harbor, N.Y.)2018
A principled method to identify individual differences and behavioral shifts in signaled active avoidance.
Article in Learning & memory (Cold Spring Harbor, N.Y.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Article
- Safety Signals Enable Single-Episode Active Avoidance paradigm and Expose Threat Generalization in Tuberous Sclerosis Complex.bioRxiv : the preprint server for biology · 2026Article
- Prefrontal cortex projections to the nucleus reuniens suppress freezing following two-way signaled avoidance training.Learning & memory (Cold Spring Harbor, N.Y.) · 2020Article
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Authors and funding
5 authors.
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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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.