In this section, we first address the relationship between mind wandering, negative mood, and depression in general. Then, in further exploration of such a relationship, we introduce some determinative factors and conditions which can particularly affect the relationship between mind wandering, unhappiness and negative mood.

Mind wandering and the DN are correlated with negative mood and depression

It seems that mind wandering and negative mood are correlated positively. However, the direction of the relationship is unclear. Some researchers propose that there may even be a third variable as the common cause of mind wandering and negative mood (Mason, Brown, Mar, & Smallwood, 2013). Regarding the direction of the relationship, two groups of ideas are distinguishable. One group argues that negative mood can be a precursor of mind wandering (Poerio et al., 2013; Smallwood et al., 2009; Stawarczyk et al., 2013). The other group suggests that mind wandering is antecedent of negative mood (Killingsworth & Gilbert, 2010; Marchetti, Koster, & De Raedt, 2012; Mor & Winquist, 2002; Mrazek et al., 2013; Perkins et al., 2015). In this article we address the ideas proposed by the latter group.

Mind wandering may also be a precursor for depressive rumination (Hamilton et al., 2011; Murphy, Macpherson, Jeyabalasingham, Manly, & Dunn, 2013). It is probably the thinking style which can increase the likelihood and the intensity of depression and excessive ruminations predisposed to the increased risk of depression (Nolen-Hoeksema, 2000; Nolen-Hoeksema & Morrow, 1993; Nolen-Hoeksema, Morrow, & Fredrickson, 1993). Correlational examinations (Giambra & Traynor, 1978; Stawarczyk, Majerus, Van Der Linden, & D’Argembeau, 2012) demonstrate that the frequency of mind wandering in everyday life is associated with the higher levels of depression. Therefore, mind wandering is correlated with negative mood as well as depression.

Such a correlation, on the neural level, suggests that the activity of the DN (as the neural correlate of mind wandering) may have positive correlation with negative mood. Various studies have focused on the role of the DN in depressive disorders. These studies show that depression is related to the increased activity and connectivity of the DN (e.g., Berman et al., 2011; Hahn et al., 2012; Kaiser et al., 2015; Marchetti et al., 2012; Marchetti et al., 2013; McCabe & Mishor, 2011; Piguet et al., 2016; Posner et al., 2013; Simon & Engström, 2015). For example, some researchers (Marchetti et al., 2012; Marchetti et al., 2013) argue that rumination and cognitive reactivity, as two kinds of cognitive risk factors for depression, are outcomes of DN dysfunctions. They have shown that rumination and cognitive reactivity are related to increased DN functional connectivity. Posner et al. (2013) have found that at the baseline, compared to healthy individuals, patients with dysthymic disorder (DD) increase DN connectivity. Also, they showed that antidepressant drugs can normalize increased baseline connectivity in the DN of patients with dysthymic disorder. Given that alteration in DN connectivity is observed in patients with DD and/or with major depressive disorder (MDD), Posner et al. (2013) speculate that increased DN connectivity may cause vulnerability for developing MDD. Also, by showing increased dorsal medial prefrontal cortex connectivity (dmPFC) in depression, McCabe and Mishor (2011) suggest that the DN may be a target for antidepressant drug treatment. Their data show that antidepressant drugs can decrease the elevated resting state functional connectivity. This has been observed in depressed patients independently of the mood change and in areas that mediate reward and emotional processing in the brain.

The above observations suggest a correlation between mind wandering, negative mood and depression. It seems that increased activity of the DN may be a hallmark of depression. In the next section, we discuss conditions under which mind wandering may or may not lead to negative mood or depression.

Important factors and conditions affecting the relationship between mind wandering and negative mood

There are factors which influence the relationship between mind wandering and negative mood. Here, according to the content-regulation hypothesis (Smallwood & Andrews-Hanna, 2013), a key factor is the content of mind wandering (Andrews-Hanna et al., 2013; Poerio et al., 2013; Ruby et al., 2013). This hypothesis suggests that the “relationship between self-generated thought and psychological wellbeing depends on assessing how individuals regulate the content of their mental experiences so as to maximize thoughts with a productive outcomes, and minimize those which are detrimental to their happiness or other life outcomes” (Smallwood & Andrews-Hanna, 2013, p. 4). For example, Ruby et al. (2013) show that thoughts about future and self are linked to the subsequent positive mood and thoughts about past and others are linked to the subsequent negative mood. Also, Franklin et al. (2013) show that the presence of interesting thoughts during mind wandering increases positive mood.

