# Interest and mind wandering: How do individual and situational characteristics impact learning?

Louise Perche, Nora Yennek, Laure Léger

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Louise Perche, Nora Yennek, Laure Léger. Interest and mind wandering: How do individual and situational characteristics impact learning?. Learning and Motivation, 2025, 92, 10.1016/j.lmot.2025.102177. hal-05227665

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# Interest and mind wandering: How do individual and situational characteristics impact learning?

Louise Perche*, Nora Yennek, Laure Léger

Interdisciplinary Laboratory in Neuroscience, Physiology, and Psychology, Paris Nanterre University, 200 avenue de la République, Nanterre 92000, France

## ARTICLE INFO

**Keywords:**

Mind wandering

Interest

Learning

Attention scale

Motivation

## ABSTRACT

This research examines the impact of individual and situational characteristics on mind wandering and interest on learning. Mind wandering is an attentional internal distraction that is deleterious to one's performance in sustained attention tasks (Cheyne et al., 2009) and in learning (Szpunar et al., 2013). First, two studies were conducted to validate a mind wandering scale in French (with a trait and a state dimension)—both in ecological and in laboratory settings (studies 1 and 2). Then, an experiment was conducted to induce mind wandering and explore the link between individual and situational characteristics of interest and their impact on learning (Study 3). Interest was gauged using a self-reported scale. Mind wandering was measured both by probe-caught measures and self-reported scales. The main results suggest that mind wandering mediates the link between individual interest and situational interest and that a mind wandering state and situational interest jointly influence learning scores.

## 1. General introduction

By examining interest, psychological research seeks to understand better the various factors that influence a learning situation, including both individual and situational factors. Interest and boredom are the most common emotions experienced by students in the classroom, comprising essential elements of their academic motivation (Fenouillet et al., 2017; Mitchell, 1993; Tanaka & Murayama, 2014). A lack of attention in class is often seen as an indicator of students' lack of interest. However, few studies have explored the relationship between interest and attention through the concept of mind wandering and its effects on learning. This article seeks to fill this gap in the literature by proposing an innovative approach that explores this relationship in adult learning situations through the distinction between individual and situational interest, a mind wandering state, and the mind wandering trait.

The aim was to understand more clearly the links between these two factors in a learning situation. Such an understanding would help design learning environments that promote adult learning. To achieve this goal, two studies were conducted. The first sought to validate in French the tools for measuring mind wandering (as both a state and a trait) using self-reported scales. While several tools to measure mind wandering exist, it is beneficial to construct one that can adapt to different learning situations and measure individual and situational characteristics. The second study aimed to measure mind wandering with this new tool and explore its link with individual interest, situational interest, and learning when students are learning with a video. In recent years, particularly since the COVID crisis, asynchronous courses on Moodle-like online platforms, which typically include resources such as PDF documents or

<footnote>* Correspondence to: Departement de Psychologie, Université Paris Nanterre, 200 avenue de la République,, Nanterre 92000, France.
E-mail addresses: l.perche@parisnanterre.fr (L. Perche), nora.yennek@gmail.com (N. Yennek), lleger-chorki@parisnanterre.fr (L. Léger).</footnote>
videos, have become increasingly prevalent. Asynchronous courses can thus serve as valuable supplementary tools for lectures. However, this type of learning situation requires us to explore the links between attention and interest in learning when the learner is alone behind a screen, a facet of student attention that differs from the classroom context.

The first part of the introduction explains mind wandering and its connection to learning, followed by a presentation of the concept of interest. The introduction will also include a literature review to explore the link between interest and mind wandering in learning contexts prior to offering the theoretical hypotheses that underpin this study.

## 1.1. Mind wandering

In this paper, mind wandering is explored as a way to study attention in learning, with mind wandering serving as a means of establishing diverted attentional focus. Specifically, this approach involves examining how a learner's attention can be diverted from the learning situation by internal thoughts, which can be just as deleterious to learning as any external distractions. Importantly, the definition and scope of what constitutes mind wandering have been debated in the literature (e.g., Christoff et al., 2016). However, in this paper, mind wandering is defined as self-generated and task-unrelated thoughts, following the definition provided by Smallwood and Schooler (2006, 2015).

A clear distinction must be made between the state of mind wandering (i.e., the actual mind wandering during a task, referring to the definition above) and the trait of mind wandering. The trait of mind wandering refers to one's tendency, in daily life, to have thoughts unrelated to a primary task. In this case, people who tend to mind wander in their daily lives will also show a tendency toward lower attentional control and lower full awareness of their actions (Mrazek et al., 2013). Conversely, the state of mind wandering refers to the phenomenon of mind wandering that occurs during the temporal course of the situation. In studies focused on mind wandering, this phenomenon is primarily considered to be a state, hence the terminology used to refer to it here. However, it is important to take into account the individual differences between one individual's mind wandering tendencies and another's.

## 1.2. Mind wandering and learning

Mind wandering during a task results in a decrease in the cognitive resources allocated to perform said task. Mind wandering is thus deleterious to performance, specifically during a sustained attention task (e.g., Cheyne et al., 2009), during reading tasks for text comprehension (e.g., Mooneyham & Schooler, 2013), or in a learning context (e.g., Szpunar et al., 2013).

From a reading comprehension perspective, mind wandering negatively affects several levels of the construction of a coherent mental representation. For instance, mind wandering is negatively linked with the word frequency effect, the phenomenon in which words are recognized more quickly when they are seen more frequently (Franklin et al., 2013; Reichle et al., 2010). This negative correlation has significant implications for understanding the negative impact of mind wandering on the construction of a coherent situation model when reading a text (Smallwood et al., 2008). Moreover, readers who report more mind wandering seem to have a lower level of overall comprehension and make more comprehension errors (Sanchez & Naylor, 2018). These findings have been further supported by a recent meta-analysis by Bonifacci et al. (2022) that indicated that mind wandering is negatively related to text comprehension.

Regarding learning as a measure of learning performance, studies have found a negative relationship between mind wandering and learning in an ecological context (e.g., Linquist & McLean, 2011; Wammes et al., 2016) and in laboratory settings (e.g., Conrad & Newman, 2021; Hollis & Was, 2016; Kane et al., 2021; Loh et al., 2016; Pan et al., 2020; Risko et al., 2012; Seli et al., 2016; Was et al., 2019; Wong & Lim, 2021). For example, Conrad and Newman (2021) explored the relationship between mind wandering and online learning. Participants watched a 75-minute educational video on venture capital, a financial concept. During the session, their mind wandering was measured, and questionnaires assessed their knowledge before and after the training. The results showed that participants who reported more frequent mind wandering obtained lower scores on the knowledge questionnaires.

Nevertheless, few other researchers have demonstrated a significant link between mind wandering and learning (e.g., Martin et al., 2018; Wammes et al., 2019). The difference between these findings could be explained by the lesson's topic or its difficulty. Relatedly, Risko et al. (2012) investigated how the amount of time spent on a task relates to mind wandering in two different settings: a simulated classroom with projected videos and a learner-only laboratory where participants viewed videos on a computer screen. The participants watched three one-hour videos on different subjects (psychology, economics, and classics), and their levels of mind wandering were measured during this time. After watching the videos, the participants were asked questions about the content. The results indicated that there was a negative relationship between mind wandering and recall scores in both settings. More precisely, a correlation analysis showed that a mind wandering state is more negatively related to lecture performances in psychology than in economics or classics lectures.

These results made us wonder if the learner's interest in the topic could explain the observed variability in results. Maybe the students, who were probably psychology students, had different levels of thematic interest in economics or classics than in psychology. It would then be necessary to measure learners' topic interest in the course when looking at the link between mind wandering and learning. Furthermore, it should be noted that mind wandering could also be beneficial in certain situations, particularly those that require creativity, such as problem-solving (Baird et al., 2012; Irving et al., 2022; Murray et al., 2021).

To summarize, mind wandering is deleterious at different performance levels, such as reading comprehension or learning. As a result, reducing mind wandering during learning situations is a critical challenge for teachers and researchers. Because interest has an impact on attentional focus (Hidi, 2001), promoting and/or establishing interest could be an effective tool to prevent mind wandering.
## 1.3. Interest

Interest is a cognitive motivational variable that is assumed to focus attention (Hidi, 2001) and facilitate learning in different contexts (Ainley et al., 2002). It develops through the interaction of the person with their environment (Renninger & Hidi, 2011). Interest appears to be a complex state that combines cognitive and emotional aspects (Schiefele, 2009). It is a motivational variable that reflects the learner's commitment to the learning situation. In the early stages of interest, its emotional component may be positive or negative, but as interest develops, the person is more likely to feel positive emotions toward the content. The cognitive component of interest arises through several phenomena: an attentional focus on the content, perseverance in engaging with the content, and the ability to take into account environmental feedback as interest develops (Hidi & Renninger, 2006; Renninger & Sue, 2019).

Interest can be distinguished into two main categories: situational interest and individual interest. Individual interest is defined as an individual's predisposition to reengage in time on a particular type of content (e.g., if someone is individually interested in learning, that person will tend to engage easily in activities requiring learning). Situational interest is defined as an affective reaction immediately triggered by stimuli in the learning environment that have captured an individual's attention (e.g., when the teacher announces that a class is going to watch a video, students' interest is activated by the situation of watching a video). These reactions can last for extended periods of time or not (Hidi, 1990). Therefore, topic interest is a multidimensional variable (Renninger & Sue, 2019), referring to a specific form of individual interest that is more focused on a specific domain of knowledge that can also be situationally influenced (e.g., if a person is individually interested in learning how to knit, they will tend to reengage in knitting content, and when this person spots a knitting book at the library, they will tend to pick it up, peruse it, and perhaps check it out).

The four phases model of interest development (Hidi & Renninger, 2006) describes the development of a person's interest in several steps: activated situational interest, maintained situational interest, emerging individual interest, and developed individual interest. In this conception, the experience of a particular topic can cause the development of individual interest. This development is linked to the acquisition of knowledge with this content, positive affective reactions, and external support from the environment in the early phases of development. According to the authors, these phases would be sequential, distinct, and cumulative and would develop progressively if encouraged and supported by the environment (Hidi & Renninger, 2006; Renninger & Sue, 2019).

## 1.4. Interest and learning

Interest allows learners to connect with learning content and to maintain this connection while learning (Ainley et al., 2002), thus allowing learners to remain engaged in the learning activity. Indeed, Kpolovie et al. (2014) showed that students' academic achievement is significantly predicted by individual interest in learning and positive attitudes toward school. Consistent with these results, Ernawati et al. (2022) also found that students' individual interest in learning significantly predicts their learning performance.

Furthermore, fostering situational interest encourages the development of individual interest, as reflected in the interest development model (Hidi & Renninger, 2006; Renninger & Sue, 2019; Rotgans & Schmidt, 2017). Accordingly, activating learners' situational interest has emerged as an essential educational issue. Some researchers have studied pedagogy to ascertain what activates and maintains learners' situational interest in the classroom. In this regard, Rotgans and Schmidt (2011) found that active pedagogy, specifically problem-based pedagogy (Rotgans & Schmidt, 2017), triggers learners' situational interest.

