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. 2026 Mar 4;46(15):e1054252026. doi: 10.1523/JNEUROSCI.1054-25.2026

Neurochemical Correlates of Mind-Wandering and Meta-Awareness

Kara Parsons 1,✉, Hannah L Filmer 1, Shane E Ehrhardt 1, Matilda Gordon 2, Reuben Rideaux 3,4, Paul E Dux 1, Natasha Matthews 1
PMCID: PMC13086245  PMID: 41781321

Abstract

Mind-wandering frequently occurs throughout daily life, diverting attention away from both goal-oriented behavior and internal mental operations. The experience of mind-wandering varies in both the degree to which an individual is aware of their attentional state (meta-awareness) and the extent of their intention to mind-wander. Prior research links mind-wandering with the brain's default mode network. However, the association between mind-wandering and the concentration of excitatory (glutamate) and inhibitory (GABA+) neurotransmitters has largely been unexplored. Here, in 46 (34 females and 12 males) human participants, we employed 7T magnetic resonance spectroscopy to explore associations between neurochemicals in the left prefrontal cortex (PFC), right intraparietal sulcus, and right primary motor cortex (M1) and subtypes of mind-wandering. Across three behavioral paradigms, we employed self-caught mind-wandering probes as a proxy measure for meta-aware mind-wandering and probe-caught to assess mind-wandering that occurs without meta-awareness. Results showed that mind-wandering associations with neurochemical concentrations varied by brain region, cognitive task, and the nature of mind-wandering. Specifically, self-caught mind-wandering during the two-back task and probe-caught mind-wandering during the finger-tapping random sequence generation task (FT-RSGT) were associated with neurochemical concentrations in the PFC. In addition, intentional mind-wandering was associated with GABA+ in M1 for the two-back (probe-caught) and FT-RSGT (self-caught). Unintentional mind-wandering was as associated with E/I balance in the PFC for the FT-RSGT (probe-caught) and two-back (self-caught). These findings provide insights into the neurochemical basis of mind-wandering. The propensity to mind-wander, as well as meta-awareness and intentionality of mind-wandering, appears to depend on the demands of different tasks.

Keywords: cognitive control, individual differences, mind-wandering, neurochemicals

Significance Statement

Mind-wandering—the failure to stay on task—has implications in a wide range of applied contexts. Furthermore, when paired with neuroimaging, studying mind-wandering sheds light on the neurophysiological processes associated with cognitive control. A key neuroimaging research gap concerns the neurochemical basis of mind-wandering. Here, we explored associations between neurochemical concentrations across three cortical regions with mind-wandering, in three distinct tasks. The relationships varied across regions, tasks, and the nature of the mind-wandering. These findings indicate that neurochemical concentrations and particularly the excitatory balance of neurochemicals predict mind-wandering propensity.

Introduction

Maintaining task performance requires sustaining attention on the task at hand. However, attention can shift toward task-unrelated thoughts/actions, a phenomenon referred to as mind-wandering (Schooler et al., 2011). Estimates suggest that up to 50% of daily life is spent mind-wandering, often without the awareness or the intent to engage in this state of task inattention (Killingsworth and Gilbert, 2010; Seli et al., 2018). Such cognitive lapses can give rise to significant impairments in task performance (Kane and McVay, 2012; Mrazek et al., 2012; Randall et al., 2014; Rummel et al., 2022). For example, lapses in attention while driving may contribute to slower reaction times and an increased risk of accidents (Galéra et al., 2012; Yanko and Spalek, 2014; Gil-Jardiné et al., 2017). However, mind-wandering has also been linked to more positive outcomes, such as increased creativity (Baird et al., 2012).

Functional magnetic resonance imaging (fMRI) studies have linked mind-wandering propensity to the default mode and executive networks of the brain (Christoff et al., 2009; Fox et al., 2015). Specifically identifying key default mode network hubs of the medial prefrontal cortex (PFC) and bilateral inferior parietal lobule and nondefault mode network regions including the rostrolateral PFC, secondary somatosensory cortex, and insula (Fox et al., 2015). Currently, the functional neurochemical mechanisms underlying these nonadaptive shifts in attention remain unclear. The imbalance between key excitatory (glutamate, Glu) and inhibitory (γ-aminobutyric acid, GABA+) neurotransmitters in the cortex has been linked to failures to inhibit default mode network activity during tasks, potentially leading to mind-wandering (Gu et al., 2019). Additionally, clinical disorders, characterized by high rates of mind-wandering, have been associated with imbalances in excitatory and inhibitory neurochemicals (Edden et al., 2012; Northoff and Sibille, 2014; Franklin et al., 2017). The association between neurochemical excitability and these involuntary shifts of attention away from goal-oriented behavior, however, remains unknown.

The search for the neural substrates of mind-wandering is complicated by the tendency to treat it as a homologous construct. However, evidence suggests that mind-wandering can be categorized into subtypes, particularly those relating to meta-awareness and intentionality (Seli et al., 2017a). Mind-wandering often occurs without awareness, with individuals lacking metacognitive knowledge of their cognitive state (Schooler, 2002). Meta-awareness is thought to play an integral role in the cyclical process of mind-wandering, as once awareness is reached, an individual may then reorient attention back toward task engagement (Schooler, 2002; Schooler et al., 2011). Indeed, mind-wandering without awareness is associated with stronger neural recruitment of default mode and executive network regions, while meta-awareness was associated with weaker recruitment of these regions (Christoff et al., 2009).

Whereas meta-awareness captures an individual's awareness of their inattentive state, an individual's intent to engage with mind-wandering must also be considered. In this framing, mind-wandering can either be an intentional (deliberate) shift of attention or an unintentional (spontaneous) shift of attention toward mind-wandering (Seli et al., 2015, 2016). Studies have found stronger brain structure integration between the default mode and frontoparietal networks (as measured by cortical thickness and functional organization) in individuals who report higher engagement with deliberate mind-wandering (Golchert et al., 2017). Hence, the neural correlates of mind-wandering may differ across subtypes (Christoff et al., 2009; Golchert et al., 2017).

The present study investigates whether neurochemical concentrations relate to an individual's propensity to mind-wander and whether these associations vary by meta-awareness and intentionality. Additionally, we assessed this across three distinct cognitive tasks typically used to assess mind-wandering, in an effort to increase generalizability of our findings across task contexts. In a preregistered study, we employed ultrahigh-field (7 T) magnetic resonance spectroscopy (MRS) to characterize concentrations of GABA+ and glutamate in the left PFC, right intraparietal sulcus (IPS), and right primary motor cortex (M1). These regions were selected as they have been implicated in attentional control and mind-wandering.

