Fall 2016 Talk

Abstract:  Closed loop learning emphasizes real-time performance feedback and adaptive challenge designs. My translation research has focused on developing closed loop learning approaches that fundamentally improve signal-to-noise resolution of information processing i.e. enhance goal relevant signals and suppress goal-irrelevant distractions. First studies were implemented as cross-species experiments in aging, and showed that adaptive learning enhances signal-to-noise ratios in both animal and human neural processing and behavior (Mishra et al., Neuron 2014).

In a first digital intervention trial applied in a global mental health setting, I recently showed that closed loop signal-to-noise training also benefits children with ADHD, with sustained clinical gains (Mishra et al., Nature Translational Psychiatry 2016). Now I ask, how do we further improve the closed loop training approaches (Mishra et al., Neuron 2016), and am developing brain computer interfaces for enhancing the effects of adaptive learning, with a focus on children with ADHD. Another arm of my research investigates, whether such neuro-cognitive enrichment strategies can also be tailored to benefit neglected children in foster-care institutions in global communities.