The Connection Between Mind Wandering,ADHD,and Level of Performance on an Attention Task

Abstract

Background: ADHD is related to increased mind wandering (MW). However, it is not clear whether this is restricted by the methods used to measure MW in those studies. Moreover, it is not certain whether MW is an independent characteristic of people with ADHD or only due to their poor performance level in the primary task (the task from which attention wanders). Method: Participants with ADHD were compared to two control groups: high performers and low performers in the primary task who had a similar performance level to the ADHD group. Results: The ADHD group had greater MW than the two control groups, while no difference in MW was found between the two control groups. Conclusion: Increased MW is an independent characteristic of ADHD. This was further discussed within the context of different theories regarding ADHD and MW.

Keywords

mind wandering task-related interferences ADHD sustain attention

Introduction

ADHD is a neurodevelopmental disorder. Approximately 5% to 10% of children are diagnosed with ADHD, and the majority of these will continue to show signs of ADHD in adulthood as well. ADHD relates to deficiencies in the control and attention system and can take one of three forms: inattentive behaviors alone, hyperactive and impulsiveness behavior alone, or a combination of these deficiencies. ADHD symptoms may vary in severity and intensity between different persons, as well as within a person in different life periods. These deficits lead to difficulties in various aspects of life, such as academic and social relationships (e.g., Barkley & Fischer, 2011; Chandler et al., 2002; Johnston, 1998; Johnston et al., 2012; Polanczyk & Rohde, 2007). Recently, the European statement on adults with ADHD (Kooij et al., 2019) suggested that mind wandering (MW) is a common characteristic of adults with ADHD and pointed out that MW is a strong predictor of ADHD, even more than symptoms of inattention or hyperactivity.

MW and Interfering Thoughts

MW refers to a cognitive process in which human cognition shifts away from the task demands to the processing of unrelated thoughts such as personal goals, and it occurs either intentionally or unintentionally (Smallwood & Schooler, 2006). Studies have shown that about half of one’s daily thoughts can be classified as MW and that this phenomenon occurs in all types of activities and tasks, such as reading, working memory, sustained attention tasks, and so on (Killingsworth & Gilbert, 2010; McVay & Kane, 2009; Mrazek et al., 2013; Schooler, 2004).

Behavioral studies have shown that MW has a negative impact on performance in both the laboratory and daily functioning. In the laboratory, MW was found to be associated with a longer reaction time (RT), high RT variability, and errors in different tasks such as sustained attention, selective attention, working memory, etc. (e.g., Cheyne et al., 2009; Kam et al., 2013; Kam & Handy, 2014; McVay & Kane, 2012; Stawarczyk et al., 2014). Regarding daily functioning, it was found to impair reading comprehension (e.g., Feng et al., 2013; Unsworth & McMillan, 2013), have a negative effect on learning and memory (Seli et al., 2016; Szpunar et al., 2013), and increase driving speed, as well as increasing response time while driving and to be possibly associated with car accidents (Galéra et al., 2012; Yanko & Spalek, 2014).

On the other hand, several studies in the field have found that MW also has benefits as well. For example, it is suggested that it can help the individual plan for the future (e.g., Baird et al., 2011), deal with unexpected events (Song & Wang, 2012), find novel solutions to previous problems (e.g., Baird et al., 2012) and that it is associated with creativity (e.g., Ellamil et al., 2012; Godwin et al., 2017). It was also found that thoughts about interesting topics outside the task were associated with a positive mood) Franklin et al., 2013).

The neural system assumed to be connected to MW is the default mode network (DMN). This network includes the anterior medial pre-frontal cortex and the posterior cingulate cortex/precuneus (e.g., Christoff et al., 2009; Mason et al., 2007; Smallwood et al., 2013). It is assumed that when the DMN is activated this happens at the expense of another network: the task positive network (TPN), which includes areas from the dorsal attention system, including the dorsolateral and ventrolateral prefrontal regions, the insular cortex, and the supplementary motor area, SMA (Fox et al., 2015). This network is activated in situations that require attention and control. In other words, TPN activity increases in attention tasks, while DMN activity decreases. Similarly, DMN activity increases in a state of wakeful rest, while TPN activity decreases (Hamilton et al., 2011).

Although the connection between MW and control performance has been intensively studied in recent years, the literature suggests two different relationships between these processes. On one hand, the control-failure hypothesis suggests that MW reflects a failure of the executive system to suppress these thoughts (e.g., McVay & Kane, 2010). According to this theory internal thoughts, which are created automatically and associated with personal goals, reappear in response to internal or external cues. MW is a result of the inability of the control system to suppress the disruptive thoughts while performing a task. On the other hand, the executive-resource hypothesis (e.g., Smallwood & Schooler, 2006) suggests that MW and task related thought share the same mechanism and compete for the same limited resources. Hence, when the level of mental resources needed for a given task decreases, the system has excess resources for MW. This, for example, fits the findings whereby the nature of the main task affects the extent of MW. For instance, it was found that MW occurs more in low-demanding tasks (e.g., Baird et al., 2012; Levinson et al., 2012; McKiernan et al., 2006; Smallwood et al., 2004).