Materials and Methods

Experimental design (overview)

To investigate the relationship between mind-wandering and neurochemical concentrations, we measured participants’ mind-wandering rates while they performed three established mind-wandering tasks. In addition, we employed mind-wandering probes that assessed the intentionality and meta-awareness of their mind-wandering (Fig. 1). We used multiple tasks because mind-wandering may manifest differently depending on specific cognitive demands, allowing us to examine whether neurochemical correlates are task-specific or represent more general mechanisms. Specifically, we employed the finger-tapping random sequence generation task (FT-RSGT), which assesses executive control; the two-back task, which evaluates working memory; and the sustained attention to response task (SART) which measures sustain attention and inhibition. Participants who had completed a prior imaging study (https://osf.io/tc8p3/) were recruited here to complete the mind-wandering measures. As our participants were recruited from an opportunistic sample, our voxels were predetermined. However, both the PFC and intraparietal sulcus have been previously implicated in networks thought to be involved in mind-wandering, with motor regions also implicated in mind-wandering (Dosenbach et al., 2008; Fox et al., 2015; Sormaz et al., 2018). By utilizing voxels that capture areas that are thought to be associated with mind-wandering and those that are not enables the ability to determine whether relationships observed are regionally specific (Rideaux et al., 2022). These participants also contributed to a larger related study where our task procedures and methods were preregistered (https://osf.io/kard5).

Figure 1.

Figure 1.

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In Session 1, participants first performed a mind-wandering training task to familiarize themselves with our probe paradigm. Following this, they performed a counterbalanced block of tasks involving the SART, the FT-RSGT, and the two-back task. Participants were then assigned to perform either an attentional control block involving the selective visual arrays task and the antisaccade task or a working memory block comprised of the operation span and digit span tasks at the end of the session. In the second session, participants once again completed the same procedures as in Session 1 and then completed the remaining attentional control or working memory block prior to completing a questionnaire. Colors represent the two different counterbalancing streams, with green representing the completion of the attentional control block in the first session and the working memory block in the second session and blue representing the completion of the working memory block in the first session followed by attentional control block in the second session.

Participants

A total of 50 participants were recruited for the present study. Four participants did not complete all sessions, three due to poor understanding of tasks and one who withdrew. Thus, the final sample consisted of 46 participants. Thirty-four participants were female (12 males) with an average age of 22.91 years (SD = 4.01; range, 18–34). The study was approved by The University of Queensland Human Research Ethics Committee (2021/HE002408). Participants provided written and informed consent for each session and were reimbursed $20AUD per hour or one course credit per hour for participation.

Apparatus

For the mind-wandering measures, stimuli were presented on a 24 inch ASUS VG248QE monitor (60 Hz, 1,920 × 1,080), and responses were made via a Dell wired keyboard (KB216t) and Dell laser wired mouse (MS3220p). All tasks were run via Psychopy (version 2022.1.3; Peirce et al., 2019) on Windows 21H1. Participants sat in a dimly lit room and were seated 57 cm from the center of the screen.

Mind-wandering measures

Measuring mind-wandering requires the presence of questions that probe an individual's thoughts at varying times throughout a task. Methodologies of experience sampling vary as to whether the presence of the probe is activated by the experiment (probe-caught) or activated by the participant (self-caught). The difference between experimental and participant-activated probes is primarily attributed to that of meta-awareness, whereby self-caught monitoring provides a measure of mind-wandering that has reached a level of meta-awareness to enable an individual to report it, whereas this is not necessarily the case for probe-caught. Given these two measurements assess mind-wandering differentially, an approach that combines both probe methodologies can enable a comparison between mind-wandering that occurred in the absence of awareness (probe-caught) or that reached meta-awareness (self-caught). We will refer to meta-aware mind-wandering as self-caught and mind-wandering in the absence of awareness as probe-caught henceforth. For all three tasks, we maintained the same probe-caught instances that were employed in the original studies from which the tasks were derived (Filmer et al., 2019; Boayue et al., 2021; Rummel et al., 2022). We also implemented self-caught probes into all three tasks based on the methodology employed by Liu et al. (2021).

Mind-wandering instructions and training

During training, participants received instruction on the definitions of mind-wandering, meta-awareness, and intentionality, followed by a short training task to familiarize them with the probe format. The researchers used a standardized script for all participants (OSF, https://osf.io/6ywb4/files/adhcn). Mind-wandering was defined for participants as “mind-wandering means that, just before the thought probes appeared, you were thinking about something unrelated to the task”; on-task performance was defined as “on task means that, just before the thought probes appeared, you were focused on completing the task and were not thinking about anything unrelated to the task.” Intentionality was defined as “mind-wandering can occur either because you intentionally decided to think about things that are unrelated to the task, OR because your thoughts unintentionally drifted away to task-unrelated thoughts, despite your best intentions to focus on the task.” Meta-awareness of mind-wandering was defined as “Sometimes, you will realise you are mind-wandering before the task asks you. When you realise you are mind-wandering please press the ‘m’ key.” The same instructions were visually provided before the training task for the participants to review.

The training task consisted of a modified SART where participants were instructed to press the space bar every time they saw the name of a color. During this task, participants practiced responding to two probe-caught instances, with no limit on self-caught instances.

Before every task, participants were also asked if they required any reminders or additional information about mind-wandering followed by three questions to check their understanding: (1) explain back to me what the questions we will ask you during the task are and what on-task thoughts and mind-wandering thoughts are; (2) what intentional or unintentional thoughts are; and (3) can you remind me what are you meant to do when you realize you are mind-wandering during the task.