## 1.5. Interest and mind wandering in learning

Several studies have explored the impact of individual differences, interests, and task difficulty on mind wandering and comprehension. Unsworth and McMillan (2013) showed that motivation, working memory capacity, and thematic interest influence mind wandering during text reading, with motivation mediating the relationship between interest and wandering. Fulmer et al. (2015) demonstrated that text difficulty and thematic interest interact to influence learner engagement, reducing mind wandering and enhancing learning transfer, provided learners have expressed their interest preferences beforehand. Lindquist and McLean (2011) observed a marginal correlation between course interest and a wandering mind in a university course. Importantly, they also found that mind wandering is associated with less detailed note-taking and lower learning performance. In 2019, Soemer and Schiefele pointed out that text difficulty increases mind wandering and impairs comprehension, but topic interest can moderate this negative effect. Finally, in the context of distance learning, Hollis and Was (2016) revealed that subject interest reduces mind wandering and improves performance on learning tests, whereas working memory capacities have a weak influence on the link between mind wandering and performance. In sum, these studies have shown that mind wandering is a critical factor in mediating interest, difficulty, and comprehension during learning.

The relationship between mind wandering and interest has been studied in different learning contexts. In the extant literature, topic interest appears as a predictor of mind wandering (Hollis & Was, 2016). Furthermore, the negative correlation between interest and mind wandering in the classroom appears to be only marginal (Linquist & McLean, 2011).

Kane et al., (2017, 2021) examined this issue in field studies and found that mind wandering mediates the link between specific individual characteristics (such as motivation or topic interest) and learning, as well as between individual characteristics and situational interest. In one of these studies, Kane et al. (2017) explored the link between mind wandering, individual and situational interest, and note-taking during video learning. The study comprised four steps: measures of individual characteristics (working memory, statistical knowledge, note-taking habits, multitasking, and interest in and beliefs about mathematics), a pretest video with self-assessment learning, viewing a statistics video with measures of mind wandering, and situational measures (interest and learning). The results indicated that mind wandering is affected by prior knowledge, interest in mathematics, and multitasking. Importantly, they
found that mind wandering predicts situational interest and learning outcomes and mediates between individual characteristics and learning scores. However, the study did not link situational characteristics, considering situational interest and learning scores as distinct. In their 2021 study, Kane et al. replicated these findings in a more ecological context, further investigating the link between individual characteristics, mind wandering, situational interest, and learning. Based on their findings, they posited that mind wandering would explain part of the link between individual and situational interest. Given that the variables included in their model are similar to those studied in this framework and that their study's context is comparable to that developed in our article, we have based our hypotheses on this theoretical model.

Notably, though, Kane et al., (2017, 2021) considered situational interest as their outcome. Therefore, to our knowledge, no studies have investigated the link between interest, mind wandering, and learning outcomes. Additionally, topic interest was considered an individual characteristic in several related studies, yet it was often only measured after the learning situation (e.g., Hollis & Was, 2016; Soemer & Schiefele, 2019; Unsworth & McMillan, 2013). Since topic interest was measured afterward, despite having both an individual and a situational dimension (Ainley et al., 2002), the situation itself could have had an impact on the level of thematic interest. It therefore seems important to measure thematic interest before the learning context occurs to prevent the context from biasing the score obtained. Given both the situational and individual nature of interest, a full understanding entails clarifying whether this link is due to an individual or situational characteristic of topic interest. The only study examining topic interest prior to the situation was conducted by Gericke et al. (2025). The study aimed to find out if topic interest, when measured before reading, is related to text comprehension. Their results showed that topic interest reduced mind wandering while reading and improved comprehension. The authors highlight the importance of considering topic interest as an effective way to counteract the negative effects of mind wandering on comprehension.

To account for this gap in the literature, the research question here explores the link between different interest forms and mind wandering in multimedia learning. The main goal of the first study was to validate a French version of a scale of mind wandering that evaluates two mind wandering components: its trait dimension using the Mind Wandering Questionnaire (MWQ) (Mrazek et al., 2013) and its state dimension using the Task-Specific Mind Wandering Questionnaire (TSMWQ) (Sanchez & Naylor, 2018).

The validation study of the mind wandering questionnaire took place in two parts. In the first part (Study 1), 341 participants completed the trait and state mind wandering scales under ecological learning conditions. This phase allowed us to assess the tool's structure and confirm that it has two dimensions: individual and situational interest. In the second phase (Study 2), 92 participants were tested in a laboratory setting and filled out various scales. This phase aimed to validate the original scales for convergent validity and to evaluate the scales' predictive validity on performance in a sustained attention task.

This validation allowed us to ensure we had a solid tool to measure mind wandering during multimedia learning with a video and to test the central hypothesis of this paper regarding how mind wandering, and interest interact to influence learning (Study 3).

## 2. Study 1: Internal consistency and homogeneity of the mind wandering questionnaire - ecological setting

### 2.1. Introduction

As an internal state, mind wandering is challenging to measure objectively, with the only clearly verifiable methods of securing objective evidence of mind wandering coming via physiological measures of brain activity or pupillary dilation in controlled luminance conditions. To account for this, self-reported measures have been developed to measure mind wandering subjectively.

The most common way of measuring mind wandering is the probe-caught method. In this method, the task is randomly stopped, and the experimenter asks the participant to declare the focus of their thoughts at that precise moment (e.g., “Just right now, what were you thinking about?” or “Just right now, was your attention on task or off task?”). The response categories can range from yes/no to a selection of different types of thoughts (usually categorized as task-unrelated thoughts, task-related thoughts, or on task). Other options also exist, such as a scale wherein the participant must rate their attention as completely on task or completely off task (Weinstein, 2018).

Two other approaches involve the self-caught method and self-reported scales. For the former, participants must press a button each time they feel their mind drift away. Meanwhile, self-reported mind wandering state scales entail participants indicating on a Likert scale their attentional state during the previous task (e.g., Sanchez & Naylor, 2018) or their tendency to mind wander during daily life (mind wandering trait) (e.g., Cheyne et al., 2006; Mrazek et al., 2013).

However, for our purposes, developing adapted tools that precisely measure the trait or state of mind wandering, two distinct dimensions remained necessary (Bonifacci et al., 2022). Additionally, to conduct our research, we had to ensure these tools were adapted to the French language (because our study was conducted in France).

One of the most commonly used self-report mind wandering scales is the Mind Wandering Questionnaire (MWQ) (Sanchez & Naylor, 2018). This five-item scale measures mind wandering as a trait (i.e., the tendency of individuals to engage in mind wandering in their daily lives). Extending its applicability, Sanchez and Naylor (2018) adapted the MWQ scale to measure mind wandering as a state during a text-reading task, naming this adapted scale the Task-Specific Mind Wandering Questionnaire (TSMWQ).

As part of their validation of the MWQ, Sanchez and Naylor (2018) authors tested the scale's predictive validity with a working memory task (i.e., the Operation Span Task (OSPAN) and its convergent validity with different scales, including an attentional control scale, a mindfulness scale, an emotion scale, and an anxiety scale. These scales were also used to validate the TSMWQ. Accordingly, the same scales were used when available and validated in French, with Watson et al. (1988) validating an emotion scale (positive affect and negative affect scale (PANAS)), Derryberry and Reed (2002) an attentional control scale (ACS), and Brown and Ryan (2003) a mindfulness scale (Mindfulness Attention Awareness Scale (MAAS)). On the other hand, the probe-caught measure has been used by
Sanchez and Naylor (2018) for concurrent validity of mind wandering scale.

## 2.2. Hypotheses

**Hypothesis 1.** Two dimensions of mind wandering will be established: one based on the individual's trait characteristics and the other on the situational state of mind wandering.

**Hypothesis 2.** The mind wandering scales (TSMWQ and MWQ) will correlate negatively with the mindfulness scale (MAAS), attentional control scale (ACS), and PANAS composite score, as has been found in similar previous validation studies (Mrazek et al., 2013; Sanchez & Naylor, 2018).

## 2.3. Method

### 2.3.1. Participants

Our two phases of questionnaires resulted in 341 respondents (including 267 women versus 74 men, Mage=40.19 years, SDage=9.81 years). All participants were employees of the healthcare industry who had completed a short professional training course that lasted between 4 and 21 h.

### 2.3.2. Material

The French translation of the MWQ (Mzarek et al., 2013) (see the translation procedure for more explanation), translated by two bilinguals, is a 5-item scale to measure participants' propensity to mind wander in their daily lives. Its five items must be rated on a Likert scale ranging from 1, "almost never," to 6, "almost all the time." The maximum score that participants can get on this scale is 36, indicating a high level of mind wandering in daily life (mind wandering trait).

The French translation of the TSMWQ (Sanchez & Naylor, 2018) (see translation procedure for more explanation) was done by two bilinguals of the is, as mentioned above, a scale adapted from the MWQ (Mzarek et al., 2013) and from the Memory and Attention Lapses subset of the Cognitive Failures Questionnaire (CFQ-MAL) (McVay & Kane, 2009). This five-item scale measures the participant's propensity to mind wander during a reading task. Its five items were modified to reference mind wandering in the task they had just completed by using the word "task" instead of "text." Thus, the scale consists of items ranging from 1, "almost never," to 6, "almost all the time." The maximum score that participants can get on this scale is 36, indicating a high level of mind wandering during the task.

### 2.3.3. Translation procedure

The translation procedure for both scales followed the one described by Vallerand (1989). Two bilingual individuals translated the two scales focused on the state of mind wandering and the trait of mind wandering, respectively. The translations were then compared, and the translators agreed on the final translations of the 10 items, the instructions, and the Likert scale for each measure. These versions of the translations were presented to the participants in our studies.

### 2.3.4. Procedure

The protocol took the following form: Before attending the training course, learners received a link containing the consent form, the mind wandering questionnaire, and the ACS questionnaire hosted on the SurveyMonkey tool. After their training, they responded online to the TSMWQ and a questionnaire on their training (type, duration) and socio-demographic information (gender, age). An open comment section was also provided to give them the opportunity to give feedback on the protocol, and they were given an email address so that they could contact the researchers.

**Data analysis.** We used the RStudio (version 2023.03.0 +386) software for the data analysis. No missing data were observed in this sample, so no procedure for replacing missing data was required.

A confirmatory factor analysis was conducted to validate the internal structure of the two scales. Several criteria were established to assess the goodness of fit of the observed model to the theoretical model: the $Chi^2$ must have been insignificant, the ratio of degrees of freedom must have been greater than 0, the comparative fit index (CFI) indicator must have been greater than .95, the Tucker-Lewis index (TLI) must have been greater than .95, and the root mean square error of approximation (RMSEA) and standardized root mean residual (SRMR) indicators must have been less than .06 (Bonneville-Roussy et al., 2022).

<table><caption>**Table 1**
Model-fitting criteria and theoretical thresholds.</caption><thead><tr><th></th><th>χ<sup>2</sup></th><th>χ<sup>2</sup>/ddl</th><th>CFI</th><th>TLI</th><th>SRMR</th><th>RMSEA</th></tr></thead><tbody><tr><td>Model 1</td><td>67.7</td><td>1.99</td><td>0.962</td><td>0.949</td><td>0.0395</td><td>0.0539</td></tr><tr><td>Second-order factor model</td><td>67.7</td><td>2.05</td><td>0.960</td><td>0.946</td><td>0.0395</td><td>0.0555</td></tr><tr><td>Thresholds</td><td></td><td>5</td><td>0,9</td><td>0,9</td><td>Near 0</td><td>0.08</td></tr></tbody></table>
## 2.4. Results

The TSMWQ and MWQ scales appeared to have satisfactory internal consistency (respectively, αTSMWQ=.71 and αMWQ=.73). The results of the confirmatory factor analysis indicated a significant $Chi^2$ ($\chi$ (34, N = 341) = 67.7, *p* < .001). While the $Chi^2$ is very sensitive to sample size (Hu & Bentler, 1999), the other fit indicators that are less sensitive to sample size showed a good fit of our data to the theoretical two-factor model presupposed for our mind wandering scales. The fit criteria and their corresponding thresholds are listed in Table 1.

