Latent-state and model-based learning in PTSD.

Recent research suggests evidence for biases in latent-state and model-based learning in post-traumatic stress disorder (PTSD). Biases in latent-state and model-based learning potentially explain a larger set of behavioral and neurocircuitry findings in PTSD. Biases in latent-state and model-based learning potentially operate as a common target of both exposure therapy and cognitive processing therapy for PTSD. More research is needed to inform how to drive learning towards specific model updates during treatment. More research is needed integrating biases in latent-state and model-based learning within existing neurocircuitry and behavior models of PTSD. Post-traumatic stress disorder (PTSD) is characterized by altered emotional and behavioral responding following a traumatic event. In this article, we review the concepts of latent-state and model-based learning (i.e., learning and inferring abstract task representations) and discuss their relevance for clinical and neuroscience models of PTSD. Recent data demonstrate evidence for brain and behavioral biases in these learning processes in PTSD. These new data potentially recast excessive fear towards trauma cues as a problem in learning and updating abstract task representations, as opposed to traditional conceptualizations focused on stimulus-specific learning. Biases in latent-state and model-based learning may also be a common mechanism targeted in common therapies for PTSD. We highlight key knowledge gaps that need to be addressed to further elaborate how latent-state learning and its associated neurocircuitry mechanisms function in PTSD and how to optimize treatments to target these processes. [ABSTRACT FROM AUTHOR]