Probe-caught

Mind-wandering was measured via experience sampling thought probes presented pseudorandomly during each task. Probe questions were taken directly from Seli et al. (2017b); participants were first asked to make a button press response to characterize their thoughts in the time directly preceding the onset of the probe as being either (1) on-task, (2) intentionally mind-wandering, or (3) unintentionally mind-wandering by pressing the corresponding number on the keyboard. If participants selected option one (on-task), the response was categorized as on-task. If Options 2 (intentionally mind-wandering) or 3 (unintentionally mind-wandering) were selected, the participant thoughts preceding the onset of the thought probe were categorized as probe-caught mind-wandering. To determine the rate of probe-caught mind-wandering for each task, we calculated the total number of times participants indicated they were mind-wandering. As data were obtained across two sessions, the number of probe-caught instances was averaged across sessions to create our probe-caught mind-wandering metric for each individual task. Each task had a specific number of probes embedded: 15 probes within the SART, 8 probes for the two-back, and 20 probes for the FT-RSGT. For the SART, the 15 probes appeared at the end of every thought probe block with probe intervals ranging from 26.4 to 191.4 s due to variances in the pseudorandomized block order and randomized nontarget trial length within each block. During the two-back task, the eight probes appeared at the end of every block, with the interval between probes 84 s due to the preset length of trials within the block. The 20 probes embedded within the FT-RSGT occurred at the end of every block, with the interval between probes varying between 40 and 80 s, dependent on the randomized number of trials within the block.

Probe-caught data cleaning. When a self-caught probe (see below) was immediately followed by a probe-caught probe, the probe-caught data were removed due to the participants being unable to respond appropriately to the probe in these instances. This did not occur in the two-back task, as the design did not allow for the probes to occur immediately after participants initiated a self-caught response. Across the two sessions, for the SART and FT-RSGT, a total of 42 probes were removed (1.32% of the data; 27 out of 1,350 during SART and 15 out of 1,840 probes during FT-RSGT). One participant was excluded from analyses for the SART due to a task error impacting probe data in one session.

Self-caught

These thought probes were instigated by participants via a keypress (the “m” key on the keyboard) and referred to as self-caught mind-wandering (Liu et al., 2021). Once initiated by the participant, the same questions were presented as described above for the probe-caught mind-wandering. Self-caught responses were classified according to meta-awareness and intentionality in the same way as for the probe-caught responses.

To enable comparisons across mind-wandering subtypes, we created a measure of the relative rate of self-caught mind-wandering. If one or more self-caught probes occurred within a task block, it was counted as a single self-caught probe enabling an equivalence to our probe-caught measurements by restricting the number of self-caught probes based on the theoretical maximum number of probes and task design. Hence, the maximum scores for self-caught mind-wandering were reduced to 8 for the two-back and 20 for the FT-RSGT. As the SART has both probe and nontarget blocks, it was reduced to 30. The self-caught relative rate was calculated individually for each session, which was then averaged across sessions to create the self-caught mind-wandering rate used for analysis. Hence, the average rates of mind-wandering differed across tasks. The SART mean self-caught relative rate across sessions had an average of 7.23 probes (SD = 6.00; n = 44), while the average mean total self-caught rate was 8.23 probes (SD = 7.63; n = 44), for the FT-RSGT mean self-caught relative rate across sessions had an average of 6.70 probes (SD = 5.08; n = 45), while the average mean total self-caught rate was 8.64 (SD = 8.65; n = 45), and for the two-back, the mean self-caught relative rate across sessions had an average of 1.66 probes (SD = 1.99; n = 45), while the average mean total self-caught rate had an average of 2.03 probes (SD = 2.86; n = 45).

To further explore the contributions of intentionality, we created additional metrics using the same methodology to identify the presence of an intentional or unintentional self-caught response to calculate the relative rate of intentional self-caught mind-wandering and unintentional self-caught mind-wandering. These were also averaged across sessions and task, with the same maximum total probe instances.

Self-caught data cleaning. Outlier analysis was performed for each task on the average total number of self-caught mind-wandering events across both sessions to identify high rates of mind-wandering that may have related to nonadherence to task instructions. Participants who reported >3 standard deviations from the mean for the overall self-caught mind-wandering rate were removed. One participant was excluded from self-caught across intentionality analysis for the SART, two from intentional self-caught analysis, and two from unintentional self-caught analysis. For the FT-RSGT, one participant was excluded from self-caught across intentionality analysis, one from intentional self-caught analysis, and one from unintentional self-caught analysis. In the two-back task, one participant was excluded as an outlier for self-caught across intentionality analysis, two for intentional self-caught analysis, and one for unintentional self-caught analysis. In instances where a self-caught mind-wandering probe's intentionality was rated as 1 for on-task, these probe data were excluded from analysis due to the inability to have self-caught mind-wandering that is also on-task in nature.

Cognitive tasks

FT-RSGT

Based on the paradigm designed by Boayue et al. (2021), participants were instructed to generate random sequences by pressing one of two keys (“C” and “B”) in time with a metronome tone (440 Hz, 0.075 s) followed by a 0.725 response window between metronome onsets. The task comprised of 20 blocks, with the number of metronome trials within each block drawn from a normal distribution, with the selected value chosen between a minimum of 49 trials (∼40 s) and a maximum of 97 trials (∼80 s). This task produces two key behavioral measures: response variability (how close in time a tap was to the metronome) and approximate entropy (how random the generated pattern was). Probe-caught thought probes were embedded at the end of each block, resulting in 20 probe-caught events in each session. Self-caught probes could be initiated at any time during the task by participants pressing the “m” key on the keyboard. Self-caught probe initiation ended the trial early upon onset after completion of the self-caught probe questions, the remainder of the block resumed. There was no limit to the number of self-caught probes that a participant could initiate.

Two-back. Here, we adapted the two-back task to include self-caught probes based on the paradigm used by Rummel et al. (2022). Specifically, participants were presented with single letters centrally on the screen and asked to indicate as accurately as possible if the current letter presented was either the same (“C” key, target trials) or different (“B” key, nontarget trials) from the letter presented two trials earlier. Trials comprised of stimulus selected from 19 letters (B, C, D, F, G, H, J, K, L, M, N, P, Q, R, S, T, W, Y, Z), presented centrally for 500 ms followed by a 3 s interstimulus interval, resulting in a 3.5 s response window. Each block lasted 1.4 min and always began with three nontarget trials, with the following trials randomly selected from eight targets and 13 nontarget trials. Probe-caught thought probes were presented at the end of each block, with eight blocks in total. Participants could initiate self-caught probes at any time during the task by pressing the “m” key on the keyboard. Upon initiation, the current trial ended, and upon resumption of the task, the first three trials following self-caught probe onset were once again nontarget trials to reorient participants to the task. There was no limit to the frequency of self-caught probe initiation.