MW is one type of situation in which the brain tends to think different thoughts while performing a task. MW involves task-unrelated thought (TUT) and extensive research in the field has been conducted on this kind of thoughts (e.g., Smallwood et al., 2003a; or SITUT, e.g., Stawarczyk et al., 2011a). Others suggest other types of interfering thoughts, such as TRI—task-related interferences, however this type of thoughts has received less research attention. TRI refers to interfering thoughts related to the task but does not refer to the stimuli of the task, such as thoughts about one’s performance. This could be, for example, thoughts about the need to work more carefully or thoughts about how well I have performed (e.g., Kucyi et al., 2013; Smallwood et al., 2002, 2003a, 2003b, 2004, 2006; Stawarczyk et al., 2011a, 2011b).

Stawarczyk et al. (2011a) argued that although both TRI and MW are interfering thoughts, they can be distinguished according to two dimensions: “task-relatedness” and “stimulus-dependency,” meaning that TRI is task-related and stimulus-independent and MW is task-unrelated and stimulus-independent. On the behavioral level, they found differences in the effect of different types of thought on performance, so MW impairs additional components of SART performance (Stawarczyk et al., 2011a), and on the neurological level MW was found to be associated with a different activation pattern in the DMN than those associated with TRI (Stawarczyk et al., 2011b).

MW, TRI, and ADHD: Review and Limitations of Previous Studies

The connection between MW and ADHD has been of main interest in recent years and it was mostly studied using the general questionnaire technique. Using this technique, it was found that participants with ADHD symptoms have more reports of MW (Jonkman et al., 2017; Mowlem et al., 2019; Seli et al., 2015; Weyandt et al., 2003).

In the general propensity questionnaires technique, participants are asked about their tendency to mind wander in different everyday tasks, such as while reading or engaging in everyday tasks (e.g., Choi et al., 2015; Godwin et al., 2017; Stawarczyk et al., 2012, 2014).

Despite these interesting findings regarding MW and ADHD, these studies appear to have a limitation as they do not measure the task from which the participants’ mind actually wandered. As noted above, the nature of the main task affects the extent of MW and the level of MW is influenced by the attention allocation in the main task (e.g., Baird et al., 2012; Levinson et al., 2012; McKiernan et al., 2006; Smallwood et al., 2004). Hence, it is difficult to know whether increased MW is observed in a population with ADHD because of their different choice of primary tasks or their different performance on those tasks. For example, people with ADHD might prefer to read easier texts in their free time, hence they will report more MW when asked about the item “While reading I find I haven’t been thinking about the text and must therefore read it again.”

Only few studies examined the connection between interfering thoughts and ADHD in a controlled laboratory task. In the literature on interfering thoughts, momentary situation questionnaires are a technique in which participants are asked to estimate their MW or TRI level when performing a controlled experimental task. The controlled task is often a sustained attention to response task (SART), which requires participants to maintain their attention on a specific stimulus for a relatively long period of time (e.g., Cornblatt et al., 1988; Manly et al., 1999; Robertson et al., 1997). The nature of the task, which requires a repetitive response, often leads to MW (e.g., McVay & Kane, 2009; Smallwood et al., 2004; Smallwood & Schooler, 2006). One of the momentary situation questionnaires that appears in the literature on interfering thoughts is the online repeated questionnaire, in which participants are interrupted while performing a task, with questions about the nature of their thoughts (e.g., Jackson & Balota, 2012; McVay & Kane, 2009; Smallwood et al., 2004).

Shaw and Giambra (1993) used this technique to study the connection between MW and ADHD and found that college students diagnosed as hyperactive in childhood reported more online MW when performing a sustained attention task. In addition, they found that in a group of students with no prior diagnosis of ADHD, students who had many symptoms of ADHD displayed more MW than students with only few symptoms.

Similarly, Franklin et al. (2017) measured MW using the online technique in reading and sustained attention tasks in a population of typically developed students with no ADHD diagnosis. The results showed that participants with more ADHD symptoms were more likely to display MW and were less aware of this and that ADHD symptom scores were positively correlated with errors on the sustained attention task. In another recent study, Jonkman et al. (2017) examined MW in a sustained attention task and reading in a population of typically developed students with no ADHD diagnosis. This group was divided into groups with high or low ADHD-inattention symptoms (and average hyperactive-impulsive symptoms) and was exposed to positive and negative mood manipulation. While participants performed the tasks, an online measurement of MW appeared. No difference was found between groups in the online questions while performing the sustained attention task. Regarding the reading task, the online questionnaire revealed that the high symptom group had more MW compared to the low symptom group, regardless of mood only in the reading task.

To the best of our knowledge, the only study conducted on interfering thoughts in a population diagnosed with ADHD using the online technique was conducted with a population of children. Van den Driessche et al.’s (2017) study examined the momentary situation thought in two experiments. The first experiment involved four groups of children between the age of 6 to 12. This included children who had been diagnosed with ADHD and who had received or had not received medication treatment in their past, as well as a control group of children with other psychiatric disorders and a typical development control group. The participants performed a sustained attention task and during the task they were asked about their thoughts. The results revealed that the medication treatment group displayed more MW compared to all other groups, while no difference was found in the TRI.