Similarly, Gruberger et al. (2013) show that the relationship between mind wandering and negative mood can be affected by some factors and conditions but, contrary to the previous studies, they do not take the content of mind wandering into account. By comparing negative affect, as a consequence of mind wandering, across two groups, that is, high-vigilance and low-vigilance individuals during rest, they argue that rest related negative affect (RRNA) is not necessarily an outcome of mind wandering and may be a phenomenon of its own.

Gruberger et al. (2013) calculated the vigilance level of each participant after 15-minute resting-state simultaneous fMRI-EEG scans. Then, they divided participants into two groups: high and low vigilance.Footnote 2 They used an algorithm (Olbrich et al., 2009) which included five stages of vigilance, based on EEG markers, from the most alert to the lowest vigilance levels before sleep onset. Affective assessment was performed before and after scans. Their experiment confirms the hypothesis that the availability of attentional resources is a necessary condition for rest related negative affect and low attentional resources during rest will create less negative affect. Their findings show that negative affect does not rise when levels of vigilance decrease whereas in participants who were vigilant during rest, negative affect rises. Given that mind wandering occurred in both groups, they argue that negative affect is a result of heightened attention toward mind wandering processes. Their findings can provide a basis for suggesting a mechanism in which when vigilance is maintained, individuals attend to mind wandering more and therefore there is more self-monitoring, while during lowered vigilance, although mind wandering occurs, there are no monitoring processes, resulting in reduced negative affect.

Regarding underlying mechanisms of RRNA on the neural level, only in the high vigilance group there was a significant increase in functional connectivity (FC) levels between ventral anterior cingulate cortex (ACC), a region in the DN associated with emotional processing, and dorsal ACC, a region in the executive network associated with monitoring of self and other’s behavior. These heightened FC levels further correlate with reported negative affect among high vigilance group (Gruberger et al., 2013). Similarly, the results of a meta-analysis show that major depressive disorder is associated with increased functional connectivity within the DN (as mentioned earlier), and increased FC between frontoparietal control network and regions of the DN that may “reflect ongoing rumination or an underlying bias for control systems to allocate resources toward internal thoughts at the cost of getting engaged in the external world” (Kaiser et al., 2015, p. 2). These are the hallmarks of depression.

The findings of Gruberger et al. (2013) can be interpreted that retaining high-vigilance levels during mind wandering causes negative mood. However, there can be other possible interpretations. For example, in healthy individuals, EEG-vigilance stages are associated with some indicators of ANS (autonomous nervous system) activity such as heart rate. High EEG-vigilance stages are associated with high heart rate. Although there is no agreement on the direction of causal relationship between high EEG-vigilance and ANS activity (Arns, Gunkelman, Olbrich, Sander, & Hegerl, 2011; Olbrich et al., 2011), it is possible that higher ANS activity during experiment leads to higher vigilance. The higher ANS activity may result from some states of participants such as negative affect and/or anxiety (Brown, Barton & Lambert, 2009; Huang et al., 2015; Olbrich et al., 2016). It is also possible that a third factor causes negative mood in participants. High vigilance can be related to individual and environmental factors that modulate brain arousal such as sleep deficits, consumption of substances that affect vigilance (e.g., caffeine, nicotine), and disease-related factors (Hegerl, Sander, & Hensch, 2016). As discussed below, although none of the above alternative interpretations can be rejected, Gruberger et al. (2013) still believe that vigilance can be considered as a mediating factor for negative affect.

As we mentioned earlier, Gruberger et al. (2013) performed affective assessment before and after scans. Given that an increase in negative affect from baseline was observed among high vigilance group but not in low vigilance group and there were no significant differences at baseline between the two groups in terms of negative affect, it can be concluded that “the rise in negative affect following rest was dependent on a maintained level of vigilance: when vigilance was diminished, negative affect level did not increase.” (Gruberger et al., 2013, p. 6). This idea can be supported by some studies which demonstrate that affective disorders are linked to the regulation of EEG-vigilance. According to these studies, disturbed vigilance regulation is related to the pathogenesis of affective disorders (Hegerl & Hensch, 2014; Sander, Hensch, Wittekind, Böttger, & Hegerl, 2015) and it is believed that symptoms of depression are the autoregulatory mechanisms for reducing levels of vigilance (Arns et al., 2011; Hegerl et al., 2016).