SART. The SART requires sustained attention throughout trials to inhibit a response toward a rare target stimulus (Axelrod et al., 2015; Filmer et al., 2019). Digits between 0 and 9 are displayed one at a time on screen. Participants were instructed to respond via a key press (space key) to every nontarget digit (0–9) except for the target number of 3, to which they were to withhold their response. Stimuli were presented centrally for 1 s with a 1.2 s interstimulus interval, resulting in a 2.2 s response window. In each block, the number of nontarget stimuli trials preceding either a target or thought probe was randomized based on a set mean of 20 (SD = 5.69) with a minimum of 12 and a maximum of 29 nontarget trials in each block. The task contained 30 blocks, with half ending in a target (Number 3) and half ending in a thought probe. Every six blocks were separated by a break, with the block types pseudorandomized across the task, with no more than two repeats of either target or thought probe sub-blocks allowed in succession. A total of 15 thought probes were presented during this task for each session, with a potential duration between probes ranging from 26.4 to 191.4 s due to the pseudorandomized block order and randomized nontarget trial length. Self-caught probes could be initiated at any time during the task by participants pressing the “m” key on the keyboard, and upon completion of the thought probe questions, participants completed the remainder of the block.

Procedure

The design consisted of two sessions (Fig. 1), each lasting ∼2.5 h. Both sessions began with instructions and a short training task to familiarize participants with the mind-wandering thought probe questions. The training comprised a standardized script that explained all the concepts related to the probe questions asked, followed by a modified version of the SART where participants were asked to press the spacebar whenever they saw the name of a color. The training task enabled participants to practice responding to probe-caught probes (embedded within the task) and practice activating and responding to participant-activated self-caught probes. Following training, the three mind-wandering tasks were completed. The same protocol was repeated in the second session. The order in which tasks were completed was counterbalanced across both participants and sessions. Participants completed either a working memory task battery or an attentional control task battery at the end of session one and the remaining task battery at the end of session two (order counterbalanced across participants). At the end of session two, demographic information and responses to a series of questionnaires were also collected.

MRS

The MRS data were collected with a MAGNETOM 7 T Plus MRI Scanner (Siemens Healthcare) equipped with a 1-channel transmit and 32-channel receive head coil (Nova Medical). Prior to MRS, a MP2RAGE sequence (TR, 4,200 ms; TE, 2.45 ms) was utilized for the acquisition of T1-weighted images for accurate voxel placement. Given that GABA concentrations in the brain are relatively low, utilizing a MEGA-editing approach enables more precise quantification of GABA+ concentrations (Hong et al., 2019; Rideaux et al., 2021). Similarly, semi-LASER is considered reliable for the acquisition of glutamate quantification without requiring editing. Hence, we utilized a combination of MEGA-semi-LASER for GABA and semi-LASER for glutamate.

During both sequences, participants passively viewed a naturalistic film. The MEGA-semi-LASER sequence, as used by Rideaux et al. (2022), had the following parameters: TE, 74 ms; TR, 8,400 ms; bandwidth, 4,000 Hz; and 32 transients (16ON, 16 OFF) for a total of 4,096 data points. A Gaussian editing pulse was applied at 1.90 (ON) and 7.46 (OFF) ppm for 23.6 ms. Water suppression was achieved using variable power and optimized relaxations (VAPOR; Tkáč and Gruetter, 2005) delays, and two additional transients were acquired to obtain water spectra without water suppression. To suppress fat, an outer volume suppression was applied. The semi-LASER sequence (Scheenen et al., 2008) had the following parameters: TE, 26 ms; TR, 8,400 ms; bandwidth, 6,000 Hz; and 32 transients (16 ON, 16 OFF) for a total of 4,079 data points. A 3D GRE brain was used for automated shimming, followed by a research FAST(EST)MAP shimming sequence for both sequences.

Each voxel was 25 × 25 × 25 mm3 in size for all three regions of interest: the left PFC, right intraparietal sulcus, and right primary motor cortex (Fig. 2C). Three voxel sequences orders of PFC–M1–IPS, M1–IPS–PFC, or IPS–PFC–M1 were counterbalanced across participants. The PFC voxel was centrally placed at one-third of the total anterior/posterior distance, halfway between the left lateral border and the midline and as close to, without intersecting, the superior margin of the lateral ventricles. In the right hemisphere, the intraparietal sulcus voxel was centered on the caudal end of the sulcus, while the primary motor cortex voxel was centered over the precentral knob (Yousry et al., 1997) and as close to the dura without intersection (Fig. 2). The coordinates for each voxel were collected and used to mask an anatomical T1-weighted image to calculate the fractional volume of cerebrospinal fluid, white matter, and gray matter. Tissue segmentation was completed using the statistical parametric mapping (SPM12, www.fil.ion.ucl.ac.uk/spm; Ashburner and Friston, 2005) toolbox for MATLAB (The MathWorks).

Figure 2.

Figure 2.

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A, The average semi-LASER spectra for the PFC, intraparietal sulcus, and primary motor cortex voxels for all participants, with shaded regions indicating ±1 SD. B, The average combined MEGA-semi-LASER spectra for the PFC, intraparietal sulcus, and primary motor cortex voxels for all participants, with shaded regions indicating ±1 SD. C, Average voxel placement for PFC, intraparietal sulcus, and primary motor cortex shown in sagittal, coronal, and axial views placed on the average T1-weighted structural image in MNI space. Placement overlap of participant voxels is indicated by color intensity.

MRS data processing and cleaning

All data were analyzed with the Siemens TWIX format, and in-house MATLAB scripts were used for all data preprocessing and spectral quantification (Rideaux et al., 2019, 2022; Ehrhardt et al., 2024). To improve frequency and phase estimation, MEGA-semi-LASER subspectra underwent 3 Hz exponential line broadening and were zero-filled to 0.061 Hz/point spectral resolution. Unedited semi-LASER data were not zero-filled or line-broadened due to LCModel limits (Provencher, 1993). As per previous work, phase and frequency parameter estimates were obtained by modeling the total creatine (phosphocreatine and creatine) signal, to which the subspectra were aligned (Rideaux et al., 2021). Prior to further analysis, once estimates of signal area and full width at maximum were obtained, subspectra with parameter estimates that exceeded 2.5 standard deviations from the mean within a scan (and corresponding ON/OFF subspectra) were removed.