The second experiment included adults who had not been diagnosed with ADHD but demonstrated a range of ADHD symptoms according the DIVA self-report. The participants performed the same experiment as described previously and no difference was found between the groups in the MW and TRI reported.

To sum up, studies that examined MW in experimental tasks have not provided a straightforward answer to the question of whether participants with ADHD display more MW. Note that in all these studies the primary task was constantly interrupted by the online questionnaire. This can cause two different artifacts that may influence the level of MW in two different directions, and it can be specifically critical in the case of participants with ADHD. The online technique has two main shortcomings. First, performance in the main task is interrupted, and second, participants are aware of the nature of the investigation while the main task is being performed (e.g., Barron et al., 2011; Bastian & Sackur, 2013; Helton & Warm, 2008; Schooler et al., 2011; Seli, 2016). In the case of ADHD, these shortcomings can be critical since the pauses in the task can affect the vigilance component of the sustained attention task. Vigilance is the ability to remain focused on a task for a period of time and its decrease is reflected in a decline in performance, such as an increase in response latency (e.g., Mackworth, 1950; See et al., 1995). Brief breaks in sustained attention tasks may increase vigilance and improve performance (Ariga & Lleras, 2011; Helton & Warm, 2008).

The ability to maintain attention for a period of time is the component that is assumed to differentiate between ADHD and a typically developed participant. Hence, breaks in the main task might mask real differences between ADHD and typically developed participants in the level of MW or TRI thoughts. On the other hand, the constant inquiries regarding MW during the task can lead to more MW while performing the main task and thus influence the results. For example, even when participants are asked not to think about something (e.g., a white bear), the notion is activated, and participants start thinking about it more often (Wegner, 1994, 1997). Examples of a similar effect can be seen in studies of social priming, in which participants begin to develop a pattern of behavior that is congruent with the primed concept (e.g., Bargh et al., 1996; Dijksterhuis & van Knippenberg, 1998; Goldfarb et al., 2011). Because of the inhibition deficiencies associated with participants with ADHD, they may be more sensitive to such an activation and interfering thoughts may arise more often in the primary task simply because they were repeatedly asked about it. Considering these limitations, it seems that examining MW using another method that utilizes an experimental primary task but without an online questionnaire, could deepen our understanding regarding the relationship between MW, TRI, and ADHD.

Another common method in the literature on interfering thoughts that assesses MW and TRI using an experimental primary task is the end-of-experiment questionnaire. Here, participants perform a primary task and are asked about their thoughts immediately after the experiment ends (e.g., Baird et al., 2012; Smallwood et al., 2009). This method overcomes the previous limitations and although it has been used in the literature on MW and TRI it was rarely used to examine the connection of these thoughts with regards to ADHD. To the best of our knowledge it was only used in one study, but this was done in addition to the online interference method. In Jonkman et al.’s (2017) study described above, typically developed participants with a range of ADHD symptoms completed a MW and TRI questionnaire at the end of the tasks that included online interfering questions as well. The questionnaire given at the end of the experiment revealed that a typical development population with high ADHD inattention symptoms had more MW, but not TRI, compared to low inattention ADHD symptoms. These differences were found only after the reading task but not after the sustained attention task.

However, since the primary task in this study was also interrupted by the online questionnaire, it did not overcome the limitations of task interference as mentioned above. This adds up to the other limitation, that this study was not conducted on a population with ADHD.

To sum up, all the tasks that control for the primary task but one were not conducted on a population diagnosed with ADHD. The study that was conducted on a population of ADHD involved children. In addition, in all of the previous studies the primary task was interfered by an online questionnaire. Finally, those studies that mostly measured MW and TRI were rarely studied in the context of ADHD.

In light of the above, the purpose of this study was to examine the relationship between ADHD and interfering thought and to examine both MW and TRI. Given the limitations in the previous methods that only used online interferences during performance of the task, it seems necessary to test this association with questionnaires at the end of the experiment. Secondly, this experiment examined whether MW and TRI are an independent characteristic of ADHD or related to the fact that performance of ADHD is low in the main task.

As mentioned before, MW may be related to performance, as some studies report a positive correlation between RT or errors and MW (e.g., Cheyne et al., 2009; Kam et al., 2013; Kam & Handy, 2014; McVay & Kane, 2012; Stawarczyk et al., 2014) as well as changes in the level of MW as a result of the attention allocated to the main task (e.g., Baird et al., 2012; Levinson et al., 2012; McKiernan et al., 2006; Smallwood et al., 2004) and as can be interpreted from the executive-resource hypothesis (e.g., Smallwood & Schooler, 2006). Therefore, it is unclear whether MW still characterizes ADHD when compared to typically developed participants with lower performance in the main task, based on the different RT and accuracy measurements previously found in the literature to be correlated with MW. Typically developed individuals show a range of performance abilities in sustained attention tasks. Hence, comparing the level of interfering thought in individuals with a lower performance ability (similar to ADHD performance) can explore whether interfering thought is an independent characteristic of ADHD and can then suggest that other characteristics of ADHD, such as failure of the control system as in the in the control-failure hypothesis (e.g., McVay & Kane, 2010) is related to increased MW.