Apart from Gruberger et al.’s (2013) research, we could not find any studies in which the mediatory role of vigilance states with regard to the effects of mind wandering on negative mood is examined. However, the latter research alone can still be inspirational to conduct two research projects that explore (1) the relationships between vigilance states and negative mood/depression, and (2) the role of monitoring processes and dACC in negative mood/depression. In what follows, we discuss studies which confirm the worthiness of such research projects.

First, some studies suggest that different types of vigilance regulation are linked to psychopathological syndromes confirming the idea that there is a relation between EEG vigilance stages and major depressive disorder (Hegerl & Hensch, 2014; Hegerl, Wilk, Olbrich, Schoenknecht & Sander, 2012; Olbrich et al., 2012; Schmidt et al., 2016). For example, in line with the assumption that the most stable EEG vigilance regulation patterns are markers of depression, Olbrich et al. (2012) observed an increased number of high EEG vigilance stages in individuals with major depressive disorder compared to healthy controls. They argue that withdrawal and sensation avoidance in depression is a reaction to be chronically in high vigilance state. Hegerl et al. (2012) demonstrate that a hyperstable vigilance regulation is observed in individuals with major depressive disorder in which, as one of its characteristics, there is missing or delayed decline in vigilance. The results of their study confirm that during rest, compared to control group, depressed patients show less and delayed declines to lower vigilance stages. They also noted that hyperstable vigilance regulation could be related to constant inner tension and inability to relax observed in depressed patients. Schmidt et al. (2016) also reached a similar result. Their study supported the idea of hyperstable vigilance regulation in depression. They demonstrate that depressed individuals show fewer declines to lower EEG vigilance stages and mostly are in the high EEG vigilance stages.

Here, alpha wave (8–12 Hz) can be a reliable measure of vigilance (e.g., De Gennaro, Ferrara, & Bertini, 2001; De Gennaro et al., 2005; Olbrich et al., 2009). So, an increase in alpha wave in the frontal lobe is known as a marker of decreased vigilance (De Gennaro et al., 2005; Olbrich et al., 2009). The relation between high EEG vigilance and depression can also be indicative of the relation between frontal alpha asymmetry and depression (Allen & Reznik, 2015; Cantisani et al., 2015; Choi, Chi, Chung, Kim, Ahn & Kim, 2011; Jesulola, Sharpley, Bitsika, Agnew, & Wilson, 2015; Vogt, Schneider, Brümmer, & Strüder, 2010). The difference between electrical activity in the left and right frontal lobes during rest under EEG assessment, that is, frontal lobe asymmetry (FLA) (Henriques & Davidson, 1990, 1991, 1997; Jesulola et al., 2015), may be a neurophysiological biomarker of depression risk (Allen & Reznik, 2015; Gollan et al., 2014; Stewart, Bismark, Towers, Coan & Allen, 2010). It is proposed that electrical activity in the left prefrontal cortex is related to approach behaviors, that is, engagement with pleasant stimuli (Davidson, 1998), and activation of the right prefrontal cortex is related to withdrawal behaviors, that is, disengagement with or avoiding aversive stimuli (Sutton & Davidson, 1997). Thus, hypoactivation of the left frontal lobes is associated with some symptoms of depression, for example, anhedonia (Trew, 2011), and hyperactivation of the right prefrontal regions is associated with hypervigilance, behavioral inhibition, and withdrawal (Sutton & Davidson, 1997). Depressed individuals show relatively greater electrical activity in the right frontal lobe than in the left frontal lobe (Allen, Urry, Hitt & Coan, 2004; Baehr, Rosenfeld, Baehr & Earnest, 1998; Carvalho et al., 2011; Deslandes et al., 2008; Gotlib, Ranganath, & Rosenfeld, 1998; Henriques & Davidson, 1990).

