Mindfulness meditation training is implemented globally in a large number of settings, including health and education, and a fast-growing scientific literature supports the positive effects mindfulness on mental and physical health. However, scientists are still struggling to understand the “active ingredients” in mindfulness meditation training – the mental capacities that are trained and impacted by mindfulness meditation (such as attention and awareness) and lead to its positive effects. This is mainly due to a limited scientific capacity to “look inside the black box of meditation” and measure what is going on in people’s minds during meditation. 

This interdisciplinary research project, supported by the Data Science Research Center, is aimed to tackle the black box of mindfulness meditation via a unique combination of computational and behavioral measurement methods. The project team includes researchers from the Observing Minds Lab at the Department of Psychology and a machine learning scientist at the Department of Computer Science. It involves application of machine learning methods to data collected in two studies via a novel measurement method of attention and awareness during meditation. Using these methods, the research team is aiming to discover key mental capacities trained during mindfulness mediation, as expressed in complex moment-to-moment interactions between attention and awareness during meditation. Doing so, the team is hoping to uncover the attentional mental capacities impacted by mindfulness training that may give rise to its positive effects. Data analysis is currently underway. The project’s findings may advance understanding of “active ingredients” in mindfulness meditation training and could inform future attempts to optimize the positive effects of mindfulness meditation.