To explore whether a more general factor of mind wandering existed, structural equation modeling was carried out to test a second-order factor model.

As shown in Fig. 1, the fit indicators were excellent. The inclusion of a second-order factor did not affect the structure of the model observed by the confirmatory factor analysis. These results confirmed that the items depended on two distinct latent variables, which were themselves dependent on a second-order latent factor.

## 2.5. Discussion

The aim of this study was to confirm the structural validity of our two mind wandering scales. Thus, it was expected that the items would fall under two distinct latent variables: a mind wandering variable as a trait and a wandering of thought variable as a state.

The results obtained indicate that the observed data differed from the theoretical model by following the $Chi^2$ indicator. However, as this indicator is very strongly linked to the size of the sample, it would have been inappropriate to accept or reject a model based solely on this finding (Bonneville-Roussy et al., 2022). Since the other indicators demonstrated a good fit between our data and the theoretical model, the results suggest that our two mind wandering scales were well distinguished into two distinct latent variables, enabling us to validate the structure of the scales. Moreover, this first study demonstrated that, although the scales depended on two distinct latent variables, adding a second-order factor underscored the value of considering these two scales as two parts of a single measure of mind wandering.

Our results replicated those observed in the original article, validating the Mind Wandering Scale (Mrazek et al., 2013) and aligning with the findings obtained in other validations of this scale with non-English-speaking populations (Peloso et al., 2024). No study has explored the factor structure of the task-specific mind wandering scale. Our results validate a good factorial structure of our items (Sanchez & Naylor, 2018).

In conclusion, the two mind wandering scales were well distinguished into two distinct factors, which remain linked. This result supports the validity of a unique mind wandering tool, distinguishing individual and situational factors of mind wandering. On the other hand, it highlights the importance of distinguishing between the mind wandering state and the mind wandering trait. In fact, despite being linked, the two constructs appear to belong to two distinct latent variables. This distinction is not sufficiently present in

<figure><img src="image_2.png" /><figcaption>Fig. 1. Structural equation model (Standardized Residuals).</figcaption></figure>
the literature on mind wandering, so the validation of tools that can clearly distinguish the two constructs was necessary (Bonifacci et al., 2022). It was also important to test the convergent and predictive validity of the two scales, which was the subject of the following study.

# 3. Study 2: Convergent and predictive validity of the mind wandering questionnaire – laboratory settings

## 3.1. Introduction

The objective of this second study is to test the convergent and predictive validity of the two scales of mind wandering, which have previously been validated from a structural point of view. Given the nature of mind wandering, it should be negatively related to performance during tasks requiring executive control or sustained attention. To verify this, the Sustain Attention to Response Task (SART), is a go/no-go task with rare targets, has been developed. This task is used to measure sustained attention and could, therefore, be relevant to study the effect of mind wandering on sustained attention.

Results suggest that measures of mind wandering are negatively related to performance on the SART, specifically regarding response times (Stawarczyk et al., 2020), error rate (Cheyne et al., 2009; Stawarczyk et al., 2020; Vinski & Watter, 2013), and sometimes the response time variability indicator (Vinski & Watter, 2013; Stawarczyk et al., 2020). Therefore, the SART was used to identify predictive validity for the MWQ and TSMWQ scales.

## 3.2. Hypotheses

**Hypothesis 1.** Links identified in Study 1 between the two mind-wandering measures and other scales measuring convergent validity will be replicated

**Hypothesis 2.** The two mind wandering measures will be positively correlated. The mind wandering scales (measured before the task for the MWQ and after the task for the TSMWQ) will correlate positively with the task-unrelated thought score measured with the probe caught (measured during the task).

**Hypothesis 3.** Scores on mind wandering measures (for both scales and the probe-caught method) will correlate with SART performance scores, positively with the error rates, and negatively with the response times.

## 3.3. Method

### 3.3.1. Participants

Ninety-two undergraduate students in psychology participated in this study ($M_{age} = 19.8$, $SD_{age} = 4.76$, 75 women). They were recruited through an internal university platform and received course credit for their participation. The present study received approval from the university's ethics committee (N° 2021-11-03). We report how we determined our sample size, all data exclusions, all manipulations, and all measures below (Simmons et al., 2012).

The power analysis on G\*Power (v. 3.1.9.7, Faul et al., 2007) testing a correlation analysis with one group revealed that a total sample size of 67 was required to achieve the standard 80 % power at a.05 alpha level ($\alpha=.05$) with a medium effect ($\rho=0.3$). The 92 participants thus exceeded this threshold.

### 3.3.2. Material

Questionnaires. Five questionnaires were used in this study to assess different dimensions of the participants' mental state before and during the experiment. The first, the French-validated version of the Mindfulness Attention Awareness Scale (MAAS) (Brown & Ryan, 2003; validated in French by Csillik et al., 2010; $\alpha=.71$), is a mindfulness assessment scale that provides a self-report of mindfulness during activities of daily living. It consists of 15 items rated on a 6-point Likert scale ranging from 1 = almost always to 6 = almost never.

The French-validated version of the Positive Affect and Negative Affect Schedule (PANAS) (Watson et al., 1988; validated in French by Gaudreau et al., 2006; $\alpha_{PAS}=.82$; $\alpha_{NAS}=.85$) is composed of 20 adjectives describing feelings or emotions. The participant must indicate how they feel at present on a scale of 1–5 (very little, little, moderate, a lot, very much). This scale calculates a positive and a negative emotion score (10 items per valence score). As in a study by Mzarek et al. (2013), a composite score was calculated on this scale by subtracting the negative affect score from the positive affect score.

The French-validated version of the Attentional Control Scale (ACS) (Derryberry & Reed, 2002; validated in French by Blekic et al., 2019) is a 20-item scale measuring participants' perceived level of attentional control in their daily lives. The participant indicates how often they feel each item happens in their daily life on a 4-point Likert scale (from 1 = almost never to 4 = always). The ACS provides an overall attentional control score ($\alpha=.79$), a concentration score ($\alpha=.79$), and a flexibility score ($\alpha=.66$).

Lastly, we also used a French translation of the MWQ (Mzarek et al., 2013; $\alpha_{MWQ}=$.74) and a French translation of the TSMWQ (Sanchez & Naylor, 2018; $\alpha_{TSMWQ}=$.70). Both of these scales were the same as those used in Study 1a.

**Sustained Attention to Response Task.**

As noted above, SART is a go/no-go task measuring laps of sustained attention (Robertson et al., 1997). In this task, a number was
presented in the center of the screen. The participant had to press the space bar whenever a number appeared on the screen, except when the number was a 3. Participants completed 18 practice trials and then 243 test trials. The appearance of one number corresponded to one trial. Each trial was organized as follows: The number appeared for 250 ms and then was replaced by a 900 ms duration mask composed of an X presented within a circle. If the participant did not answer, it switched to another digit. During the training, participants were given feedback on their performance, but feedback was never given during the experimental trial. Participants' success rate and response times were recorded. The task was piloted, and data were recorded with E-prime3© software.

**Probe-Caught method.** Participants were probed four times during the SART. A black screen with a prompt appeared every 60 trials (on average after 79 s) asking participants to report their thoughts. (For more detailed instructions, see Appendix A.) Participants were told to answer as honestly and spontaneously as possible.

### 3.3.3. Procedure

Testing took place collectively (up to four participants per session) in a room with several computers at the university. First, participants were welcomed and signed a consent form. The experimenter then explained the instructions, specifically the detection of mind wandering. Immediately after receiving their instructions, participants completed the ACS, MAAS, MWQ, and PANAS French questionnaires. They completed 18 training trials with feedback from the SART, during which the experimenter ensured that participants had understood the instructions. Participants then completed 243 test trials, including the probe-caught method. Finally, they completed the French version of the TSMWQ and the sociodemographic questionnaire (about age, gender, and native language) and were then debriefed and thanked. The session lasted around 45 min.

**Data analysis.** The data analyzed in this study were the scores on the six scales, the task-unrelated thoughts (TUT) measured by the probe-caught method, and the SART performance scores (error rate on no-go trials, response times on go trials, and response time coefficient variability (RTCV)). The TUT score represented the percentage of times participants responded that their attention was focused on non-task-related thoughts during the probe-caught measures. The RTCV was calculated by dividing the standard deviation of response times by the mean response time (Cheyne et al., 2009). Because of data non-normality (SART error rate and SART RTCV), the data were analyzed using non-parametric Spearman's correlations. The significance level was *p* < .05.

## 3.4. Results

### 3.4.1. Hypothesis 1: Correlation between the dimension of the mind wandering questionnaire and other validation scales

The score on the MAAS correlated weakly and negatively with the TSMWQ score. The correlation between the MAAS and MWQ scores was negative and moderate. The composite PANAS score correlated moderately and negatively with both the TSMWQ and MWQ scores. Lastly, the score on the ACS correlated negatively and moderately with the scores on the TSMWQ and MWQ. All correlations and descriptive statistics can be found in Table 2.

### 3.4.2. Hypothesis 2: Correlation between mind wandering measures

The MWQ score correlated positively and strongly with the TSMWQ score. In addition, the TSMWQ score correlated positively and strongly with the probe-caught measure, while the MWQ score correlated positively and moderately with the probe-caught measure.

<caption>**Table 2**</caption>

Correlations among mind wandering measures, performance measures, and other scales (n = 92).