The current study will investigate this connection and will compare the level of MW and TRI in the ADHD group to two control groups—high main task performers and low main task performers (with a performance level similar to the ADHD group). If MW is indeed an independent component of people with ADHD, we would expect to find more MW in the ADHD group compared to the control groups. If the increased level of MW found in the ADHD group is only due to their poor performance level in the main task, we would expect to find the same level of MW in the ADHD group and the low-performing control group. Regarding TRI, since TRI is another kind of disturbing thoughts, TRI might show a similar pattern of interference as will be observed for MW.

Method

Participants

Sixty-nine university students participated in this study, 23 from each of the following three groups: (1) ADHD aged 20 to 32 (M = 25.78, SD = 3.32); (2) high performance control group aged 21 to 32 (M = 24.91, SD = 2.91); (3) low performance control group aged 20 to 30 (M = 24, SD = 2.71). The participants spoke fluent Hebrew and had normal or corrected-to-normal vision and did not take any medication at least 24 hr before the experiment began or were under the influence of other psychotic drugs. They received in return one credit point, as part of their curriculum requirements at the University of Haifa, or 40 shekels. All participants signed a participation consent form.

ADHD Classification and Assessments

In order to be included in the ADHD group: (1) The participants declared that they had been diagnosed with ADHD; (2) The participants presented a diagnosis from a neurologist, psychiatrist, or family physician who specializes in diagnosing ADHD; (3) The participants had five or more symptoms of ADHD on one part (part A—inattentive, part B—hyperactive/impulsiveness) of the questionnaire, based on the DSM-5 ADHD Criteria Questionnaire (American Psychiatric Association, 2013); and (4) The participants were interviewed before the experiment by a certified learning disability diagnostician.

All ADHD participants underwent a series of tests in order to rule out other learning difficulties that are known to be related to symptoms of attention difficulties: intelligence (Raven Progressive Matrices, Raven, 1960), reading abilities (One-minute test for words, Shatil, 1995a), and basic numerical abilities (2 minute calculation test, Openhaim-Biton & Breznitz, 2004). All participants preformed within the normal range.

The control group had no more than four symptoms of ADHD in either part of the questionnaire, based on the DSM-5 (the range was 0–4), see Table 1. The control group declared that they had not been diagnosed with ADHD or other learning disabilities and a certified learning diagnostician verified this by a short interview. In addition, all the participants (controls and ADHD) studied in departments within the university that requires a minimum score in the psychometric exam (examining English and Hebrew verbal reasoning as well as Quantitative reasoning) that is above the national average.

Table 1.

Means and SDs for Participants’ Characteristics and Dependent Variables, for Each Group.

		ADHD	Low performance	High performance
Age		25.78 (3.32)	24 (2.71)	24.91 (2.91)
Attention questionnaire (sum of symptoms marked “yes”)	Part A	1.78 (1.445)	6.96 (1.796)	2 (1.595)
Attention questionnaire (sum of symptoms marked “yes”)	Part B	1.83 (1.466)	6.96 (1.82)	1.57 (1.5)
RT		405.5 (56.54)	389.32 (45.91)	333.54 (20.12)
Variability of RT		99.96 (20.04)	101.4 (20.84)	67.84 (11.17)
Omission errors		3.22 (3.85)	3.34 (5.95)	0.42 (0.5)
Commission errors		21.65 (13.07)	25.7 (13.63)	20.02 (8.9)

The Experimental Task

Stimulus CPT-GNG—In this study, we used the Continuous Performance Test (CPT) using the Go–NoGo (GNG) procedure. CPT tasks are an umbrella of sustained attention tasks, with the CPT-GNG being one of its common versions. In the CPT-GNG, participants need to respond rapidly to presentation of Go stimuli and withhold responding to NoGo stimuli. NoGo stimuli can be either the rare stimuli or the frequent stimuli. Our CPT-GNG was similar in its main parameters, such as the simplicity of the stimuli and the frequency of the go stimuli to the popular commercial CPT-GNG tasks often used to diagnose ADHD populations, such as: the TOVA-second block (e.g., Leark et al., 2007), and Conners’ Continuous Performance Test (CCPT; Conners et al., 2000). In addition, these tasks commonly measure RT, RT variability, omission and commission errors, as performed in the current study.

As in the study conducted by Gmehlin et al. (2014), each stimulus consisted of one of two geometric shapes: a triangle or a square. The diameter of the circle was 2.5 cm. The triangle was an isosceles triangle, where the length of each side was 3 cm and of the base 2.7 cm. The shapes appeared in the center of the screen and were printed in black on a white screen. The stimuli were selected at random.

Procedure

All participants were run individually and sat approximately 60 cm from the computer screen. Data collection and stimulus presentation were controlled by an HP Compaq computer with an Intel core i7-2600 central processor. Stimuli were presented on a 22-inch Samsung monitor.

