Mind-wandering (MW) refers to ongoing mentation which occurs spontaneously, and largely autonomously, whenever an awake individual is not engaged in a cognitively demanding task. Alternative names to the term “MW” (Smallwood and Schooler, 2006
; Mason et al., 2007
) in past and recent literature include “day dreaming” (Giambra, 1979
), “task-unrelated images and thought” (Giambra and Grodsky, 1989
), “stimulus independent thought” (Teasdale et al., 1995
), “task-unrelated thought” (Smallwood et al., 2003
), “incidental self-processing” (Gilbert et al., 2005
), “inner speech” (Morin, 2009
), and “spontaneous thought” (Christoff et al., 2008
Conceptualized as a core element of what William James defined as the “stream of consciousness” (James, 1892
), MW, in various names and forms, has gained considerable attention in ancient and modern philosophy and in theoretical psychology. The robust, autonomous, and continual nature of this psychological process has led writers to suggest that rather than being an undesired lapse of attention to the external world (William James remarked, when he was accused of being absent-minded, that he was really just present-minded to his own thoughts; Barzun, 1983
), MW must have an important adaptive value for healthy cognition (Christoff et al., 2008
; Baars, 2010
). Yet much like the neural basis of MW, its adaptivity and the nature of its interaction with other cognitive processes remain a scientific blind spot.
In the relatively short history of cognitive neuroscience, which has inherited much of its models, paradigms, and findings from behavioral and cognitive psychology, MW is virtually absent (Smallwood and Schooler, 2006
) as a subject of research. The reluctance in the scientific arena to study MW can be accounted for by its non-behavioral characteristics when compared to more conventionally studied mental functions: MW occurs in the absence of any external cue; it is often unintended and even unaware; it takes its own course – probably driven by internally generated cues; and it is hard to trace back, replicate or report. However, a recent paradigm shift in functional neuroimaging holds a great promise for the development and establishment of MW research. The discovery of the “default-mode network” (DMN; Raichle et al., 2001
) and the following realization of the significance of spontaneous resting-state neural activity (Raichle, 2009
) dramatically launched a prosperous path in the scientific exploration of MW.
Default-mode network relates to a functionally meaningful neural network, which includes the medial prefrontal cortex (MPFC), the precuneus, the posterior cingulate cortex, and the inferior parietal and lateral temporal cortices (Figure ). In comparison to other functional neural networks, DMN has unique patterns of activity (Gusnard et al., 2001
; Raichle et al., 2001
): both in terms of energy consumption and in terms of the blood oxygen-level dependent (BOLD) signal, activation levels in this network were shown to descend below baseline during cognitively demanding tasks. Moreover, this network shows high activation levels at rest compared to task. These activation patterns and their possible functional meaning have received considerable attention in recent years, using independent as well as combined neuroimaging techniques (e.g., Ben-Simon et al., 2008
). Studies with clinical populations shed additional light on the critical functionality of the DMN by demonstrating that malfunctioning of the DMN is associated with several neurological, psychiatric, and psychological pathologies (for a review see Buckner et al., 2008
Figure 1 Results of overviewed studies in relation to DMN regions. DMN-related results of studies overviewed in this review, categorized by strategy, superimposed on a template brain. Light-grey markings denote DMN areas (in accordance with Buckner et al., 2008 (more ...)
Since the first reports describing it, DMN rest-related activity had been suggested to comprise a neural correlate of MW (Gusnard et al., 2001
). This proposition was based on two main features of the DMN: First of all, like MW, DMN activity occurs during rest and shows a reverse correlation with cognitive load (Mason et al., 2007
). Secondly, task-related activations in the medial prefrontal and parietal areas, which comprise substantial elements of the DMN, have been shown to occur during self-related tasks (Northoff and Bermpohl, 2004
; Spreng et al., 2009
). This has led writers to suggest that rest-related activations in these areas might subserve MW, in itself a process of self-related mentation (Baars, 2010
With the exceedingly growing body of information on neural activity in the wakeful, resting state, the shortage in accepted modus operandi regarding the scientific examination of MW has become a bottle neck, restraining further examination of the functionality of the DMN on the one hand and of the neural basis of MW on the other. However, several pioneering attempts have been made to study the relation between DMN activity and MW, yielding striking results. Converging the solutions to the challenge of quantifying and scientifically studying MW presented in these studies portrays an array of potential strategies to address the question of a DMN–MW association.
The current review aims to facilitate the scientific exploration of the neural correlates of MW by overviewing existing literature and defining, respectively, five methodological strategies for studying MW within a functional neuroimaging paradigm. Two of these strategies include direct measurements of MW (strategies A1 and A2), whether in real time – during rest or task performance, or retrospectively. Three additional strategies (Strategies B1, B2, and B3) rely on theoretical assumptions regarding MW and self related or cognitive functioning, as well as on the known functionality of networks emerging from connectivity analysis performed on data acquired during the resting state. Through the prism of these five strategies, we review existing literature and findings regarding MW published mainly in the recent decade. Each strategy will be presented in light of its advantages and disadvantages as well as the degree of its fitting to various paradigms and data analysis techniques in experimental neuroimaging.