<table><thead><tr><th>Variable</th><th>MWQ<sup>a</sup><br>1</th><th>TSMWQ<sup>b</sup><br>2</th><th>TUT<sup>c</sup><br>3</th><th>PANAS<sup>d</sup><br>4</th><th>ACS<sup>e</sup><br>5</th><th>MAAS<sup>f</sup><br>6</th><th>SART<sup>g</sup> RT<sup>h</sup> Go<br>7</th><th>SART<sup>g</sup> Error Rate<br>8</th><th>SART<sup>g</sup> RTCV<sup>i</sup><br>9</th></tr></thead><tbody><tr><td>1</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>2</td><td>0.54***</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>3</td><td>0.41***</td><td>0.67***</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>4</td><td>-0.38***</td><td>-0.36***</td><td>-0.19</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>5</td><td>-0.50***</td><td>-0.32**</td><td>-0.16</td><td>0.470***</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>6</td><td>-0.39***</td><td>-0.28**</td><td>-0.21*</td><td>0.33**</td><td>0.20</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>7</td><td>-0.20</td><td>-0.14</td><td>0.10</td><td>0.13</td><td>0.15</td><td>0.08</td><td>—</td><td>—</td><td>—</td></tr><tr><td>8</td><td>0.10</td><td>0.08</td><td>-0.15</td><td>0.01</td><td>-0.15</td><td>-0.01</td><td>-0.49***</td><td>—</td><td>—</td></tr><tr><td>9</td><td>-0.14</td><td>0.03</td><td>0.15</td><td>0.02</td><td>0.14</td><td>0.08</td><td>-0.00</td><td>-0.02</td><td>—</td></tr><tr><td>Mean</td><td>18.3</td><td>14.8</td><td>0.221</td><td>11.1</td><td>49.7</td><td>51.8</td><td>319</td><td>0.019</td><td>0.485</td></tr><tr><td>Median</td><td>19.0</td><td>14.0</td><td>0.00</td><td>12.0</td><td>50.0</td><td>52.0</td><td>303</td><td>0.009</td><td>0.452</td></tr><tr><td>SD</td><td>4.61</td><td>4.73</td><td>0.267</td><td>9.19</td><td>8.26</td><td>9.70</td><td>81.7</td><td>0.027</td><td>0.122</td></tr><tr><td>MinSPR<sup>j</sup></td><td>5</td><td>5</td><td>0</td><td>-50</td><td>20</td><td>15</td><td>0</td><td>0</td><td>0</td></tr><tr><td>MaxSPR<sup>k</sup></td><td>36</td><td>36</td><td>1</td><td>50</td><td>80</td><td>90</td><td>1150</td><td>1</td><td>1</td></tr><tr><td>Skewness</td><td>0.10</td><td>0.22</td><td>0.82</td><td>-0.73</td><td>-0.05</td><td>-0.43</td><td>0.82</td><td>3.26</td><td>1.59</td></tr><tr><td>Kurtosis</td><td>-0.50</td><td>0.09</td><td>-0.53</td><td>0.63</td><td>0.02</td><td>0.79</td><td>0.01</td><td>15.5</td><td>2.48</td></tr></tbody></table>

NB: The correlations presented are Spearman correlations. <sup>a</sup> MWQ: Mind Wandering Questionnaire. <sup>b</sup> TSMWQ: Task-Specific Mind Wandering Questionnaire. <sup>c</sup> TUT: Task-unrelated thoughts. <sup>d</sup> PANAS: Positive and Negative Affective States. <sup>e</sup> ACS: Attentional Control Scale. <sup>f</sup> MAAS: Mindfulness Attention Awareness Scale. <sup>g</sup> SART: Sustained Attention to Response Task. <sup>h</sup> RT: Response Time. <sup>i</sup> RTCV: Response Time Coefficient Variability. <sup>j</sup> MinSPR: Minimum Score Possible to Reach. <sup>k</sup> MaxSPR: Maximum Score Possible to Reach. *p* < .05; **p* < .01; ***p* < .001.
### 3.4.3. Hypothesis 3: Correlations between mind wandering measures and SART performance

Response times on go trials did not correlate with either the TSMWQ or MWQ scores. Additionally, the error rate did not correlate with the TSMWQ or MWQ scores. Finally, the RTCV also did not correlate with the TSMWQ or MWQ scores. In sum, none of the SART measurements correlated with the mind wandering scores.

## 3.5. Discussion

This study aimed to validate the French translation of two mind wandering scales: the MWQ and the TSMWQ. The validity of these two scales was tested using several indicators: convergent validity through the scales used in the scale validation articles (Mzarek et al., 2013; Sanchez & Naylor, 2018) and predictive validity through probe-caught measures and attentional task performances. The Cronbach's alphas of our two scales were satisfactory, highlighting good internal consistency (Cronbach, 1951).

Regarding the construct validity obtained from the correlations with the scales and correlation with TUT scores, we found the expected correlations between the MWQ and TSMWQ, between the MWQ and ACS, between the MWQ and MAAS, and between the TSMWQ and the probe-caught measure. These results are consistent with validation studies of the English versions of the scales (Mzarek et al., 2013; Sanchez & Naylor, 2018). Furthermore, both mind wandering scales correlated positively with reported TUT scores. This finding suggests that the mind wandering scales measured the correct construct (Weinstein, 2018).

However, none of the mind wandering measures correlated with SART performance regarding either response times or success rates. These results did not support the findings of Cheyne et al. (2009), with this difference potentially being explained by the use of a different mind wandering scale. The scale used in Cheyne et al. (2009) was the Attention-Related Cognitive Error Scale (ARCES). This scale is not specific to mind wandering reports but is instead used to measure the frequency of everyday cognitive failures caused by attention lapses. The other studies exploring the relationship between mind wandering and SART performance used a modified form of the SART in which the interstimulus interval was lengthened to induce mind wandering during the task (Jubera-Garcia et al., 2020; Smallwood et al., 2006; Stawarczyk et al., 2020; Vinski & Watter, 2013). It should be noted, however, that measures of mind wandering with the probe-caught method also did not correlate with performance on the SART.

In conclusion, the two primary scales measured the trait of mind wandering (for the MWQ) and the state of mind wandering (for the TSMWQ). Notably, though, our results showed a lower rate of state mind wandering than has been commonly observed in the literature (e.g., around 40%; see Kane et al., 2017; Risko et al., 2012; Spuznar et al., 2013). As such, perhaps the original SART task was not long enough (with a maximum time of 4 min and 39 s) to observe relationships with self-reported measures of mind wandering. To dig further into the topics at hand, we sought to examine the impact of mind wandering in a more ecological context of sustained attention use: learning.

# 4. Study 3: How do interest and mind wandering interact and affect learning? An exploratory study

## 4.1. Introduction

This second experiment focused on the impact of individual and situational characteristics of interest and mind wandering in a multimedia learning context. To investigate this topic, the researchers first questioned a method of inducing mind wandering through a four-minute writing task, drawing on both the content manipulation of mind wandering thoughts and their tendency to be naturally temporally oriented (Kopp et al., 2015; McVay & Kane, 2013). Experimentally inducing mind wandering allowed us to study in more detail the link between mind wandering and interest. Indeed, this approach enabled us to increase the number of episodes of mind wandering, which is necessary to be able to compare situations with and without mind wandering.

### 4.1.1. Inducing mind wandering

Inducing mind wandering is a crucial challenge in research, as mind wandering often stems from personal concerns or worries. McVay and Kane (2013) demonstrated that personalizing tasks, such as the SART, with words tied to participants' concerns could increase reports of mind wandering. Similarly, Kopp et al. (2015) found that asking participants to think about upcoming plans before a reading task led to more TUTs than neutral conditions did. However, Stawarczyk et al. (2011) found no increase in mind wandering from a to-do list exercise, though they observed more future-oriented thoughts. Combined, these studies have demonstrated how current concerns, and future planning can trigger mind wandering, which tends to focus on future or past events, and have shown the valuable role of mind wandering in processing people's concerns, particularly in planning.

For us, the remaining question was whether the temporal focus of thoughts would impact mind wandering or whether participants' concerns would increase reports of mind wandering. However, studies that have explored this temporal focus have most often focused on a plan to be completed by participants. To this end, this experiment explored a technique for inducing mind wandering by comparing three conditions of orientation at the time of the instruction: past, future, and neutral. The aim was to increase the number of episodes of mind wandering, thus enabling a more detailed study of its relationship with individual and situational factors of interest.

### 4.1.2. Individual and situational factors of interest and mind wandering

As established in the general introduction, interest plays a crucial role in learning by helping learners connect with the content and stay engaged, positively affecting their academic performance (Kpolovie et al., 2014; Ernawati et al., 2022). Interest can be both individual and situational. Significantly, fostering situational interest through active pedagogies, such as problem-based learning, can
enhance individual interest (Hidi & Renninger, 2006; Rotgans & Schmidt, 2017).

A crucial area to explore concerns the link between interest and mind wandering in learning contexts. Because mind wandering mediates individual characteristics (like motivation and interest) and learning performance (Kane et al., 2017, 2021), studies have shown that thematic interest reduces mind wandering and subsequently improves comprehension and learning outcomes (Unsworth & McMillan, 2013; Soemer & Schiefele, 2019). Mind wandering could play a mediating role in the relationship between individual interest and situational interest by influencing the attentional mechanisms involved in learning tasks. According to the interest development model proposed by Hidi and Renninger (2006), this process relies in particular on the activation and maintenance of attention. However, several studies (McDaniel et al., 2000; Shirey & Reynolds, 1988) show that interest can promote more automatic, flexible, and cognitively less costly attention. Thus, by directly influencing the quality and stability of attention, mind wandering could explain why individual interest does not always translate into situational interest. It could therefore act as a mediator in the attentional dynamics of interest development, modulating the transition between its different phases and influencing the consolidation of lasting interest. Although some research has explored the interaction between interest and mind wandering, the effects of this interaction on learning remain unclear.

Therefore, a deeper study of the relationship between interest, mind wandering, and learning is essential. This study aimed to address this gap by clarifying how various factors interact, and influence learning outcomes. To this end, it distinguished between the effects of individual characteristics (like individual interest) and situational characteristics (such as situational interest) and the role of mind wandering—both as a trait and as a temporary state—on learning. To our knowledge, no prior study has specifically explored this dimension.

The study followed the following steps: participants were asked to complete questionnaires about their individual characteristics and were then randomly assigned to one of three induction conditions: future, neutral, or past. After this, they were asked to watch a learning module during which mind wandering measurements were taken. Lastly, they completed questionnaires on their learning and situational characteristics.

## 4.2. Hypotheses

### 4.2.1. Hypothesis 1: Mind wandering induction

One of the aims of this experiment was to explore mind wandering induction through a four-minute short writing task inspired by emotion-induction tasks. Because mind wandering thoughts tend to have a solid temporal polarity (Smallwood et al., 2009; Stawarczyk et al., 2011), we expected that participants in the write-to-future condition would have higher mind wandering scores during the task than participants in the write-to-past condition, who we thought would have higher scores than participants in the control condition. Additionally, we posited that a higher rate of a mind wandering state would be associated with lower learning scores.

### 4.2.2. Hypothesis 2: Links between mind wandering and interest

We predicted that mind wandering as a state would mediate the link between the trait of mind wandering and situational interest. We furthermore hypothesized that the mind wandering state would also be a partial mediator of the link between individual interest and situational interest (Kane et al., 2021).

### 4.2.3. Hypothesis 3. Effects of the mind wandering-interest link on learning

Additionally, we assumed that situational interest and individual interest would correlate positively with learning scores. Conversely, we predicted that the trait mind of wandering and the state of mind wandering scores would correlate negatively with learning scores. Lastly, we theorized that variance in learning scores would be predicted by scores on individual characteristic scales (the trait of mind wandering and individual interest) and scores on situational scales (situational mind wandering and situational interest) (Kane et al., 2017, 2021; Soemer & Schiefele, 2019; Unsworth & McMillan, 2013).

## 4.3. Method

### 4.3.1. Participants

The power analysis on G\*Power (v. 3.1.9.7, Faul et al., 2007), testing an ANCOVA with three groups (induction: past vs neutral vs future) and two covariates (individual interest and mind wandering trait) for a two numerator, revealed that a total sample size of 158 was required to achieve the standard 80 % power at a.05 alpha level (α = .05) with a medium effect (Cohen's f = .25). Our sample included 165 undergraduate psychology students (148 women and 17 men, range 18–51 years; M\_age = 18.86; SD\_age = 3.13). All participants were recruited through an internal university platform and received course credit for their participation. None of them had participated in any other study in this research. All participants completed the entire study and were native French speakers. This study received approval from the university's ethics committee (N° 2021-11-03).

