The influence of aerobic exercise on model-based decision making in women with posttraumatic stress disorder.

Individuals with PTSD often exhibit deficits in executive functioning. An unexplored aspect of neurocognitive functions associated with PTSD is the type of learning system engaged in during decision-making. A model-free (MF) system is habitual in nature and involves trial-and-error learning that is often updated based on the most recent experience (e.

Aerobic exercise after extinction learning reduces return of fear and enhances memory of items encoded during extinction learning.

Fear conditioning paradigms are widely used in laboratory settings to discover treatments that enhance memory consolidation and various fear processes (extinction learning, limit return of fear) that are relevant targets of exposure-based therapies. However, traditional lab-based paradigms often use the exact same conditioned stimuli for acquisition and extinction (typically differentiated with a context manipulation), whereas the opposite is true in clinical settings, as exposure therapy rarely (if ever) uses precisely the exact same stimuli from an individual's learning history. Accordingly, this study utilized a novel three-day category-based fear conditioning protocol (that uses categories of non-repeating objects [animals and tools] as conditioned stimuli during fear conditioning and extinction) to determine if aerobic exercise enhances the consolidation of extinction learning (reduces return of fear) and memory (for items encoded during extinction) during subsequent tests of extinction recall.

Large-scale neural network computations and multivariate representations during approach-avoidance conflict decision-making.

Many real-world situations require navigating decisions for both reward and threat. While there has been significant progress in understanding mechanisms of decision-making and mediating neurocircuitry separately for reward and threat, there is limited understanding of situations where reward and threat contingencies compete to create approach-avoidance conflict (AAC). Here, we leverage computational learning models, independent component analysis (ICA), and multivariate pattern analysis (MVPA) approaches to understand decision-making during a novel task that embeds concurrent reward and threat learning and manipulates congruency between reward and threat probabilities.