An edited spectrum for MEGA-semi-LASER data was created by subtracting average OFF spectra from average ON spectra and was used to model GABA+ (3 ppm) signal intensity via a Lorentzian. A Lorentzian model was also used to fit the water intensity signal to the average water-unpressed spectra at 4.7 ppm. LCModel was used for the quantification of glutamate and the water signal intensity for the semi-LASER data.

GABA+ and glutamate concentrations were calculated by determining the area of the fitted peaks and expressed in institutional units (i.u.), with unsuppressed water signals used as an internal reference. A tissue-correction method was applied to account for differences in relaxation time across tissue types within each voxel (Rideaux et al., 2021). This method utilized established 7 T longitudinal relaxation times: 1.78 s for water, 1.33 s for GABA+, and 1.68 s for glutamate (Andreychenko et al., 2012; Xin et al., 2013). Transverse relaxation times were assumed for water (0.05 s), GABA+ (0.09 s), and glutamate (0.09 s; Gasparovic et al., 2006; Marjańska et al., 2012). Additionally, the relative densities for MR-visible water were assumed for gray matter (0.78), white matter (0.65), and cerebrospinal fluid (0.97; Peters et al., 2017).

The data cleaning procedure for the MRS consisted of visual inspections of both individual MEGA-semi-LASER data for GABA+ and semi-LASER data for glutamate for each region individually to identify overly noisy spectra and poor water suppression. Eight participants were brought back for a second scan after quality issues were identified in their original scan. From visual inspection, four scans in the PFC were identified for exclusion, one in the IPS; none were removed from the M1. Data quality metrics, as summarized in Table 1, were assessed to identify further exclusions. Two from the PFC were excluded due to water linewidth of the MEGA-semi-LASER exceeding 19 Hz (Öz et al., 2021). One was excluded from the PFC, two from the IPS and one from the M1 as they exceeded ±2.5 standard deviations for frequency drift values of the semi-LASER. One from the IPS and one from the M1 were excluded due to metabolite values exceeding ±2.5 standard deviations. Missing data resulted in two exclusions for the PFC, three exclusions for the IPS, and one exclusion for the M1. In total, nine participants were excluded for the left PFC, six participants were excluded for the right IPS, and three participants were excluded for the right M1.

Table 1.

Summary of data quality metrics

PFC Intraparietal sulcus Primary motor cortex
n 37 40 43
MEGA-semi-LASER water linewidth (Hz) Range 12.50–18.36 11.08–14.47 11.59–14.10
Mean (SD) 15.24 (1.30) 13.11 (0.69) 12.53 (0.56)
Glu CRLB Range 2–6 2–4 3–11
Mean (SD) 3.27 (0.99) 2.92 (0.61) 4.70 (1.86)
GABA fit error Range 0.05–0.15 0.05–0.11 0.05–0.19
Mean (SD) 0.09 (0.02) 0.08 (0.01) 0.08 (0.02)
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Excitatory/inhibitory balance

To ascertain the contributions of the overall balance between key excitatory and inhibitory neurochemicals (E/I), we created a ratio metric by dividing glutamate concentrations by GABA+ concentrations as follows:

GluGABA+=E/IRatio.

Statistical analysis

We employed Bayesian linear regressions to assess the relationship between MRS measures (Glu, GABA+, and E/I ratio) and mind-wandering rates for each task. The Bayesian methodology enables the determination of the strength of evidence toward the null and alternative hypotheses, with values of 1–2 considered indeterminate (Rosenfeld and Olson, 2021), 2–3 anecdotal, 3–10 moderate, and over 10 strong evidence for H1. There were no corrections for multiple comparisons applied to our analysis as by employing a Bayesian analysis framework, the chance for Type 1 error is reduced as this approach makes conservative comparisons compared with classical NHST procedures (via a process colloquially known as “partial pooling”; Gelman et al., 2012). All Bayesian regressions were run using the default settings in JASP [version 0.17.3; prior, Jeffreys–Zellner–Siow r-scale = 0.354; model prior, beta binomial (a = 1; b = 1); sampling method, BAS] with the variables of sex, weeks between MRS scan and first mind-wandering session, and the task-specific counterbalancing order for each participant included in the null models. Individual regressions were run for the overall probe- and self-caught mind-wandering rates and for intentional and unintentional mind-wandering rates with concentrations of Glu, GABA+, and the E/I (Glu/GABA+) ratios in each respective region of interest (left PFC, right IPS, and right M1) included as predictors. Models selected were based on the highest Bayesian factor (BF10).

Weeks since MRS scan

To account for any variance due to the time between a participant's MRS scan and their participation in the first session of this study, we created a metric that calculated the number of weeks since the MRS scan was conducted (rounded up to nearest whole number). Prior research has indicated that quantification of these neurochemical concentrations remains stable across time for up to 7 months (Near et al., 2014; Greenhouse et al., 2016; Ferland et al., 2019). Weeks since the MRS scan ranged from 47 prior to 36 weeks post the first mind-wandering session, with a median of 6 weeks. The 73.26% of participants completed the first mind-wandering session within <3 months of the MRS scan.

Task order counterbalancing

As tasks were repeated across the two sessions, to account for the order in which they were performed, a metric was created, to be employed as a null, to determine their order across the two sessions. We created this metric by averaging the placement order of the three tasks and taking the average between these two sessions. This was conducted for all three tasks with our counterbalancing metric ranging from 1 (the task was done first for each session) to 3 (the task was done last for each session).

Results

Is meta-awareness of mind-wandering related to neurochemical concentrations?

As the neurochemical basis of mind-wandering has not yet been definitively characterized, we explored associations between the meta-awareness of mind-wandering and neurochemical concentrations across tasks. Collectively, the results demonstrated that the meta-awareness of mind-wandering had unique associations with neurochemical concentrations across our three tasks.

FT-RSGT

Probe-caught mind-wandering during the FT-RSGT was associated with neurochemical concentrations primarily in the PFC (Fig. 3). Specifically, there was strong evidence that lower levels of glutamate in combination with a higher ratio of E/I balance (indicating more excitation) in the PFC was associated with a greater incidence of probe-caught mind-wandering (BF10 = 12.132; R2 = 0.303). This pattern of results may be related to variance from the creatine reference used for glutamate concentrations while our E/I ratio metric does not have these variances present. There was only anecdotal evidence that higher levels of probe-caught mind-wandering was associated with decreased glutamate levels in M1 (BF10 = 2.398; R2 = 0.103), and there was no evidence supporting a relationship between neurochemical concentrations in the IPS and probe-caught mind-wandering (BF10 ≥ 0.338 and ≤0.736 for all). There was no evidence for a relationship between neurochemical concentrations in any of the regions of interest and the rate of self-caught mind-wandering during the FT-RSGT (BF10 ≤ 1.470 for all; Table 2).