### 4.3.2. Material

#### Learning video.

The learning video was 16.5 min long. The topic was an introduction to descriptive statistics: from indicator calculations to the interpretation of the linear correlation coefficient. The course format was a PowerPoint, with sound and animations allowing information to appear as the speech progressed.

**Questionnaires.** As with Study 1, and Study 2 six French questionnaires were used: the MAAS (α = .85), the MWQ (α = .84), the
TSMWQ (α =.82), the STAI-Y (α =.92), and the PANAS (αPAS =.78; αNAS =.87). Another questionnaire, a scale adapted from the Serious Games Interest Scale (IS2G) (Chainon, 2017), was employed to measure the learner's interest in the video. This scale measured interest along three dimensions of interest: individual interest (α =.90), activated situational interest (α =.92), and maintained situational interest (α =.85). It comprised 16 items (i.e., four items in each dimension) to be rated on a 7-point Likert scale from "strongly disagree" to "very strongly agree." In analyses, a combined situational interest score (α =.90) was used (i.e., the sum of activated situational interest and maintained situational interest).

**Statistics learning questionnaire.** Two questionnaires were constructed: one for prior knowledge and one for learning post-video. The prior-knowledge questionnaire consisted of five questions built on general statistical knowledge studied in the college mathematics curriculum and five questions built on information in the lesson. The learning questionnaire consisted of five recall questions from the prior-knowledge questionnaire, three comprehension questions, and five transfer questions. All questions were open-ended and required a short answer.

First, 20% of the scores on the learning questionnaire were scored by two researchers; the agreement rate between the two scores was 96%. Thus, the rest of the learning questionnaire was rated only by one person (the experimenter, the first author). A correct answer was scored as 1 point, a wrong answer as -1 point, and no answer as 0 points.

**Mind wandering induction.** The mind wandering induction was a four-minute writing task with three different conditions: a future-oriented condition (planning an event that will take place in the future), a past-oriented condition (telling a memory from the past), and a control condition (describing a typical day).

**Probe-Caught measure.** Four probe-caught measurements of mind wandering took place during the video (at 2:15, 7:02, 12:05, and 16:16). The probe-caught instruction was the same as in Study 1b. On a sheet of paper, participants answered the number corresponding to their current state at the time of the measurement.

### 4.3.3. Procedure

The tests took place at the university and were collectively administered in groups of up to six participants. Participants arrived and filled in the consent form, and the first phase of the questionnaire, composed of the MAAS, the STAI-Y, the PANAS, the IS2G individual interest dimension, the MWQ, and the prior-knowledge assessment questionnaire. Afterwards, they had to complete the writing task according to the experimental group to which they were randomly assigned. They then watched the learning video, during which four probe-caught measures were performed. Participants were not allowed to take notes during the viewing of the video. After watching the video, the participants had to complete a questionnaire phase again, this time consisting of the TSMWQ, the IS2G situational interest dimensions, the learning assessment questionnaire, and a socio-demographic questionnaire (asking age, gender, and native language). Finally, participants were debriefed and thanked for their participation. The session lasted around one hour.

**Data analysis.** The data analyzed in this study were the scores on the collected scales (i.e., the MAAS, STAI-Y, PANAS, MWQ, IS2G (both individual and situational interest), prior-knowledge questionnaire, TSMWQ, and socio-demographic questionnaire), the mind wandering scores in the probe-caught measure, and the learning scores on three indicators: learning (comprised of the same five questions from the learning questionnaire), comprehension, and transfer. As the skewness and kurtosis indicators for the different

<table><caption>**Table 3**
Matrix of correlations between mind wandering, interest, and learning measures (n = 165).</caption><thead><tr><th>Variable</th><th>MWQ<sup>a</sup><br>1</th><th>TSMWQ<sup>b</sup><br>2</th><th>TUT<sup>c</sup><br>3</th><th>II<sup>d</sup><br>4</th><th>SI<sup>e</sup><br>5</th><th>Recall<br>6</th><th>Comprehension<br>7</th><th>Transfer<br>8</th></tr></thead><tbody><tr><td>1</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>2</td><td>0.35<sup>***</sup></td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>3</td><td>0.22<sup>**</sup></td><td>0.70<sup>***</sup></td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>4</td><td>-0.07</td><td>-0.23<sup>**</sup></td><td>-0.12<sup>*</sup></td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>5</td><td>-0.14</td><td>-0.72<sup>***</sup></td><td>-0.51<sup>***</sup></td><td>0.41<sup>***</sup></td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>6</td><td>-0.05</td><td>-0.33<sup>***</sup></td><td>-0.22<sup>**</sup></td><td>-0.07</td><td>0.28<sup>***</sup></td><td>—</td><td>—</td><td>—</td></tr><tr><td>7</td><td>-0.05</td><td>-0.04</td><td>-0.02</td><td>0.01</td><td>0.11</td><td>0.11</td><td>—</td><td>—</td></tr><tr><td>8</td><td>0.02</td><td>-0.21<sup>**</sup></td><td>-0.14</td><td>0.10</td><td>0.19<sup>*</sup></td><td>0.26<sup>***</sup></td><td>0.31<sup>***</sup></td><td>—</td></tr><tr><td>Mean</td><td>19.42</td><td>13.56</td><td>0.26</td><td>12.15</td><td>29.98</td><td>0.95</td><td>-0.41</td><td>0.35</td></tr><tr><td>Median</td><td>20</td><td>13</td><td>0.25</td><td>12</td><td>30</td><td>1</td><td>-1</td><td>0</td></tr><tr><td>SD</td><td>5.73</td><td>4.83</td><td>0.24</td><td>5.67</td><td>10.57</td><td>2.10</td><td>1.31</td><td>1.75</td></tr><tr><td>Skewness</td><td>-0.14</td><td>0.07</td><td>0.79</td><td>0.27</td><td>0.16</td><td>0.08</td><td>0.17</td><td>0.21</td></tr><tr><td>Kurtosis</td><td>-0.47</td><td>-0.58</td><td>0.21</td><td>-0.89</td><td>-0.50</td><td>-0.44</td><td>-0.04</td><td>0.10</td></tr><tr><td>MinSPR<sup>f</sup></td><td>5</td><td>5</td><td>0</td><td>6</td><td>8</td><td>-5</td><td>-3</td><td>-5</td></tr><tr><td>MaxSPR<sup>g</sup></td><td>36</td><td>36</td><td>1</td><td>35</td><td>56</td><td>5</td><td>3</td><td>5</td></tr></tbody></table>

Note. This table contains Pearson correlations.

<sup>a</sup>MWQ: Mind Wandering Questionnaire.

<sup>b</sup>TSMWQ: Task-Specific Mind Wandering Questionnaire.

<sup>c</sup>TUT: Task-Unrelated Thoughts.

<sup>d</sup>II: Individual Interest.

<sup>e</sup>SI: Situational Interest.

<sup>f</sup>MinSPR: Minimum Score Possible to Reach.

<sup>g</sup>MaxSPR: Maximum Score Possible to Reach.

*p* < .05; **p* < .01; ***p* < .001.
variables ranged between -1 and 1, our data were considered to be normally distributed. Consequently, we used parametric tests. The significance level was *p* < .05.

To test the predicted model, we first analyzed the correlation between the trait and the state of mind wandering—namely, the link between individual interest and situational interest and the relationship between mind wandering and interest. These links were tested using multiple regression analysis.

## 4.4. Results

### 4.4.1. Descriptive statistics

The results replicated the links observed in Study 2. More details on the preliminary replication analyses can be found in Appendix B.

### 4.4.2. Hypothesis 1: Mind wandering induction

Induction had no effect on mind wandering scale scores ($\chi^2$ (2, N = 165) = 3.96, *p* = .14 or probe-caught measure scores ($\chi^2$ (2, N = 165) = 2.86, *p* = .24). Our results further revealed that induction had no effect on recall ($\chi^2$ (2, N = 165) = 4.36, *p* = .11), comprehension ($\chi^2$ (2, N = 165) = 1.27, *p* = .53), or transfer ($\chi^2$ (2, N = 165) = 1.20, *p* = .55). Means and standard deviations split by induction group appear in Table 4.

### 4.4.3. Hypothesis 2: Links between mind wandering and interest

#### Mind wandering measures with the TSMWQ.

The mediation analysis showed a significant direct effect between individual interest and situational interest (path a, $\beta$ = -.26, *p* < .001). Additionally, the results revealed significant effects of individual interest on the mind wandering state (path a, $\beta$ = -.23, *p* < .01) and of the mind wandering state on situational interest (path b, $\beta$ = -.66, *p* < .001). A representation of these relations is available in Fig. 2, image A. The mediation analysis showed that the mind wandering state's effect on the relationship between individual and situational interest was significant (path c, $\beta$ = .41, *p* < .001). In addition, the test for the indirect effect was also significant (*z* = 3.00, *p* < .01). Therefore, a partial mediation occurred between individual interest and situational interest via the state of mind wandering. As supposed in Hypothesis 2, our results indicate that the state of mind wandering is a partial mediator between individual interest and situational interest.

#### Mind wandering measured with the Probe-Caught method.

The mediation analysis showed a significant total effect between individual interest and situational interest (path c, $\beta$ = .33, *p* < .001). The results also revealed significant effects of individual interest on the mind wandering state (path a, $\beta$ = -.18, *p* < .05), and of the mind wandering state on situational interest (path b, $\beta$ = -.45, *p* < .001). A representation of these relations is available in Fig. 2, image B. The mediation analysis showed that the mind wandering state's effect on the relationship between individual interest and situational interest was significant (path c', $\beta$ = .08, *p* < .05), and the test for the indirect effect was also significant (*z* = 2.19, *p* < .05). Therefore, a partial mediation occurred between individual interest on situational interest via the state of mind wandering, even when it was measured with the probe-caught method. These analyses complement those carried out previously, as they highlight the fact that the results obtained were replicated whether mind wandering was measured via a questionnaire or probe-caught measures.

### 4.4.4. Hypothesis 3: Effects of the mind wandering-interest link on learning

This section covers the links between individual characteristics (i.e., individual interest and the trait of mind wandering), situational characteristics (i.e., situational interest and the state of mind wandering), and learning scores (i.e., recall, comprehension, and transfer) by using correlations and regressions (see Table 3 for all correlations).

Correlation analyses showed that situational interest correlated positively and significantly with recall and transfer, indicating that the more situational interest participants reported, the better their recall. No correlation was observed with comprehension. The results also revealed no correlation between individual interest and recall score, comprehension, or transfer.

<caption>Table 4</caption>

Mean (standard deviation in brackets) scores of measures of interest, mind wandering, and learning (n = 165) per induction group.