The participants were asked to press the space key when a triangle appeared in the center of the screen (go trial) and not to press anything when a square appeared in the center of the screen (no-go trial). Each trial started with the appearance of a black triangle or square at the center of the screen for 200 ms. The participants could respond from the time the stimulus was presented until 1 s. Then, in half of the trials a white screen appeared for 800 ms, and for 1800 ms in the other half. After the white screen disappeared, the next trial began. Each experiment began with a practice block that included 20 trials (16 go trials and four no-go trials), followed by two blocks of 138 experimental trials (228 go trials and 48 no-go trials). The task length was about 12 min. RT in milliseconds, as well as errors, were collected in each trial by the computer. This allowed the calculation of the parameters: commission errors, omission errors, mean RT, and variability of RT.

At the end of the CPT-GNG task, the participants completed self-report questionnaires in counterbalanced order. One questionnaire was the individuals’ general propensity to mind wander questionnaire. This questionnaire was developed by Mrazek et al. (2013) in order to assess MW as a general property, meaning the questionnaire included everyday tasks and the participants had to report the amount of unrelated thoughts that interfere with those tasks. For example: “While reading, I find I haven’t been thinking about the text and must therefore read it again.” The questionnaire consists of five statements, and response options were designated along a 6-point Likert scale (1—almost never; 2—very infrequently; 3—somewhat infrequently; 4—somewhat frequently; 5—very frequently; 6—almost always), with Cronbach’s alpha of .850.

The second questionnaire was the mind wanders in momentary situation questionnaire. This questionnaire is part of the thinking content section in the Dundee Stress Test (Matthews et al., 1999) and was used by previous studies to assess MW and TRI during a specific experimental task (e.g., Maillet & Rajah, 2013). This questionnaire consists of 15 statements: 8 on MW, for example “I thought about members of my family” (internal consistency of .85) and 7 on TRI, for example “I thought about the purpose of the experiment” (internal consistency of .76). For each statement the participants had to note the prevalence of their thoughts while performing the sustained attention task. The answers were on a 1 to 5 scale (1—never, 2—once, 3—a few times, 4—often, 5—very often).

The questionnaires were translated into Hebrew with the forward and backward translation procedure. Two native speakers of Hebrew who speak fluent English, separately translated the original questionnaires once. A backward translation of the reviewed version was then translated into Hebrew separately by two other native speakers of Hebrew who speak fluent English, to verify that the meaning of each item in the questionnaires had been preserved. The validity of each questionnaire was tested separately in a pilot study of 20 typically developed students. Cronbach’s alpha was revealed to be α = .833 for the MW questionnaire, α = .762 for the TRI questionnaire, and α = .867 for the general propensity questionnaire. Furthermore, the pilot study confirmed that all questions were clear to the participants.

The order of the questions was mixed randomly, such that two questionnaires with a different random order were created. Each participant received one of the two versions, in counterbalanced order. The average score for each participant was calculated for the general propensity, MW, and TRI questionnaires.

Results

Errors and responses of under 150 ms were not included in the analysis. For the remaining trials, for each participant mean RT was calculated, as well as omission and commission errors. In order to divide the control group into two groups of high and low performance: for the control group, standard scores were calculated for RT and errors and then averaged in order to create a performance score. According to the median score for performance, the control group was divided into two groups: high and low performance. Thus, three groups were created with 23 participants each: (1) an ADHD group, 15 females; (2) a high performance control group, 12 females; (3) a low performance control group, 16 females.

CPT-GNG Performance

RT analysis

Mean RT for each group was calculated

To test the differences in RT between the three groups, a one-way analysis of variance was applied (see Table 1). The results revealed a significant difference between the groups [F(2, 66) = 17. 22, p < .001].

Further orthogonal analysis revealed significantly better performance in the high performance group than in the ADHD and low performance groups [t(64.4) = 7.35, p < .001]. The ADHD and low performance groups did not differ from each other significantly [t(44) = −1.07, N.S.].

In addition, analyzing the difference in RT between the high performance and the two other groups: ADHD and low performance, using the Bayesian approach (using JASP; JASP Team, 2018), revealed a BF10 of 83,396.44, suggesting extreme evidence for the hypothesis that the high performance group are faster than the ADHD and low performance groups. When comparing between the ADHD and low performance control group, a BF10 of 0.462 was revealed, suggesting no meaningful evidence for a difference between the groups.

RT variability

Mean RT variability for each group was calculated

To test the differences in RT variability between the three groups, a one-way analysis of variance was applied (see Table 1). The results revealed a significant difference between the groups [F(2, 66) = 20.94, p < .001].

Further orthogonal analysis revealed significantly better performance in the high-performance group than in the ADHD and low performance groups [t(66.99) = 8.03, p < .001]. The ADHD and low performance groups did not differ from each other significantly [t(44) = .213, N.S.].

In addition, analyzing the difference in variability between the high performance and the ADHD and low performance groups using the Bayesian approach revealed a BF10 of 1,638,000, suggesting extreme evidence that the high-performance group have less variability than the ADHD and low performance groups. When comparing between the ADHD and low performance control groups, a BF10 of 0.298 was revealed, suggesting moderate evidence for no difference between the two groups.

Omission errors

Mean omission errors rate for each group was calculated for each group

To test the differences in omission errors between the three groups, a one-way analysis of variance was applied (see Table 1). A significant difference was found between the groups [F(2, 66) = 3.75, p < .05].

