Mindfulness and distraction:
Using computational models to explore the mechanisms
Marieke van Vugt
Department of Artificial Intelligence (ALICE)
University of Groningen
The Netherlands
Mindfulness has been shown to affect performance on various cognitive tasks, but
how does it do that? We can use computational models in combination with
behavioral tasks and neuroscientific methods to explain how mindfulness affects
cognition. For example, using modeling we can show that intensive concentration
meditation affects the quality of mental representations of stimuli. We can also use
these models to describe mindfulness and how it affects the meditator's attitude to
distraction. This may have testable implications for people's behaviour in the real
world.
About the speaker:
Marieke van Vugt is an assistant professor at the Department of Artificial Intelligence
(ALICE) at the University of Groningen. She obtained her PhD in neuroscience
focusing on the role of brain oscillations in recognition memory with Dr Michael
Kahana at the University of Pennsylvania in 2008. She then went on to do
postdoctoral research on the neural correlates of decision making with Dr Jonathan
Cohen at Princeton University before starting her own group as a tenure track
assistant professor in Groningen in 2010.
Her research focuses on dissecting the fundamental cognitive operations and neural
processes involved in making decisions, and on how our decisions are affected by
meditation practice. She makes use of a combination of computational modeling,
neuroscience, and experimental psychology tools. She has developed a unique
approach to studying meditation by using computational models of cognition. Using
these methods, she showed that meditation reduces mental noise. She has also
developed a novel method to track perseverative cognition, with which she showed
that after mindfulness-based cognitive therapy, people were less "stuck" in recalling
negative memories. More recently she has started to build models of the meditation
and distraction process itself.