Due to difficulties in elucidating neurobiological aspects of psychological disorders, the National Institute of Mental Health (NIMH) created the Research Domain Criteria (RDoC), which encourages novel conceptualizations of the relationship between neurobiological circuitry and clinical difficulties. This approach is markedly different from the Diagnostic and Statistical Manual of Mental Disorders (DSM) based approach that has dominated clinical research to date. Thus, RDoC necessitates exploration of novel experimental and statistical approaches. Fear learning paradigms represent a promising methodology for elucidating connections between acute threat (“fear”) circuitry and fear-related clinical difficulties. However, traditional analytical approaches rely on central tendency statistics, which are tethered to a priori categories and assume homogeneity within groups. Growth Mixture Modeling (GMM) methods such as Latent Class Growth Analysis (LCGA) may be uniquely suited for examining fear learning phenotypes. However, just three extant studies have applied GMM to fear learning and only one did so in a human population. Thus, the degree to which classes identified in known studies represent characteristics of the general population and to which GMM methodology is applicable across populations and paradigms is unclear. This preliminary study applied LCGA to a fear learning lab study in an attempt to identify heterogeneity in fear learning patterns based on a posteriori classification. The findings of this investigation may inform efforts to move toward a trans-diagnostic conceptualization of fear learning. Consistent with the goals laid out in RDoC, explication of fear learning phenotypes may eventually provide critical information needed to spur innovation in psychotherapeutic and psychopharmacological treatment. / Master of Science / To date, most clinical psychology research has been based on the Diagnostic and Statistical Manual of Mental Disorders (DSM), which is a catalog of mental health disorders that was originally designed to facilitate communication among clinicians. Many experts contend that this approach has hampered progress in the field of biological clinical psychology research. Thus, the National Institute of Mental Health (NIMH) created a new template for biological clinical psychology research called the Research Domain Criteria (RDoC). Since RDoC calls for a complete overhaul in the conceptualization of clinical dysfunction, this approach requires statistical and experimental innovation. One traditional experimental approach that may be helpful in understanding the RDoC topic of acute threat (“fear”) is called Pavlovian Fear Learning (PFL). However, traditional PFL studies have utilized statistical methods that are based on comparing group averages and require researchers to determine groups of interest based on theory before the study begins. This is problematic because RDoC calls for research that begins with evidence rather than theory. Growth Mixture Modeling (GMM) is a statistical methodology that may allow researchers to analyze fear learning data without having to begin with theoretically determined categories such as DSM disorders. However, little research has tested how well this approach would work. This study is just the second to apply a GMM approach to a human PFL study. The findings from this investigation may inform efforts to develop a statistical technique that is well suited for RDoCian research and may also spur innovation in psychotherapeutic and psychopharmacological treatment.