Further orthogonal analysis revealed significantly better performance in the high-performance group than in the ADHD and low performance groups [t(46.85) = 3.89, p < .001]. The ADHD and low performance groups did not differ from each other significantly [t(44) = .078, N.S.].

In addition, analyzing the difference in omission errors between the high performance and the ADHD and low performance groups using the Bayesian approach revealed a BF10 of 11.692, suggesting strong evidence that the high-performance group had less errors than the ADHD and low performance groups. When comparing between the ADHD and low performance control groups, a BF10 of 0.293 was revealed, suggesting moderate evidence for no difference between the two groups.

Commission errors

Mean commission errors rate for each group was calculated

To test the differences in commission errors between the three groups, a one-way analysis of variance was applied (see Table 1). No difference was found between the groups [F(2, 66) = 1.37, N.S.].

In addition, analyzing the difference in commission errors between the high performance and ADHD groups together with the low performance group using the Bayesian approach revealed a BF10 of 0.81, suggesting once again no meaningful evidence of a difference between the groups.

When comparing between the ADHD and low performance control groups a BF10 of 0.451 was revealed, suggesting once again no meaningful evidence for a difference between the groups.

Overall, the results indicate that the performance of the high-performance control group differs from that of the ADHD group and the low performance group in the measures of RT and omission errors, while the ADHD and low performance groups had a similar performance level.

Questionnaires

Mind wander/TRI questionnaire in momentary situation

To test the research hypotheses about differences in the types of thoughts between the ADHD and control groups after CPT-GNG performance, a two-way analysis of variance was applied to thought type (MW/TRI) as the within participant factor and group (ADHD/high performance/low performance) as the between participants factor, and average score on each questionnaire as the dependent variable. A significant main effect was found for thought type, F(1, 66) = 34.29, MSE = .45, p < .001 and for group, F(2, 66) = 5.68, MSE = .55, p < .01.

Further orthogonal analysis revealed that the ADHD group displayed more MW than the control groups [t(67) = 2.99, p < .01] and specifically, based on the research question, more MW than the low performance group [t(44) = 3.2, p < .01]. No significant difference was found in the control groups between high and low performance [t(44) = 1.12, N.S.] (see Figure 1).

Figure 1.

Average score and SD for the questionnaires as a factor of group and questionnaire type.

In addition, analyzing the difference in MW between the ADHD and control groups using the Bayesian approach revealed a BF10 of 20.24, suggesting that the hypothesis that ADHD display more MW than controls is 20.24 times more likely than the null hypothesis, which suggests strong evidence for the hypothesis that MW is associated with ADHD more than with controls even when using questionnaires at the end of the experiment.

When comparing between ADHD and the low performance control group, a BF10 of 29.071 was revealed, suggesting that the hypothesis that ADHD display more MW is 29.071 times more likely than the null hypothesis, which suggests strong evidence for the hypothesis that MW is an independent characteristic of ADHD, regardless of their level of performance on the main task.

Finally, analysis of differences between the high performance control group and the low performance control group yielded a BF10 of 0.485, suggesting no meaningful evidence for the hypothesis that MW is similar between the two groups.

For TRI, the results suggested that the ADHD group had more TRI than both control groups [t(67) = 1.87, p < .05] as well as than the low performance group [t(44) = 1.94, p < .05]. No significant difference was found between the high and low performance groups [t(44) = .45, N.S.].

Analysis of the difference between the ADHD and control groups using the Bayesian approach revealed a BF10 of 2.127 between the ADHD and control groups and a BF10 of 2.51 between the ADHD and the low performance control group, suggesting that the hypothesis that ADHD have more TRI is 2.51 times more likely than the null hypothesis. In addition, analysis of the difference between the high performance control group and the low performance control group revealed a BF10 of 0.318, suggesting that the null hypothesis (i.e., no differences between the control groups) is 3.18 times more likely than the alternative hypothesis, that is, presenting moderate evidence for the hypothesis that TRI is similar between the two groups.

The General Propensity Questionnaire

To test the research hypotheses concerning differences in the general propensity questionnaire between the ADHD and control groups, a one-way analysis of variance was applied to general propensity thought as the within participant factor and group (ADHD/high performance/low performance) as the between participants factor. A significant difference was found between the groups [F(2, 66) = 29.56, p < .001].

Further orthogonal analysis revealed that the ADHD group have more of a personal tendency than both control groups [t(67) = 7.47, p < .001] and specifically, based on the research question, that they had more general propensity thought than the low performance group [t(44) = 7.8, p < .001]. No significant difference was found between the high and low performance groups [t(39.32) = 1.56, N.S.].

Analyzing the difference in this questionnaire using the Bayesian approach revealed a BF10 of 6.421e+7, suggesting that the hypothesis that ADHD have more of a personal tendency to MW is 6.421e+7 times more likely, suggesting extreme evidence for the hypothesis that ADHD have more general propensity thought. Furthermore, when comparing between the ADHD and the low performance control group alone, a BF10 of 1.847e+7 was revealed, suggesting that the hypothesis that ADHD have more of a personal tendency for MW is 1.847e+7 times more likely than the null hypothesis, that is, presenting extreme evidence for the hypothesis that general propensity thought is an independent characteristic of ADHD, regardless of their level of performance in the main task.