<table><thead><tr><th rowspan="2">Variable</th><th colspan="3">Induction</th></tr><tr><th>Future</th><th>Neutral</th><th>Past</th></tr></thead><tbody><tr><td>MWQ<sup>a</sup></td><td>19.52 (4.90)</td><td>19.27 (6.05)</td><td>19.49 (6.26)</td></tr><tr><td>TSMWQ<sup>b</sup></td><td>12.84 (4.98)</td><td>14.38 (4.57)</td><td>13.46 (4.89)</td></tr><tr><td>TUT<sup>c</sup></td><td>0.22 (0.25)</td><td>0.29 (0.23)</td><td>0.26 (0.23)</td></tr><tr><td>Recall</td><td>1.00 (1.88)</td><td>0.52 (2.17)</td><td>1.34 (2.18)</td></tr><tr><td>Comprehension</td><td>-0.48 (1.29)</td><td>-0.27 (1.33)</td><td>-0.49 (1.31)</td></tr><tr><td>Transfer</td><td>0.45 (1.87)</td><td>0.16 (1.68)</td><td>0.43 (1.70)</td></tr></tbody></table>

Note. This table contains scores that do not refer to the same scale. For the maximum score possible to reach for each scale, see Table 2. a MWQ: Mind Wandering Questionnaire. b TSMWQ: Task-Specific Mind Wandering Questionnaire. c TUT: Task-Unrelated Thoughts.
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Mediation analysis of mind wandering state on the individual-situational interest link, Note: Beta coefficients are standardized. A. Mind wandering state measured with the TSMWQ. B. Mind wandering state measured with the Probe-Caught method.</figcaption></figure>

Other significant correlations were found between the TSMWQ and recall and transfer. More precisely, the TSMWQ correlated negatively and significantly with both, indicating that the higher the score a participant reported on the TSMWQ, the lower their recall and transfer scores. No correlation was observed between the TSMWQ and comprehension. Additionally, the correlation analysis showed no correlation between the MWQ and recall, comprehension, or transfer.

Regarding mind wandering measurements performed via probe caught, mind wandering correlated negatively and significantly with recall. No other correlation was observed between mind wandering and comprehension or transfer.

Given the significant correlations described above, a multiple linear regression was used to examine whether the state of mind wandering and situational interest significantly predicted recall scores.

**Mind wandering measured with the TSMWQ.** The multiple regression model ($y = 1,97 + (-0.12)_{\text{TSMWQ}} + 0.02_{\text{IS}} + e$) was tested. Results showed that the state of mind wandering (measured with the TSMWQ) significantly predicted recall scores when controlling for situational interest ($\beta = -0.12, p < .05, 95 \% \text{ CI: } -0.21, -0.02$) and that, conversely, situational interest did not significantly predict recall scores when controlling for the state of mind wandering ($\beta = .02, ns$). The overall regression was statistically significant, $R^2 = .12, F(2162) = 10.3, p < .001$.

The interaction effect between situational interest and the state of mind wandering recall score was examined. The results showed no significant effect ($\beta = -0.003, ns$), indicating that the significant effect of the state of mind wandering on recall score when controlling for situational interest is independent of this interaction.

**Mind wandering measured with the Probe-Caught method:** The multiple regression model ($y = 0.33 + (-0.003)_{\text{TUT}} + 0.009_{\text{IS}} + e$) was tested. Results showed that the state of mind wandering (measured with the probe-caught method) significantly predicted recall scores when controlling for situational interest ($\beta = -0.003, p < .05, 95 \% \text{ CI: } -0.005, -0.001$) and that situational interest also significantly predicted recall scores when controlling for the state of mind wandering ($\beta = -0.009, p < .001, 95 \% \text{ CI: } 0.004, 0.013$). The overall regression was statistically significant, $R^2 = .22, F(2162) = 22.9, p < .001$.

The effect of the interaction between situational interest and the state of mind wandering on recall score was examined. The results showed no significant effect ($\beta = 6.66e-5, ns$), indicating that the significant effect of the state of mind wandering and situational interest on recall score is independent of this interaction.
# 5. General discussion

This research investigated the link between mind wandering and interest in a multimedia learning context. To do so, it explored the possibility of inducing mind wandering via a short writing task. The applied approach also enabled us to study the measurement of mind wandering by validating two mind wandering scales in French: a scale focused on mind wandering as a trait scale and a scale focused on mind wandering as a state. This study provides new insights into mind wandering and interest and their combined impact on learning. Indeed, it is one of the few studies to include a measure of individual interest and to compare both the probe-caught method and a retrospective measurement of mind wandering.

This research hypothesized that mind wandering could explain part of the link between individual and situational interest during a lecture and that situational interest and mind wandering could impact learning scores. To test this hypothesis, we conducted an experiment on mind wandering. Participants watched a video on descriptive statistics. During and after the viewing, they were questioned about their concentration level (measured as their mind wandering level). In order to maximize the probability of observing mind wandering, an attempt was explicitly made to induce mind wandering. Mind wandering was measured with a validated scale (Studies 1 and 2) and a probe-caught technique.

## 5.1. Mind wandering induction

Contrary to expectation, the prior task used here did not induce mind wandering. Indeed, our results show no significant difference between the group writing about a past, future, or neutral event on the different indicators of mind wandering used here (the probe-caught method and the TSMWQ). This lack of difference could stem from the task's length: With the writing task lasting only four minutes, participants may not have had time to think through the details of this event. Notably, time limits are not standard in emotion-induction studies, and they range from 4 to 12 min (Mills & D'Mello, 2014). Because of this failure to induce mind wandering, our results do not indicate any difference between our groups in this regard. However, it would have been interesting to observe whether the induction task impacted the content of thoughts during episodes of mind wandering, such as by using a 6-point probe-caught method rather than a 3-point one (Stawarczyk et al., 2011). On the other hand, it could be that participants did not write about things that concerned them during the writing task.

Relatedly, McVay and Kane (2013) highlighted that adapting the content of the task to activate topics that currently preoccupy participants increases the rate of mind wandering during the task. In the case of this study, because of concerns around not modifying the content of the learning task, the topic of the writing task was relatively free. Perhaps prompting participants to write about something that preoccupied them would have induced more mind wandering. Indeed, Kopp et al. (2015) showed that explicitly asking participants to plan a task could potentially induce mind wandering.

Moreover, the rate of mind wandering measured with the probe-caught method (between 22 % and 29 %) was lower than the rates found in other studies observing mind wandering in a virtual classroom context, with such rates being around 40 % (Kane et al., 2017; Risko et al., 2012; Spuznar et al., 2013). These results could be explained by the quantity of measurements or differences between the probe-caught techniques used. Another explanation could be that participants perceived the two tasks as distinct: When they had finished the writing task, they stopped thinking about it and moved on to the next task.

It is possible that mind wandering is too dynamic, unstable, and strongly influenced by individual and contextual factors to be effectively induced using a standardized task that is identical for all participants without altering the main task. In other words, the very nature of mind wandering—spontaneous, fluctuating, and deeply subjective—may make any attempt at experimental standardization difficult, if not illusory. That said, as the scientific literature on this topic is still emerging, further research is needed to assess the feasibility of such inductions and to understand better the conditions that promote or inhibit this phenomenon.

Indeed, if it were to be demonstrated that it is truly impossible to induce a state of mind wandering reliably, this impossibility would in itself be a meaningful result. It could offer valuable insights into the very nature of mind wandering and, more broadly, into the mechanisms of consciousness and spontaneous cognition.

## 5.2. Link between mind wandering and interest

The results found in Study 2 showed that mind wandering explains part of the link between individual and situational interest when watching an educational video. One explanation for this link centers on the cognitive dimension of interest (Hidi, 2001). Therefore, our results support the notion that the cognitive dimension of interest would tend to be attentional, replicating the link that Kane et al., (2017, 2021) found in their study. These results add to a literature that has thus far focused mainly on the affective component of interest (Renninger & Hidi, 2011). Further studies would be worthwhile to understand which attentional dimension (e.g., orientation, alert, or inhibition) comes into play in the development of interest over time. In addition, it should be noted that these links were replicated with the mind wandering measurement achieved via the probe-caught method.

## 5.3. Link between situational variables and learning

We expected that learning scores would be predicted by combining individual characteristic variables (individual interest and the mind wandering trait) and situational variables. The observed results on learning were mixed. In sum, individual characteristic variables did not predict learning scores when situational characteristics were present in the statistical model. In contrast, individual characteristics did predict the situational characteristics to which they were attached (i.e., situational interest for individual interest
and the state of mind wandering for the trait of mind wandering). Overall, therefore, our data suggest that individual characteristics had no direct impact on learning scores.

Concerning situational characteristics, only the recall score was predicted by the state of mind wandering and situational interest. This observation could be explained by the participants' low learning rates on transfer and comprehension tests. Consequently, the low variability observed in transfer and comprehension scores could explain the lack of relationship between the predictor and observed variables. This low variability could have several causes. Firstly, open-ended questions could be less engaging for participants. This effect could be due to the more difficult questions, particularly compared with multiple-choice questions, which provide an indexed recall of learned information. Secondly, participants did not have the opportunity to take notes during the learning situation. Thus, a controlled laboratory setting may not effectively engage learners in the same way a classroom might, especially given that the topic was often uninteresting to students of psychology. Future research on interest and mind wandering that accounts for note taking or different question types would shed light on these issues.

On the other hand, our results highlight that situational interest's impact on recall scores is linked with the impact that the state of mind wandering has on recall scores. This observation makes us wonder about the cognitive aspect of interest, as it seems that situational interest's impact on learning is mediated by the attentional focus learners achieve during the lecture. These results suggest that, in order to develop their situational interest, learners must be able to focus their attention on the learning situation, and reciprocally, situational interest might be helpful for maintaining attention in a learning situation. More research is still needed to better understand the reciprocal links between mind wandering and interest.

As intimated above, the learning situation is integral to the development of situational interest (and possibly individual interest later on). However, not every means of attracting attention during the lesson is effective for learning. For example, to capture the learner's attention, adding seductive details to the lesson may be tempting. Seductive details are defined as information that captures the learner's situational interest but is not helpful in understanding the learning content. While this approach may garner increased attention, capturing interest by adding seductive details would have an overall negative impact on learning, especially since it would promote cognitive overload (for a review, see Sundararajan & Adesope, 2020). Thus, situational interest would best benefit learning when activated and maintained by relevant learning content. In other words, it is necessary to attract and maintain attention to the relevant content of the lesson, not just to the lesson in general. To this end, interpolating several tests during the sequence would enhance learning and limit mind wandering (Spuznar et al., 2013).

## 5.4. Limitations

This study was limited because it did not consider the difference between intentional and unintentional mind wandering, a dimension of mind wandering that has become increasingly relevant in the mind wandering literature (Stawarczyk et al., 2011). Mind wandering, for instance, could be a strategy implemented during low-involvement situations. If so, when students are in such situations, they may be more prone to intentionally letting their mind wander. Future research should investigate the link between intentional and unintentional mind wandering and its relationship with interest. Additionally, the ability to be aware of one's attentional state requires hindsight and metacognition, and the state of mind wandering appears to be generally unconscious (Hidi & Renninger, 2006).

## 6. General conclusions

This study aimed to understand the relationship between mind wandering and interest during multimedia learning. It also investigated whether a brief writing task could experimentally induce mind wandering. To pursue this line of inquiry, we first analyzed two mind wandering scales that had been adapted to French, both of which showed good internal validity in both experiments (all Cronbach's alphas >.70). Concurrent validity was also satisfactory with other scales and with the probe-caught measure. Predictive validity, meanwhile, was insufficient regarding correlations between scales and SART scores. Nevertheless, the mind wandering scales were predictive of learning scores, which could be considered a form of predictive validity. Due to our somewhat mixed results, further investigations concerning links between SART scores and mind wandering still need to be done. Nevertheless, the validity of these scales in French presents a promising step forward in developing tools to measure mind wandering as a trait and as a state during learning situations. The linguistic conversion of such scales will undoubtedly make it easier to explore mind wandering in more ecological contexts and to distinguish between the effects of the trait and the state of mind wandering, and intentional and unintentional mind wandering.