Figure 3.

Figure 3.

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Partial residual plots of glutamate concentrations (A) and E/I ratio (B) within the PFC related to mean probe-caught mind-wandering during the FT-RSGT. Upper and lower confidence intervals (95%) from the prediction line are indicated in blue.

Table 2.

Summary of Bayesian regressions across probe and task

Probe-caught Self-caught
Region n Winning model BF10 Region n Winning model BF10
FT-RSGT PFC 37 Glu & E/I ratio 12.132 FR-RSGT PFC 36 N/A -
IPS 40 N/A - IPS 39 N/A -
M1 43 Glu 2.398 M1 42 N/A -
Two-back PFC 37 N/A - Two-back PFC 36 E/I ratio 3.462
IPS 40 N/A - IPS 39 N/A -
M1 43 N/A - M1 42 N/A -
SART PFC 36 N/A - SART PFC 35 N/A -
IPS 39 N/A - IPS 38 N/A -
M1 42 N/A - M1 41 N/A -
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Two-back

For the two-back paradigm, there was little evidence in support of a relationship between neurochemical concentrations in any of the three regions with any probe-caught mind-wandering measures (BF10 ≤ 1.734 for all; Table 2). Self-caught mind-wandering during the two-back task was related to the E/I ratio in the PFC (Fig. 4). Specifically, lower E/I ratios (lower excitation) were associated with greater self-caught mind-wandering (BF10 = 3.462; R2 = 0.218). There was no evidence relating self-caught mind-wandering to neurochemical concentrations in the IPS or M1 (BF10 ≤ 0.792 for all).

Figure 4.

Figure 4.

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Partial plots (residuals) of E/I ratio within the PFC, related to rates of self-caught mind-wandering during the two-back. Upper and lower confidence intervals (95%) from the prediction line are indicated in blue.

SART

There was little to no evidence in support of a relationship between neurochemical concentrations in any of the three regions with any probe- or self-caught mind-wandering measures in the SART (Table 2).

Is the intentionality of mind-wandering related to neurochemical concentrations?

In addition to categorizing mind-wandering by meta-awareness, each probe enabled further categorization of mind-wandering instances dependent on the intentionality. Hence, we examined the associations between the intentionality of mind-wandering and neurochemical concentrations across a variety of tasks. Intentionality of mind-wandering had unique associations with neurochemical concentrations across our three tasks.

FT-RSGT

Intentional. Overall, there was no evidence to support a relationship between neurochemical concentrations in any of the three regions with intentional probe-caught mind-wandering during the FT-RSGT (BF10 ≤ 1.259 for all).

Self-caught intentional mind-wandering was related to the concentration of GABA+ in M1 (Fig. 5). Specifically, there was moderate evidence that higher concentrations of GABA+ in M1 were associated with greater incidence of intentional mind-wandering (BF10 = 3.584; R2 = 0.250). There was no evidence for a relationship between intentional self-caught mind-wandering and neurochemical concentrations in the PFC or IPS (BF10 ≤ 0.906 for all).

Figure 5.

Figure 5.

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Partial residual plots of GABA+ concentrations in M1, related to rates of intentional self-caught mind-wandering during the FT-RSGT. Upper and lower confidence intervals (95%) from the prediction line are indicated in blue.

Unintentional. Unintentional probe-caught mind-wandering during the FT-RSGT was related to neurochemical concentrations in the PFC (Fig. 6). There was moderate evidence that lower concentrations of glutamate, higher concentrations of GABA, and higher E/I ratios (more excitation) in the PFC were associated with greater incidence of unintentional probe-caught mind-wandering (BF10 = 3.131; R2 = 0.241). Neurochemical concentrations in the IPS and M1 did not relate to unintentional probe-caught mind-wandering (BF10 ≤ 1.143 for all). There was no relationship between neurochemical concentrations in any of the regions of interest and unintentional self-caught mind-wandering during the FT-RGST (BF10 ≤ 1.369 for all).

Figure 6.

Figure 6.

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Partial residual plots of GABA+ concentrations (A), glutamate concentrations (B), and E/I ratio (C) within the PFC, related to rates of unintentional probe-caught mind-wandering during the FT-RSGT. Upper and lower confidence intervals (95%) from the prediction line are indicated in blue.

Two-back

Intentional. Intentional probe-caught mind-wandering was related to neurochemical concentrations in M1 (Fig. 7). Specifically, there was moderate evidence that higher GABA+ concentrations in M1 were associated with greater intentional probe-caught mind-wandering (BF10 = 4.545; R2 = 0.170). For the IPS, there was anecdotal evidence that higher glutamate concentrations were associated with greater intentional probe-caught mind-wandering (BF10 = 2.337; R2 = 0.122). No evidence was found for a relationship between intentional probe-caught mind-wandering and neurochemical concentrations in the PFC (BF10 ≤ 0.573 for all). There was no meaningful evidence for a relationship between neurochemical concentrations and intentional self-caught mind-wandering in any of the regions (BF10 ≤ 1.567 for all).

Figure 7.

Figure 7.

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Partial plots of GABA+ concentrations within the M1, related to rates of intentional probe-caught mind-wandering during the two-back. Upper and lower confidence intervals (95%) from the prediction line are indicated in blue.

Unintentional. E/I ratios in the PFC were associated with unintentional self-caught mind-wandering during the two-back task (Fig. 8). Specifically, there was moderate evidence that lower E/I ratios (less excitation) in the PFC were associated with greater levels of unintentional self-caught mind-wandering (BF10 = 4.264; R2 = 0.207). There was no meaningful evidence relating neurochemical concentrations to unintentional self-caught mind-wandering in the IPS or M1 (BF10 ≥ 0.219 and ≤0.743 for all). Unintentional probe-caught mind-wandering during the two-back was not meaningfully associated with neurochemical concentrations across the regions (BF10 ≤ 1.209 for all).

Figure 8.

Figure 8.

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Partial plots of the E/I ratio within the PFC, related to rates of unintentional self-caught mind-wandering during the two-back. Upper and lower confidence intervals (95%) from the prediction line are indicated in blue.