Finally, comparing between the high performance control group to the low performance control group revealed a BF10 of 0.779, suggesting no meaningful evidence for H1.

Discussion

The current study examined interfering thoughts in participants with ADHD and in control groups. The novelty of the current experiment is that this experiment examined this connection (1) when the main task was experimentally controlled but there were no online interruptions; (2) in addition to MW, TRI was also examined; and (3) participants with ADHD were compared to two control groups, such that the control group was divided into high and low performers. In this manipulation we could specifically examine the performance of participants with ADHD versus typically developed participants with low performance in the CPT-GNG task, similar to that of ADHD. Hence, this manipulation eliminated the factor of performance in the main task as a potential explanation of the variance in MW.

Overall, the results indicated that participants with ADHD have a significantly greater tendency to MW in everyday life compared to the control groups, as measured in the general propensity to MW questionnaire as well as in momentary situation (MW) and task interferences (TRI) questionnaires during a specific experimental task.

Our findings regarding the general propensity to MW questionnaire are in line with previous studies which reported that participants with ADHD display more mind wandering, using the general questionnaire technique that related to mind wandering in daily life (Jonkman et al., 2017; Mowlem et al., 2019; Seli et al., 2015; Weyandt et al., 2003). As noted above, those studies did not measure the task from which the participants’ mind actually wandered. This makes it unclear as to whether the increased MW in a population with ADHD is observed because of their different choice of primary tasks or their different performance on those tasks. Moreover, studies that examined MW in participants with ADHD often used research methods that may affect the results, such as online questionnaires (e.g., Jonkman et al., 2017; Stawarczyk et al., 2014). This method interferes with the participants while performing the primary task as the participants are asked about the appearance of MW. This method can lead to priming effects, for instance when MW rises to consciousness it may lead to these thoughts (e.g., Bargh et al., 1996; Dijksterhuis & van Knippenberg, 1998; Goldfarb et al., 2011). In addition, these questions can affect the vigilance component of the sustained attention task (e.g., Ariga & Lleras, 2011; Helton & Warm, 2008).

The current study attempted to address these issues, as it also measured MW when the main task was experimentally controlled but there were no online interruptions. Using this approach, it was found that participants with ADHD are more prone to MW. This provides another strong piece of evidence for the connection between MW and ADHD and eliminates alternative explanations for this connection.

Another important finding in the current study is the comparisons that take into account the participants’ level of performance in the main task. Here, the level of MW in participants with ADHD was also compared to a low performance control group who had a similar performance level on the main CPT-GNG task (similar RT, omission and commission errors, and RT variability). The current study found that participants with ADHD showed more MW than the control groups and, interestingly, they also showed more MW than the low performance control group. No significant difference in MW was found between the high and low performance control groups. As mentioned above, it has been suggested that MW is associated with participants’ low performance (e.g., Cheyne et al., 2009; Kam et al., 2013; Kam & Handy, 2014; McVay & Kane, 2012; Stawarczyk et al., 2014). However, the current study found that MW is an independent characteristic of ADHD and that ADHD show more MW even when participants with ADHD are compared to a control group with low performance on the main task.

As noted before, the literature suggests a connection between ADHD and MW. On one hand this connection can be explained by the performance level of ADHD in the primary task. For example, since those with ADHD invest less in the main task, they have more resources for MW. Or, alternately, poor performance leads to MW. This connection can be explained by several theories, for example Smallwood and Schooler (2006) suggested a trade-off between performance in the task and MW. They claim that MW and executive control use the same resources or mechanism and when participants invest less resources in the main task, they have more resources left for MW. Hence, it is possible that ADHD invest little attention resources in the main task and therefore have more resources left for MW. Other findings showing that participants who invest less in performance have more MW can be found in studies whereby fatigue also leads to more MW (see examples in McVay & Kane, 2010).

Another theory that supports the connection between the degree of investment in a task and MW is that of Mason et al. (2007), which investigated the involvement of the DMN in states of MW. They suggested that when the brain uses less cognitive resources there is an increase in DMN activity, which leads to more MW. Thomson et al. (2013) support a similar approach, as they suggest that MW occurs when participants take the attentional resources assigned to the task and redirect them to MW.

Another way to explain the connection between MW and ADHD, is that MW is an independent characteristic of ADHD and the level of MW is not directly explained by the level of performance. In this explanation, MW has a more central place as a symptom of ADHD. In line with this notion, some theories indicate certain deficient mechanisms in ADHD, such as a lack of inhibition or low meta-awareness, which can explain MW. For example, McVay and Kane’s (2010) theory claimed that MW arises because of the difficulty of the executive control system to suppress disturbing and automatic thoughts. Since it was suggested that one of the main deficiencies in ADHD is impaired in the inhibition system and executive resources (e.g., Barkley, 1997), this impairment may affect their difficulties to suppress unrelated thoughts. Another example is a low meta-awareness of distractors, which is associated with the ADHD population. Accordingly, it may take individuals with ADHD a long time to be aware that their mind is wandering. For example, Franklin et al. (2017) investigated the relationship between MW in daily life and in the laboratory to symptoms of ADHD in adults with no diagnosis of ADHD. The results showed that meta-awareness mediates the relationship between MW and symptoms of ADHD. In other words, those who had more symptoms of ADHD were less aware that their mind was wandering. Moreover, they suggested that those with high symptoms of ADHD had difficulty operating high control processes to control their MW. The findings of Seli et al. (2015) also supported the suggestion that some MW is a feature of ADHD. The researchers’ conclusion was that the tendency to MW in individuals with ADHD may be due to both lack of control of MW and lack of awareness of MW.