Future studies should focus on the induction of mind wandering to provide a better understanding of this process. Further research is also needed to understand how to capture situational interest and reduce mind wandering so that recommendations can be made for designing more effective learning environments.

Our main results concern the mediation of the state of mind wandering between individual and situational interest and the joint role of the state of mind wandering and situational interest in their effect on learning scores (i.e., recall scores). Our results shed light on the cognitive dimension of interest and demonstrate a fundamental link between interest and attention in a multimedia learning context. However, it remains necessary to study these links experimentally to better understand the causal link between interest and attention. Furthermore, research detailing the cognitive and emotional impact of interest on learning, as well as the link between these aspects and attention or distraction, would provide a better understanding of which dimension of interest most significantly impacts learning over time. Furthermore, it would be worthwhile to identify which situational aspects might best promote attention, interest, and learning. Ultimately, future research should investigate which elements or tools within a learning situation are most effective in
helping learners remain focused and engaged with the relevant learning content.

## CRediT authorship contribution statement

**Louise Perche:** Writing – review & editing, Writing – original draft, Project administration, Methodology, Investigation, Formal analysis, Conceptualization.
**Nora Yennek:** Writing – review & editing, Resources, Funding acquisition, Conceptualization.
**Laure Léger:** Writing – review & editing, Supervision, Resources, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization.

## Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) used Deepl and Grammarly in order to improve language and readability, with caution. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication. Moreover, the final version of this manuscript has been proofreading by a professional English proofreading service.

## Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Louise Perche reports financial support was provided by Institut de Formation des Industries de Santé. Louise Perche reports financial support was provided by Agence Nationale de Recherche et Technologie (ANRT). Louise Perche reports a relationship with Agence Nationale de Recherche et Technologie (ANRT) that includes: funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgment

The authors thank Gabriela de Oliveira Gonçalves for her assistance with data collection, Léonie Messmer for her help in rating learning data, Fabien Fenouillet for helping with statistical analysis, Alexander Soemer for proofreading the first version of this article, and Agence Nationale de Recherche et Technologie (ANRT) for funding this research (grant number: CIFRE N° 2019/1327).

## Appendix A. Probe-Caught Instructions

Following instructions:

*Just now, what was your attention focused on?*

1. On the content of the task

2. On thoughts related to the task

3. On thoughts unrelated to the task.

Press the corresponding number on the numeric keypad.

## Appendix B. Preliminary analyses

**Replication of experiment 1.** Results revealed a large and negative correlation between the MAAS and MWQ and a small correlation with the TSMWQ. A small and negative correlation was found between the PANAS 1 and MWQ and between the PANAS 1 and TSMWQ. A medium positive correlation was found between the MWQ and TSMWQ scores. Finally, the results showed a small and positive correlation between the probe-caught method and the MWQ and a positive and strong correlation between the probe-caught method and the TSMWQ. Correlations between mind wandering, interest, and learning can be found in Table 2.

**Linking individual and situational characteristics of interest.** Results showed a significant, medium, and positive correlation between situational interest and individual interest. The analysis of individual interest on situational interest also revealed a significant effect, indicating that participants with high individual interest scores were more likely to report high situational interest scores, $\beta$ = .77, t(163) = 5.76, p < .001. The adjusted R² = .16, F(1163) = 33.2, p < .001.

**Linking individual and situational characteristics of mind wandering.** The regression analysis of the MWQ on TSMWQ revealed a significant effect, indicating that participants with a high level of mind wandering in their daily lives were more likely to report a high level of mind wandering during the task, $\beta$ = .29, t(163) = 4.69, p < .001. The adjusted R² = .11, F(1163) = 22, p < .001.

## Data availability

Data will be made available on request.
## References

Ainley, M., Hidi, S., & Berndorff, D. (2002). Interest, learning, and the psychological processes that mediate their relationship. *Journal of Educational Psychology, 94*(3), 545-561. https://doi.org/10.1037/0022-0663.94.3.545

Baird, B., Smallwood, J., Mrazek, M. D., Kam, J. W. Y., Franklin, M. S., & Schooler, J. W. (2012). Inspired by distraction: Mind wandering facilitates creative incubation. *Psychological Science, 23*(10), 1117-1122. https://doi.org/10.1177/0956797612446024

Blekic, W., Wauthia, E., Loureiro, I. S., Arachchige, K., & Lefebvre, L. (2019). Étude factorielle exploratoire de la version française de l'Échelle de Contrôle attentionnelle: liens avec les symptômes d'anxiété et de dépression [Exploratory factor analysis of the French version of the Attention Control Scale: Links with symptoms of anxiety and depression. *Revue Francophone Déleòtt Clinique Comportementale et Cognitive, 24*(3), 13-22.

Bonifacci, P., Viroli, C., Vassura, C., Colombini, E., & Desideri, L. (2022). The relationship between mind wandering and reading comprehension: A meta-analysis. *Psychonomic Bulletin and Review, 30*, 40-59. https://doi.org/10.3758/s13423-022-02141-w

Bonneville-Roussy, A., Fenouillet, F., & Morvan, Y. (2022). Introduction aux analyses par équations structurelles: Applications avec Mplus en psychologie et sciences sociales. [Introduction to structural equation analysis: Applications with Mplus in psychology and the social sciences.] Dunod.

Brown, K. W., & Ryan, R. M. (2003). The benefits of being present: Mindfulness and its role in psychological well-being. *Journal of Personality and Social Psychology, 84*(4), 822-848. https://doi.org/10.1037/0022-3514.84.4.822

Chainon, D. (2017). L'effet de l'expertise sur l'expérience-utilisateur dans le cadre de l'utilisation des Serious Games. [The effect of expertise on user experience in the context of serious games]. [These de doctorat, Paris 10]. https://www.theses.fr/2017PA100181.

Cheyne, J. A., Carriere, J. S. A., & Smilek, D. (2006). Absent-mindedness Lapses of conscious awareness and everyday cognitive failures. *Consciousness and Cognition, 15*(3), 578-592. https://doi.org/10.1016/j.concog.2005.11.009

Cheyne, J., Solman, G. J. F., Carriere, J. S. A., & Smilek, D. (2009). Anatomy of an error: A bidirectional state model of task engagement/disengagement and attention-related errors. *Cognition, 111*(1), 98-113. https://doi.org/10.1016/j.cognition.2008.12.009

Christoffo, K., Irving, Z. C., Fox, K. N., Spreng, R. N., & Andrews-Hanna, J. R. (2016). Mind-wandering as spontaneous thought: a dynamic framework. *Nature reviews neuroscience, 17*(11), 718-731.

Conrad, C., & Newman, A. (2021). Measuring mind wandering during online lectures assessed with EEG. *Frontiers in Human Neuroscience, 15*. https://doi.org/10.3389/fnhum.2021.697532

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. *Psychometrika, 16*(3), 297-334.

Csillik, A.S., Mahr, S., & Meyer, T. (2010). The mindful attention awareness scale (MAAS) French validation: Convergent and divergent validity. Paper presented at the Fourth Meeting of the Social Determination Theory Society, Ghent, Belgium.

Derryberry, D., & Reed, M. A. (2002). Anxiety-related attentional biases and their regulation by attentional control. *Journal of Abnormal Psychology, 111*(2), 225-236. https://doi.org/10.1037/0021-843X.111.2.225

Ernawati, M. D. W., Sudarmin, S., Asrial, A., Haryanto, H., Sanova, A., Kurniawan, D. A., & Azzahra, M. Z. (2022). The influence of student interest on student learning outcomes in science subjects. *Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education), 10*(4), 849-861.

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. *Behavior Research Methods, 39*(2), 175-191. https://doi.org/10.3758/BF03193146

Fenouillet, F., Chainon, D., Yennek, N., Masson, J., & Heutte, J. (2017). Relation entre l'intérêt et le bien-être au collège et au lycée. [Relationship between interest and well-being in middle school and high school]. *Enfance, 2017*(01), 81-103. https://doi.org/10.4074/S0013754517001069

Franklin, M. S., Broadway, J. M., Mrazek, M. D., Smallwood, J., & Schooler, J. W. (2013). Window to the wandering mind: Pupilometry of spontaneous thought while reading. *The Quarterly Journal of Experimental Psychology, 66*(12), 2289-2294. https://doi.org/10.1080/17470218.2013.858170

Fulmer, S. M., D'Mello, S. K., Strain, A., & Graesser, A. C. (2015). Interest-based text preference moderates the effect of text difficulty on engagement and learning. *Contemporary Educational Psychology, 41*, 98-110. https://doi.org/10.1016/j.cedpsych.2014.12.005

Gaudreau, P., Sanchez, X., & Blondin, J.-P. (2006). Positive and negative affective states in a performance-related setting: Testing the factorial structure of the PANAS across two samples of French-Canadian participants. *European Journal of Psychological Assessment, 22*(4), 240-249.

Gericic, C., Soemer, A., & Schiefele, U. (2025). Attentional mediators of the effect of interest on comprehension. *Learning and Instruction, 95*, Article 102010. https://doi.org/10.1016/j.learinstruc.2024.102010

Hidi, S. (1990). Interest and its contribution as a mental resource for learning. *Review of Educational Research, 60*(4), 549-571.

Hidi, S. (2001). Interest, Reading, and learning: Theoretical and practical considerations. *Educational Psychology Review, 13*(3), 191-209.

Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. *Educational Psychologist, 41*(2), 111-127.

Hollis, R. B., & Was, C. A. (2016). Mind wandering, control failures, and social media distractions in online learning. *Learning and Instruction, 42*, 104-112. https://doi.org/10.1016/j.learinstruc.2016.01.007

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives. *Structural Equation Modeling: A Multidisciplinary Journal, 6*(1), 1-55. https://doi.org/10.1080/10705519909540118

Irving, Z. C., McGrath, C., Flynn, L., Glasser, A., & Mills, C. (2022). The shower effect: Mind wandering facilitates creative incubation during moderately engaging activities. *Psychology of Aesthetics, Creativity, and the Arts, 18*(6), 1096-1107. https://doi.org/10.1037/aca0000516

Jubera-García, E., Gevers, W., & Van Opstal, F. (2020). Influence of content and intensity of thought on behavioral and pupil changes during active mind wandering, off-focus, and on-task states. *Attention, Perception, and Psychophysics, 82*(3), 1125-1135. https://doi.org/10.3758/s13414-019-01865-7

Kane, M. J., Carruth, N. P., Lurquin, J. H., Silvia, P. J., Smeekens, B. A., von Bastian, C. C., & Miyake, A. (2021). Individual differences in task-unrelated thought in university classrooms. *Memory and Cognition, 49*(6), 1247-1266. https://doi.org/10.3758/s13421-021-01156-3

Kane, M. J., Smeekens, B. A., Von Bastian, C. C., Lurquin, J. H., Carruth, N. P., & Miyake, A. (2017). A combined experimental and individual-differences investigation into mind wandering during a video lecture. *Journal of Experimental Psychology: General, 146*(11), 1649-1674.