SART

There was little to no evidence in support of a meaningful relationship between neurochemical concentrations in any of the three regions with intentional or unintentional mind-wandering measures in the SART task (Table 3).

Table 3.

Summary of Bayesian regressions across probe, intentionality, and task type

Intentional Unintentional
Task Region n Winning model BF10 Task Region n Winning model BF10
Probe-caught
 FT-RSGT PFC 37 N/A - FT-RSGT PFC 37 GABA+, Glu, and E/I ratio 3.131
IPS 40 N/A - IPS 40 N/A
M1 43 N/A - M1 43 N/A
 Two-back PFC 37 N/A - Two-back PFC 37 N/A -
IPS 40 Glu 2.337 IPS 40 N/A -
M1 43 GABA+ 4.545 M1 43 N/A -
 SART PFC 36 N/A - SART PFC 36 N/A -
IPS 39 N/A - IPS 39 N/A -
M1 42 N/A - M1 42 N/A -
Self-caught
 FT-RSGT PFC 36 N/A - FT-RSGT PFC 36 N/A -
IPS 39 N/A - IPS N/A -
M1 43 GABA+ 3.584 M1 42 N/A -
 Two-back PFC 35 N/A - 2-Back PFC 36 E/I ratio 4.246
IPS 38 N/A - IPS 39 N/A -
M1 43 N/A - M1 42 N/A -
 SART PFC 34 N/A - SART PFC 34 E/I ratio 2.063
IPS 37 N/A - IPS 37 N/A -
M1 41 N/A - M1 41 N/A -
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Rates of mind-wandering, reliability, and validity

Overall, it was observed that mind-wandering rates differed across tasks, with participants more likely to engage with mind-wandering during the FT-RSGT and SART in comparison with the two-back task. Specifically, using paired samples t test, it was found that probe-caught mind-wandering rates were higher during the FT-RSGT (m = 7.62; SD = 4.79) than the SART (m = 4.80; SD = 3.30; BF10 = 851.536) and two-back (m = 1.76; SD = 1.843; BF10 = 5.069 × 106) and higher in the SART than the two-back (BF10 = 1.826 × 106). There were no differences between self-caught mind-wandering rates during the FT-RSGT (m = 6.70; SD = 5.07) and SART (7.23, SD = 6.00; BF10 = 0.209) and higher mind-wandering in the SART than the two-back (m = 1.66; SD = 0.30; BF10 = 3.341 × 106) and higher mind-wandering during the FT-RSGT than the two-back (BF10 = 1.149 × 107). We also conducted correlations across task and found that mind-wandering rates were correlated across tasks to varying extents (Tables 4, 5). It should be noted, however, that due to self-caught probes requiring participant activation, all feature zero-inflation as part of their distribution. Importantly, the difference in findings for each task in terms of the relationship between the mind-wandering generated and MRS measures demonstrates there is unique variance present in the mind-wandering rates, and it is this that relates to neurochemistry.

Table 4.

Pearson’s r correlations between probe-caught mind-wandering across tasks

FT-RSGT mean probe-caught Two-back mean probe-caught SART mean probe-caught
FT-RSGT mean probe-caught BF10 -
r
Two-back mean probe-caught BF10 0.950 -
r 0.274
SART mean probe-caught BF10 1,419.757 165.989
r 0.594 0.530 -
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Table 5.

Pearson’s r correlations between self-caught mind-wandering across tasks

FT-RSGT mean self-caught Two-back mean self-caught SART mean self-caught
FT-RSGT mean self-caught BF10 -
r
Two-back mean self-caught BF10 132.149
r 0.523 -
SART mean self-caught BF10 3.243 × 108 4,338.030
r 0.808 0.627 -
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Importantly, performance measures for each of the tasks (as shown in Table 6) were observed to be similar to those found in prior literature [SART (Liu et al., 2021) nontarget accuracy (NACC) and target accuracy (TACC); FT-RSGT behavioral variability (BV) of RT and approximate entropy (AE; Boayue et al., 2021); and two-back performance rate (PR), defined as the false alarm rate subtracted from the correct hit rate (Rummel et al., 2022)].

Table 6.

Mean scores of relevant behavioral task measures for each task comprised of SART NACC, SART TACC, FT-RSGT BV, FT-RSGT AE, and two-back PR

Task Measure N M SD Range
SART NACC 45 0.98 0.04 0.72–1.00
TACC 45 0.59 0.19 0.20–1.00
FT-RSGT BH 45 −0.02 - -
AE 45 0.01 - -
Two-back PR 46 0.80 0.18 0.25–0.99
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Additionally, we sought to investigate the test–retest reliability of the mind-wandering data across sessions. Bayes factor correlations were performed between Session1 and Session 2 mind-wandering rates for self-caught and probe-caught mind-wandering for each task separately. All probes demonstrated good test–retest reliability (Table 7). To assess validity of our probe approach, we sought to determine if measurements taken during the SART replicated common findings from previous research using this task on the relationship between mind-wandering and reaction time for both probe-caught and self-caught measures (Bastian and Sackur, 2013). We conducted a Bayesian t test to identify differences in reaction time variability between the probe-caught and self-caught mind-wandering for the five trials prior to probe onset. Data were averaged across sessions, with data trimming to extract useable sequences of five trials performed in line with prior work by Bastian and Sackur (2013). There was strong evidence for differences in reaction time variability proceeding probe-caught mind-wandering compared with on-task behavior (BF10 = 34.592), with higher reaction time variability prior to probe-caught mind-wandering reports (m = 23.94; SD = 8.83) compared with on-task reports (m = 19.80; SD = 6.40). Similarly, higher reaction time variability was observed for the five trials preceding self-caught mind-wandering (m = 23.58; SD = 10.203) compared with the five trials proceeding on-task probe responses (BF10 = 261.831). When comparing reaction time variability between probe-caught and self-caught mind-wandering, there was moderate evidence that there was no difference in reaction time variability between these mind-wandering types (BF01 = 5.995). Hence, our pattern of results for the SART demonstrated valid measurements between implicit behavioral measures of mind-wandering and explicit experience sampling measures in line with previous work.

Table 7.