Furthermore, Bozhilova et al. (2018) suggested that abnormal activity of the DMN and inadequate communication between the DMN and executive control, lead to spontaneous MW (uncontrolled thoughts that interfere with tasks), which underlie the inattentive symptoms of individuals with ADHD and their cognitive deficit in cognitive performance. Hence, as noted before, the current findings suggest that MW is an independent characteristic of individuals with ADHD and it is not simply associated with ADHD because this population perform badly on the main task.

Moreover, the nonclinical control group with low performance showed similar performance in a sustained attention task as those with a clinical diagnosis of ADHD. The present findings show an interesting different cognitive profile between participants with ADHD and the low-performance control group. Although, these two groups have similar performance they have different levels of MW. This means that a poor performance level per se is not the reason for the increased MW in ADHD participants and that this increased level of MW should be linked to the ADHD deficiency. It is possible that among the ADHD participants MW caused some decline in performance, or that another cognitive mechanism led to both poor performance and increased mind wandering. However, in the lower control group what led to a poor performance level in the main task is clearly unrelated to MW. This strengthens the assumption that increased MW is an independent characteristic of the ADHD population.

Finally, the current study also examined whether participants with ADHD not only have more MW but also more TRI. As noted before, similar to MW, TRI refers to interfering thoughts, however here the interfering thoughts relate to the task but do not refer to the stimuli of the task, such as thoughts about one’s performance. Studies show different connections between performance on main tasks and MW or TRI (e.g., Stawarczyk et al., 2011a) as well as differences in brain activity between those thoughts (e.g., Stawarczyk et al., 2011b). As noted, only few studies have been conducted on the relationship between ADHD and TRI. For example, Jonkman et al. (2017) investigated TRI in relation to ratings of attention-deficit symptoms in a population of typically developed students with no ADHD diagnosis. No differences were found between the groups.

Van den Driessche et al. (2017), in experiment 2, found that adults who described symptoms of ADHD but had not been diagnosed with ADHD did not display different levels of TRI (or differences in MW). To the best of our knowledge, only one study investigated TRI in a population diagnosed with ADHD, and this was in a population of children, finding no differences in the TRI level. In the current study, using a conventional ANOVA analysis, our results suggest that participants with ADHD compared to controls had not only a greater tendency to MW but also to TRI. However, Bayesian analysis further suggests that the differences between controls and ADHD has stronger support for MW than for the TRI component. If indeed ADHD have also more TRI than typically developed participants, this may reflect a borderline deficiency in different types of interfering thoughts. However, our data suggest more support for increased MW than TRI in the ADHD population. In addition, the literature on this topic includes more studies that found a connection between ADHD and increased MW than ADHD and increased TRI. Hence it seems that for now increased MW as a characteristic of the ADHD population has better support. Other studies should further investigate the relationship between TRI and ADHD and examine whether it can be found in additional tasks and conditions.

Despite the important results in this study, it has several methodological limitations, including a small sample size and the specific population in the present study, which was composed of students in higher education, and therefore is not necessarily representative of the population as a whole. It is also important to note, however, that even in a population of university students, characterized by a relatively high functioning ADHD population, the ADHD group still demonstrates difficulties controlling their disruptive thoughts.

To sum up, the main finding of the current study is that increased MW is an independent characteristic of individuals with ADHD. This finding remained when the main task was experimentally controlled and there were no online interruptions, as well as when participants with ADHD was compared to a control group with similar performance in the main task. These findings have theoretical implications for understanding the connection between MW and ADHD as well as potential implications for the field as a whole. If indeed MW is an independent characteristic of ADHD this may have implications also for the inclusion of MW symptoms in the ADHD diagnosis process, making the diagnostic process more accurate and reliable. Furthermore, it may lead to developing treatments that focus on MW for this specific population. This might help those individuals to control their thoughts, or to be able to recognize such thoughts before or during their emergence. Consequently, this might affect different aspects of everyday life, as it might help reduce interfering thoughts during different everyday tasks such as concentrating on a lecture, studying for exams, or even engaging in a conversation with a friend.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Klodin Alali-Morlevy

Author Biographies

Klodin Alali- Morlevy, is a certified learning disability diagnostician and conducting the PhD research at Edmond J. Safra Brain Research Center for the Study of Learning Disabilities, University of Haifa, Israel.

Liat Goldfarb, PhD is the head of the attention laboratory at Edmond J. Safra Brain Research Center for the Study of Learning Disabilities and a faculty member at the Department of Learning Disabilities,University of Haifa, Israel.

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