In the above studies, measurement of the differences between electrical activity across frontal lobes in depressed persons is described in terms of alpha power so that high alpha power is indicative of less overall electrical activity (hypoactivation) while low alpha power is indicative of greater overall electrical activity (hyperactivation) (Cook, O’Hara, Uijtdehaage, Mandelkern & Leuchter, 1998; Laufs et al., 2003). Therefore, it can be hypothesized that EEG asymmetry in depression is accompanied by lesser alpha power in the right frontal lobe and greater alpha power in the left frontal lobe.

In sum, taking into consideration that right frontal lobe plays an active role in withdrawal behaviors and becomes more active (lesser alpha power) in depression, it can be concluded that frontal alpha asymmetry is probably associated with an important aspect of all depressive behaviors, that is, withdrawal from uncontrollable aversive stressors (Cantisani et al., 2015; Jesulola et al., 2015). In line with these observations, some studies show that changing frontal asymmetry by repetitive transcranial magnetic stimulation (rTMS) and neurofeedback training can be an effective treatment for major depressive disorder (Baehr et al., 1998; Choi et al.,2011; Mantovani, Aly, Dagan, Allart & Lisanby, 2013; Noda et al., 2013; Peeters, Oehlen, Ronner, van Os, & Lousberg, 2014). Also, it is demonstrated that meditation therapy and mindfulness-based cognitive therapy can have positive effects on depression, mostly by increasing left frontal activity (Barnhofer, Chittka, Nightingale, Visser, & Crane, 2010; Barnhofer et al., 2007; Keune, Bostanov, Hautzinger, & Kotchoubey, 2013; Moynihan et al., 2013).

Secondly, as we saw, Gruberger et al. (2013) suggest that more monitoring processes are associated with more negative mood. They also emphasize the role of dACC in such association. In what follows, we discuss studies which confirm and support the latter claims.

Several studies have shown that, first, dACC activity is correlated with conflict detection (Botvinick, Braver, Barch, Carter, & Cohen, 2001; Botvinick, Cohen, & Carter, 2004; Botvinick, Nystrom, Fissell, Carter, & Cohen, 1999; Cohen, Botvinick, & Carter, 2000; Egner & Hirsch, 2005); second, conflict is aversive (Hajcak & Foti, 2008; Lindström, Mattsson-Mårn, Golkar, & Olsson, 2013; Spunt, Lieberman, Cohen, & Eisenberger, 2012); and third, conflict arouse negative affect (e.g., Botvinick, 2007; Braem et al., 2016; Elliot & Devine, 1994; Inzlicht, Bartholow, & Hirsh, 2015). As to the relationship between conflict and negative affect, for example, an early proposal on the negative nature of cognitive dissonance holds that cognitive dissonance is experienced as a negative state (Elliot & Devine, 1994). Later on, studies on cognitive and affective neuroscience provided additional support for the emotional cost of conflict (Botvinick, 2007), even in conflicts that carry low inconsistency and are not a threat to goals (Proulx, Inzlicht, & Harmon-Jones, 2012). For instance, performing experimental tasks which involve conflict (e.g., Stroop task) produces a state of negativity in participants (Dreisbach & Fischer, 2012; Fritz & Dreisbach, 2013). Performing such tasks is related to emotional primitivesFootnote 3 such as increasing the activity of sympathetic nervous system (Critchley et al., 2003; Hoshikawa & Yamamoto, 1997) including heart rate and blood pressure. Finally, as noted earlier, dACC activity is correlated with conflict detection. Studies on the role of dACC in negative affect (Koban & Pourtois, 2014; Shackman et al., 2011) suggest a functional overlap between conflict and negative affect (Inzlicht et al., 2015).

In addition to the two research projects mentioned above, the findings of Gruberger et al. (2013) on monitoring processes during mind wandering and their effect on negative affect confirm the role of attention and meta-awareness in affective processing.