Summary of test–retest correlations for probe-caught and self-caught relative rate mind-wandering

Task Probe type N r BF10
Two-back Self-caught 45 0.647 12,940.00
Probe-caught 46 0.434 15.00
SART Self-caught 44 0.695 105,637.00
Probe-caught 45 0.468 30.40
FT-RSGT Self-caught 45 0.556 370.10
Probe-caught 46 0.528 181.00
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Discussion

The neurophysiological basis of mind-wandering is yet to be fully characterized. While previous studies have linked mind-wandering with default mode network activity (Fox et al., 2015) and suggested that the balance between glutamate and GABA may drive default mode function (Gu et al., 2019), direct evidence connecting neurochemical concentrations to mind-wandering has been lacking. Here, we provide, for the first time, evidence that GABA, glutamate, and the balance of the two (E/I) is associated with an individual's mind-wandering propensity. Importantly, these relationships were nuanced, varying with meta-awareness, intentionality, and the cognitive demands of different tasks. Specifically, during the FT-RSGT, which primarily engages executive control processes, probe-caught mind-wandering (occurring without meta-awareness) was associated with concentrations of glutamate and the overall E/I ratio within the left PFC. In contrast, intentional self-caught mind-wandering (with meta-awareness) during the same task was associated with GABA+ concentrations in the right primary motor cortex. For the two-back task, which primarily engages working memory processes, we observed a different pattern; intentional probe-caught mind-wandering was associated with GABA concentrations in the right primary motor cortex, whereas self-caught and unintentional self-caught mind-wandering was associated with E/I in the left PFC. Surprisingly, for the SART, which is the most commonly used task in the mind-wandering literature, we found no meaningful associations between neurochemical concentrations and mind-wandering propensity in the brain regions examined. Collectively, these results demonstrate that the neurochemical underpinnings of mind-wandering cannot be characterized by a “one size fits all” approach. Rather, the neurochemical correlates of mind-wandering vary across brain regions, task demands, and the aspect of mind-wandering being studied. This complexity likely reflects the multifaceted nature of mind-wandering as a cognitive phenomenon.

Our study features several methodological strengths. We used a large sample size, preregistered the study, and provided a comprehensive measurement of mind-wandering through the use of both self- and probe-caught methods and capturing both intentional and unintentional mind-wandering. To address concerns about task heterogeneity in mind-wandering research (Nawani et al., 2023), we employed three distinct tasks capturing three cognitive domains; attentional control (FT-RSGT), working memory (two-back task), and sustained attention (SART). The task-specific pattern of results we observed suggests that the neurochemical correlates of mind-wandering depend on the cognitive demands of the task being performed. For mind-wandering assessment, we adopted the intentionality questions developed by Seli et al. (2017b) and combined them with probe-caught/self-caught methodology from Liu et al. (2021) . This approach provided consistency across tasks within our study, allowing for direct comparison of mind-wandering across different cognitive contexts. While Weinstein et al. (2018) have noted that question framing and probe implantation can influence participant responses, the distinct pattern of mind-wandering we observe across tasks suggests that our results reflect genuine task-dependent differences rather than methodological artifacts. Additionally, electrophysiological evidence has shown that the mind-wandering, detected through probe-caught and self-caught methods, can be dissociated in the brain (Liu et al., 2021). Hence, a combined probe-caught/self-caught paradigm was employed here throughout the three tasks to assess these underlying neural differences. This dual-method approach enabled our assessment of meta-awareness, which is considered a key part of the cyclical process of mind-wandering (Smallwood and Schooler, 2015; Schooler et al., 2011). Our results, again, support the idea that distinct neurophysiological mechanisms may support mind-wandering across differing levels of meta-awareness. Specifically, we have evidence of distinct associations between probe-caught and self-caught mind-wandering with neurochemical concentrations that vary dependent on the task demands. Early work by Smith et al. (2006) found that during the SART, mind-wandering with awareness was associated with activation in the lateral PFC, while absence of awareness was associated with the activation in the lateral temporal cortex. Our results partially align with these findings, showing PFC involvement in self-caught mind-wandering during the two-back task, but contrastingly, we found PFC associations with probe-caught mind-wandering during the FT-RSGT. A systematic review of electrophysiological markers of mind-wandering by Kam et al. (2022) also suggests that while ERP markers were reliable across tasks, spectral markers of mind-wandering were likely task-dependent. Additionally, while the M1 has not been previously implicated in mind-wandering, adjacent areas in sensorimotor regions may distinguish between on and off task thoughts (Sormaz et al., 2018; Kucyi et al., 2021). Interestingly, we observed associations between intentional (but not unintentional) mind-wandering in both the two-back (probe-caught) and FT-RSGT (self-caught) and increased GABA+ in the right M1. This pattern may implicate the region in an individual's choice to deliberately disengage attention during a task. Indeed, M1 activation in specific subareas has been shown to vary depending on whether a task requires attention for action execution (Binkofski et al., 2002), and the region has been associated with higher cognitive processes beyond motor action alone (Bhattacharjee et al., 2021). This suggests that the right M1 may play a role in an individual's deliberate decision to disengage attention during task performance. However, further research is needed to disentangle these potential relationships.

While neurochemical concentrations have not been previously examined in relation to mind-wandering, the dynamic interplay between the default mode network and the executive network has been attributed to shifts in attention from external task-oriented focus to an internalized mind-wandering focus (Christoff et al., 2009; Fox et al., 2015). Research by Hu et al. (2013) identified the relationships between task-induced default mode network deactivation, GABA concentrations, and cognitive load, highlighting the importance of considering task-specific demands on these potential underlying relationships. Although previous research has suggested that GABA concentrations in the posterior cingulate cortex may be particularly important for these attentional shifts, we did not measure this region in the current study, which warrants further exploration to better understand the mechanisms that determine mind-wandering behavior within individuals. Understanding the interplay between the balance between excitatory and inhibitory neurochemicals will enable the identification of when an individual is likely to engage in mind-wandering in specific contexts.

In conclusion, our findings provide an initial investigation into understanding the neurochemical underpinnings of mind-wandering. Broadly, we provide preliminary evidence on how key excitatory and inhibitory neurochemicals predict meta-awareness and intentionality of mind-wandering behavior. Due to the distinct patterns of associations across tasks, our findings indicate that mind-wandering propensity is potentially dependent on task-specific demands. These findings have important implications for the consideration of task-dependent differences in mind-wandering behavior. Further investigation is warranted to fully understand the neurochemical mechanisms that underly change in attentional states in distinct.

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