Kopp, K., D'Mello, S., & Mills, C. (2015). Influencing the occurrence of mind wandering while Reading. *Consciousness and Cognition, 34*, 52-62. https://doi.org/10.1016/j.concog.2015.03.003

Kpolovie, P. J., Joe, A. I., & Okoto, T. (2014). Academic achievement prediction: Role of interest in learning and attitude towards school. *International Journal of Humanities Social Sciences and Education (IJHSE), 11*(1), 73-100.

Lindquist, S. I., & McLean, J. P. (2011). Daydreaming and its correlates in an educational environment. *Learning and Individual Differences, 21*(2), 158-167. https://doi.org/10.1016/j.lindif.2010.12.006

Loh, K. K., Tan, B. Z. H., & Lim, S. W. H. (2016). Media multitasking predicts video-recorded lecture learning performance through mind wandering tendencies. *Computers in Human Behavior, 63*, 943-947. https://doi.org/10.1016/j.chb.2016.06.030

Martin, L., Mills, C., D'Mello, S. K., & Risko, E. F. (2018). Re-Watching Lectures as a Study Strategy and Its Effect on Mind Wandering. *Experimental Psychology, 65*(5), 297-305. https://doi.org/10.1027/1618-3169/a000412

McDaniel, Mark A., et al. (2000). The effects of text-based interest on attention and recall. *Journal of Educational Psychology, 92*(3), 492.

McVay, J. C., & Kane, M. J. (2009). Conducting the train of thought: Working memory capacity, goal neglect, and mind wandering in an executive-control task. *Journal of Experimental Psychology: Learning, Memory, and Cognition, 35*(1), 196-204. https://doi.org/10.1037/a0014104

McVay, J. C., & Kane, M. J. (2013). Dispatching the wandering mind? Toward a laboratory method for cuing "spontaneous" off-task thought. *Frontiers in Psychology, 4*, 570. https://doi.org/10.3389/fpsyg.2013.00570

Mills, C., & D'Mello, S. (2014). On the validity of the autobiographical emotional memory task for emotion induction. *PLOS One, 9*(4), Article e95837. https://doi.org/10.1371/journal.pone.0095837

Mitchell, M. (1993). Situational interest: Its multifaceted structure in the secondary school mathematics classroom. *Journal of Educational Psychology, 85*(3), 424-436. https://doi.org/10.1037/0022-0663.85.3.424
Mooneyham, B. W., & Schooler, J. W. (2013). The costs and benefits of mind wandering: A review. *Canadian Journal of Experimental Psychology/Revue canadienne Déléott psychologie expérimentale*, 67(1), 11–18.

Mrazek, M. D., Phillips, D. T., Franklin, M. S., Broadway, J. M., & Schooler, J. W. (2013). Young and restless: Validation of the Mind-Wandering Questionnaire (MWQ) reveals disruptive impact of mind-wandering for youth. *Frontiers in Psychology, 4*, 560. https://doi.org/10.3389/fpsyg.2013.00560

Murray, S., Liang, N., Brosowsky, N., & Seli, P. (2021). What are the benefits of mind wandering to creativity? *Psychology of Aesthetics, Creativity, and the Arts*, 18(3), 403–416.

Pan, S. C., Schmitt, A. G., Bjork, E. L., & Sana, F. (2020). Pretesting reduces mind wandering and enhances learning during online lectures. *Journal of Applied Research in Memory and Cognition*, 9(4), 542–554. https://doi.org/10.1016/j.jarmac.2020.07.004

Peloso, F. C., Zibetti, M. R., Nardi, A. E., & Catelan, R. F. (2024). Cross-cultural adaptation of the mind wandering questionnaire (MWQ) for Brazilian Portuguese and evidence of its validity. *Brazilian Journal of Psychiatry*, 46, Article e20233312.

Reichle, E. D., Reineberg, A. E., & Schooler, J. W. (2010). Eye movements during mindless reading. *Psychological Science*, 21(9), 1300–1310. https://doi.org/10.1177/0956797610378686

Renninger, K. A., & Hidi, S. (2011). Revisiting the conceptualization, measurement, and generation of interest. *Educational Psychologist*, 46(3), 168–184. https://doi.org/10.1080/00461520.2011.587723

Renninger, K. A., & Su, S. (2019). In R. M. Ryan (Ed.), *The Oxford Handbook of Human Motivation, Interest and Its Development* (pp. 203–226). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190666453.013.12.

Risko, E. F., Anderson, N., Sarwal, A., Engelhardt, M., & Kingstone, A. (2012). Everyday attention: Variation in mind wandering and memory in a lecture. *Applied Cognitive Psychology*, 26(2), 234–242. https://doi.org/10.1002/acp.1814

Robertson, I. H., Manly, T., Andrade, J., Baddeley, B. T., & Yiend, J. (1997). 'Oops!': performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. *Neuropsychologia*, 35(6), 747–758. https://doi.org/10.1016/S0028-3932(97)00015-8

Rotgans, J. I., & Schmidt, H. G. (2011). The role of teachers in facilitating situational interest in an active-learning classroom. *Teaching and Teacher Education*, 27(1), 37–42. https://doi.org/10.1016/j.tate.2010.06.025

Rotgans, J. I., & Schmidt, H. G. (2017). Interest development: Arousing situational interest affects the growth trajectory of individual interest. *Contemporary Educational Psychology*, 49, 175–184. https://doi.org/10.1016/j.cedpsych.2017.02.003

Sanchez, C. A., & Naylor, J. S. (2018). Mindwandering while reading not only reduces science learning but also increases content misunderstandings. *Journal of Applied Research in Memory and Cognition*, 7(3), 332–341. https://doi.org/10.1016/j.jarmac.2018.05.001

Schiefele, U. (2009). Situational and Individual Interest. In K. R. Wentzel, & A. Wigfield (Eds.) (Eds.), *Dialogue: The Official Newsletter of the Society for Personality and Social Psychology*, 26, 4–7.

Smallwood, J., McSpadden, M., & Schooler, J. W. (2008). When attention matters : The curious incident of the wandering mind. *Memory & Cognition*, 36(6), 1144–1150. https://doi.org/10.3758/MC.36.6.1144

Shirey, L. L., & Reynolds, R. E. (1988). Effect of interest on attention and learning. *Journal of Educational Psychology*, 80(2), 159.

Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2012). A 21 word solution. *Dialogue: The Official Newsletter of the Society for Personality and Social Psychology*, 26, 4–7.

Smallwood, J., McSpadden, M., & Schooler, J. W. (2008). When attention matters : The curious incident of the wandering mind. *Memory & Cognition*, 36(6), 1144–1150. https://doi.org/10.3758/MC.36.6.1144

Smallwood, J., Nind, L., & O'Connor, R. C. (2009). When is your head at? An exploration of the factors associated with the temporal focus of the wandering mind. *Consciousness and Cognition*, 18(1), 118–125. https://doi.org/10.1016/j.concog.2008.11.004

Smallwood, J., Riby, L., Heim, D., & Davies, J. B. (2006). Encoding during the attentional lapse: accuracy of encoding during the semantic sustained attention to response task. *Consciousness and Cognition*, 15(1), 218–231. https://doi.org/10.1016/j.concog.2005.03.003

Smallwood, J., & Schooler, J. W. (2006). The restless mind. *Psychological Bulletin*, 132(6), 946–958. https://doi.org/10.1037/0033-2909.132.6.946

Soemer, A., & Schiefele, U. (2019). Text difficulty, topic interest, and mind wandering during reading. *Learning and Instruction*, 61, 12–22. https://doi.org/10.1016/j. learninstruc.2018.12.006

Stawarczyk, D., François, C., Wertz, J., & D'Argembeau, A. (2020). Drowsiness or mind wandering? Fluctuations in ocular parameters during attentional lapses. *Biological Psychology*, 156, Article 107950. https://doi.org/10.1016/j.biopsycho.2020.107950

Stawarczyk, D., Majerus, S., Maj, M., Van der Linden, M., & D'Argembeau, A. (2011). Mind wandering: phenomenology and function as assessed with a novel experience sampling method. *Acta Psychologica*, 136(3), 370–381. https://doi.org/10.1016/j.actpsy.2011.01.002

Sundararajan, N., & Adesope, O. (2020). Keep it coherent: A meta-analysis of the seductive details effect. *Educational Psychology Review*, 32(3), 707–734. https://doi.org/10.1007/s10648-020-09522-4

Szpunar, K. K., Khan, N. Y., & Schacter, D. L. (2013). Interpolated memory tests reduce mind wandering and improve learning of online lectures. *Proceedings of the National Academy of Sciences*, 110(16), 6313–6317.

Tanaka, A., & Murayama, K. (2014). Within-person analyses of situational interest and boredom: Interactions between task-specific perceptions and achievement goals. *Journal of Educational Psychology*, 106(4), 1122–1134. https://doi.org/10.1037/a0036659

Unsworth, N., & McMillan, B. D. (2013). Mind wandering and Reading comprehension: Examining the roles of working memory capacity, interest, motivation, and topic experience. *Journal of Experimental Psychology: Learning, Memory, and Cognition*, 39(3), 832–842. https://doi.org/10.1037/a0029669

Vallerand, R. J. (1989). Vers une méthodologie de validation trans-culturelle de questionnaires psychologiques : Implications pour la recherche en langue française. [*Toward a methodology for the transcultural validation of psychological questionnaires: Implications for research in the French language.*]. *Canadian Psychology / Psychologie canadienne*, 30(4), 662–680. https://doi.org/10.1037/h0079856

Vinski, M., & Watter, S. (2013). Being a grump only makes things worse: A transactional account of acute stress on mind wandering. *Frontiers in Psychology, 4*, 730. https://www.frontiersin.org/articles/10.3389/fpsyg.2013.00730).

Wammes, J. D., Ralph, B. C. W., Mills, C., Bosch, N., Duncan, T. L., & Smilek, D. (2019). Disengagement during lectures : Media multitasking and mind wandering in university classrooms. *Computers & Education*, 132, 76–89. https://doi.org/10.1016/j.compedu.2018.12.007

Wammes, J. D., Seli, P., Cheyne, J. A., Boucher, P. O., & Smilek, D. (2016). Mind wandering during lectures II: Relation to academic performance. *Scholarship of Teaching and Learning in Psychology*, 2(1), 33–48.

Was, C. A., Hollis, R. B., & Dunlosky, J. (2019). Do students understand the detrimental effects of mind wandering during online learning? *Computers and Education*, 135, 113–122. https://doi.org/10.1016/j.compedu.2019.02.020

Watson, D., Clark, L. A., & Carey, G. (1988). Positive and negative affectivity and their relation to anxiety and depressive disorders. *Journal of Abnormal Psychology*, 97(3), 346–353.

Weinstein, Y. (2018). Mind wandering, how do I measure thee with probes? Let me count the ways. *Behavior Research Methods*, 50(2), 642–661. https://doi.org/10.3758/s13428-017-0891-9

Wong, S. S. H., & Lim, S. W. H. (2021). Take notes, not photos: Mind wandering mediates the impact of note-taking strategies on video-recorded lecture learning performance. *Journal of Experimental Psychology: Applied*, 29(1), 124–135.