My main mission is investigating how, when, and why we mind-wander, and how distraction in general affects what we do and decide. My research involves neuroscience, computational modelling, and behavioural testing, and can be divided into several main strands:

Understanding the cognitive mechanisms underlying mind-wandering

Mind-wandering is a cognitive process that occurs when we drop our main task for a moment and instead think about other things. I have created the first computational model of mind-wandering (van Vugt et al, 2015), based on the idea that mind-wandering arises when a task goal loses competition from a distraction goal (the “thought pump”). I have developed various tasks to manipulate the content of mind-wandering, in particular, the extent to which it is difficult to disengage from (e.g., van Vugt & Broers, 2016). In on-going work I examine how we can relate this to psychopathology such as depression, in collaboration with my colleague Marie-Jose van TolmwModelFig

Modelling the effects of meditation on cognition

In this research I aim to develop a cognitive theory for how mindfulness and meditation affect cognition, and therefore determine what we do. For example, I have shown that Mindfulness-Based Cognitive Therapy affects the way in which depressed patients organize their recall, which I argue is a model for the
process of rumination (van Vugt et al. (2012)). I am currently developing a computational model of mind-wandering and meditation, a project I kicked off as a Mind & Life visiting scholar in Amherst, MA, supported by the Mind & Life Institute. I am also involved as a member of the Mind and Life Europe verein.

visiting scholar at Amherst, MA

The effect of monastic debate on cognition, emotion, and social connectedness

In addition to mindfulness and meditation, I have also started to investigate how monastic debate affects cognition, emotion, and social connectedness. Monastic debate is a practice engaged in by Tibetan Buddhist monks, in which they use specific physical movements and specific forms of argumentation to learn more about the material they are studying. Based on our discussions with our monastic collaborators, we think these practices also involve training ofmonasticDebateStudy emotion regulation, critical thinking, and confidence. The unique feature of this project is that it combines teaching our monastic collaborators to become scientists with studying the debate itself. As such, it is a truly interdisciplinary collaboration. More information: and Science for Monks.

Evidence accumulation for decisions

In terms of decisions I am very curious about how decisions are affected by the information we have available in our memory. A popular theoretical framework for how we make decisions are evidence accumulation models such as the drift diffusion models. I have shown that in perceptual decisions, the process of evidence accumulation is
correlated with the amplitude of 4-9 Hz theta oscillations (van Vugt et al. (2012)). In on-going research, I further explore how brain oscillations are involved with several aspects of this evidence accumulation process, e.g., adapting your decision threshold after you
have made an error (van Vugt et al., 2011). And can we observe similar accumulation processes when evidence is not perceptual but mnemonic in nature? How do we adjust
our decisions after we made an error?DDMeeg

Neural correlates of cognitive architectures

I have been pioneering model-based neuroscience–neuroscience that makes use of cognitive models of cognition to better understand brain activity. Upon moving to Groningen, I have started to investigate whether we can use the cognitive architecture ACT-R to understand the role of brain oscillations in cognition. I have shown that ACT-R’s working memory resource correlates with fronto-parietal theta oscillations, and its
visual resource with more posterior oscillations (van Vugt, 2014). The cognitive architecture can also make predictions for, and help us interpret, patterns of oscillatory synchronization that are thought to reflect information transfer.ACTRbrain