Neuroscience Forefront ReviewToward a model-based cognitive neuroscience of mind wandering

- Mind wandering is introduced in a model-based cognitive neuroscience framework.
- Neural and behavioral data provide mutual constraint on theories of mind wandering.
- We discuss latent mixture, hidden Markov and single-trial regression techniques.
- We conclude the study of mind wandering will benefit from model-based neuroscience.

People often “mind wander” during everyday tasks, temporarily losing track of time, place, or current task goals. In laboratory-based tasks, mind wandering is often associated with performance decrements in behavioral variables and changes in neural recordings. Such empirical associations provide descriptive accounts of mind wandering - how it affects ongoing task performance - but fail to provide true explanatory accounts - why it affects task performance. In this perspectives paper, we consider mind wandering as a neural state or process that affects the parameters of quantitative cognitive process models, which in turn affect observed behavioral performance. Our approach thus uses cognitive process models to bridge the explanatory divide between neural and behavioral data. We provide an overview of two general frameworks for developing a model-based cognitive neuroscience of mind wandering. The first approach uses neural data to segment observed performance into a discrete mixture of latent task-related and task-unrelated states, and the second regresses single-trial measures of neural activity onto structured trial-by-trial variation in the parameters of cognitive process models. We discuss the relative merits of the two approaches, and the research questions they can answer, and highlight that both approaches allow neural data to provide additional constraint on the parameters of cognitive models, which will lead to a more precise account of the effect of mind wandering on brain and behavior. We conclude by summarizing prospects for mind wandering as conceived within a model-based cognitive neuroscience framework, highlighting the opportunities for its continued study and the benefits that arise from using well-developed quantitative techniques to study abstract theoretical constructs.