As to the relationship between meta-awareness and mind wandering, we now know that individuals generally do not know “when” mind wandering occurs. Only after a certain period of time they realize that their minds wandered. This realization is called “meta-awareness,” a re-representation of conscious contents (Braboszcz, Hahusseau, & Miles, 2010; Smallwood & Schooler, 2006). Meta-awareness refers to the ability to consider the content of mental state carefully (Smallwood, McSpadden, & Schooler, 2007). Different studies (e.g., Deng, Li, & Tang 2014; Smallwood, McSpadden, & Schooler, 2007) have compared the differences between mind wandering with meta-awareness and mind wandering without meta-awareness from different perspectives. For example, Smallwood et al. (2007) show that in the context of task performance, the occurrence of mind wandering without meta-awareness during the task is associated with greater behavioral cost such as rapid and careless task performance. Deng et al. (2014) show that mind wandering in the absence of meta-awareness is associated with higher levels of negative mood.

In addition to the studies accomplished on the differences between mind wandering with and without meta-awareness, combining two studies conducted by Christoff et al. (2009) and Deng et al. (2014) shows that, similar to the results of Gruberger et al. (2013), negative affect is a result of heightened attention and more monitoring of the content of mind wandering.

One of the aims of the study of Christoff et al. (2009) was to examine the relationship between meta-awareness and the neural recruitment associated with mind wandering. According to their assessment, mind wandering in the absence of meta-awareness was associated with strong activation of the executive network (dorsal ACC and dorsolateral PFC) and the default network (ventral ACC, posterior cingulate/precuneus and temporoparietal cortex). Also, mind wandering with meta-awareness was associated with similar but weaker activation in those networks. They did not observe any brain regions being significantly more activated during mind wandering with meta-awareness than during mind wandering without meta-awareness. Regarding the role of executive recruitment during mind wandering, Christoff et al. (2009) argue that executive recruitment can reflect the process of detecting conflict within the content of mind wandering itself. In line with this interpretation, some evidence shows that dACC activity, which is stronger during mind wandering without meta-awareness, is correlated with conflict detection (Botvinick et al., 1999; Botvinick et al., 2001; Botvinick et al., 2004; Cohen et al., 2000; Enger & Hirsch, 2005). Similarly, Fox et al. (2015) consider the role of executive control regions during mind wandering in guiding, evaluating and selecting among many spontaneous thoughts, memories and imaginations made conscious by the DN.

As we saw, it is likely that conflict detection and monitoring the content of mind wandering occur more when the person does not have meta-awareness of his own mind wandering. On the other hand, Deng et al. (2014) propose that mind wandering without meta-awareness may be associated with higher levels of negative mood. So we can suggest that monitoring mind wandering contents and attention toward them, as results of activity in the executive control regions or FPCN, are possibly related to higher levels of negative mood. This further corroborates Gruberger et al.’s (2013) assumption that rest-related negative affect may be a phenomenon of its own and is not merely a simple or unavoidable outcome of mind wandering. Here, it should be noted that some researchers make a distinction between meta-awareness of the content and meta-awareness of the process of mind wandering. We can have meta-awareness of the contents of mind wandering without meta-awareness of the process of mind wandering itself (Christoff et al., 2009; Schooler, 2002). Also, it is possible that some cognitive processes, such as monitoring processes, occur in the absence of meta-awareness (Schooler, 2002). Monitoring of desired goals in social situations (Bargh, 1997) and monitoring processes for finding mental contents that indicate failure of achieving a desired state (Wegner, 1994) are two instances of monitoring processes without meta-awareness. It is even possible to perform a task without meta-awareness (Mrazek et al., 2013). By taking such possibilities into consideration, the observation that more self-monitoring and more attention toward the content of mind wandering can occur during mind wandering without meta-awareness may not be implausible or contradictory anymore.

Thus far in this section we have supported two proposals according to which mind wandering per se does not lead to negative affect (e.g., Gruberger et al., 2013) and meta-awareness can be related to the effects of mind wandering (Christoff et al., 2009; Deng et al., 2014). Given that mind wandering and meta-awareness can co-occur during meditation practices (Fox & Christoff, 2014), the question arises whether meditation can prevent negative affect despite the presence of mind wandering (regardless of its content), and if so, how?

Also, given that mind wandering may not be directly related to negative affect but executive control regions or FPCN may play a role in negative mood as well as depression (as we have seen earlier in this section), another question is under what conditions would increased activity and connectivity in the DN not lead to increasing depression? In the next section, we will address these questions.