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COVID-19 has led to mental health adversities worldwide. The current study examined whether daily practice of brief mindfulness training has a beneficial impact on affective well-being, and mitigates the negative impact of exposure to COVID-19 news during the pandemic. Participants were randomly assigned into a mindfulness training (MT) group or a waitlist control (WC) group. Participants in the MT group practiced guided mindfulness meditation for a minimum of 10 min each day for 10 days. Both groups completed questionnaires assessing well-being at baseline and after the 10-day period. We also included four ecological momentary assessments (EMA) interspersed throughout the day to measure fine-grained affective states and recent exposure to COVID-19-related news, which has been linked to negative affect. We observed an increase in positive affect in the MT group compared to the WC group in the post-training assessment. However, no group differences emerged in the other three post-training affective measures of negative affect, anxiety and depression. EMA revealed that the MT group also showed more positive affective valence than the WC group across the 10 days. Notably, the WC group reported more negative affective valence following COVID-19 news exposure, whereas the MT group was not impacted. Taken together, our study indicates brief sessions of guided mindfulness meditation during COVID-19 may boost positive affect and serve as a protective buffer against the negative impact of exposure to COVID-19-related news on affective well-being. These findings highlight the utility of mindfulness meditation as an accessible and cost-effective technique to elevate positive affect amidst the COVID-19 pandemic. Supplementary Information The online version contains supplementary material available at 10.1007/s00426-021-01550-1.

The prevalence of the acronym tl;dr (“too long; didn’t read”) suggests that people intentionally disengage their attention from long sections of text. We studied this real-world phenomenon in an ed...

Rumination occurs when an individual becomes mentally stuck and cannot redirect attention away from an unwanted thought demonstrating cognitive inflexibility. Cognitive flexibility is important for various cognitive functions, including episodic memory. Trait rumination is a partial mediator in the relationship between depression and overgeneral episodic memory, suggesting that rumination may negatively influence memory for contextual details. Oscillations in the alpha (8-12 Hz) and beta (13-30 Hz) frequency bands are crucial for various cognitive functions (e.g., attention control and episodic memory) and may help to explain the relationship between trait rumination and memory for contextual details. Our study uses EEG recorded during a source memory task to assess how alpha and beta oscillations during memory for contextual details may change as a function of trait rumination, anxiety, and depression level (n = 43). The source memory task instructs participants to remember objects and their associated contextual details. Memory for contextual details is lessened for participants higher in trait rumination paired with higher trait anxiety. Oscillations were analyzed in posterior parietal/occipital regions. During encoding, an interaction of nonclinical depression level and rumination predicts higher alpha power for items that were later not successfully remembered. During test, depression and rumination interact and predict higher alpha power for both successful and unsuccessful memory. These results suggest that trait anxiety, depression, and rumination impact accuracy and alpha oscillatory dynamics during contextual memory via changes in attention control.

We zone out roughly 20-40% of the time during reading – a rate that is concerning given the negative relationship between mind-wandering and comprehension. We tested if Eye-Mind Reader – an intelli...

We investigate the use of commercial off-the-shelf (COTS) eye-trackers to automatically detect mind wandering—a phenomenon involving a shift in attention from task-related to task-unrelated thoughts—during computerized learning. Study 1 (N = 135 high-school students) tested the feasibility of COTS eye tracking while students learn biology with an intelligent tutoring system called GuruTutor in their classroom. We could successfully track eye gaze in 75% (both eyes tracked) and 95% (one eye tracked) of the cases for 85% of the sessions where gaze was successfully recorded. In Study 2, we used this data to build automated student-independent detectors of mind wandering, obtaining accuracies (mind wandering F1 = 0.59) substantially better than chance (F1 = 0.24). Study 3 investigated context-generalizability of mind wandering detectors, finding that models trained on data collected in a controlled laboratory more successfully generalized to the classroom than the reverse. Study 4 investigated gaze- and video- based mind wandering detection, finding that gaze-based detection was superior and multimodal detection yielded an improvement in limited circumstances. We tested live mind wandering detection on a new sample of 39 students in Study 5 and found that detection accuracy (mind wandering F1 = 0.40) was considerably above chance (F1 = 0.24), albeit lower than offline detection accuracy from Study 1 (F1 = 0.59), a finding attributable to handling of missing data. We discuss our next steps towards developing gaze-based attention-aware learning technologies to increase engagement and learning by combating mind wandering in classroom contexts.

In university classrooms, the use of laptops or smartphones for purposes unrelated to the lecture is on the rise. Consequently, it is important to understand how frequently this behavior occurs, to track whether it increases throughout a lecture, and to quantify the potential costs to learning. In two studies, we measured rates of disengagement during lectures related to media use (i.e. media multitasking; Studies 1 & 2) and lecture-unrelated thoughts (i.e. mind wandering; Study 2). We also measured the impact of these behaviors on learning using quiz questions at the end of each lecture, and students’ actual course tests. In both Study 1 and 2, we found that rates of media multitasking were relatively high and increased as time elapsed in a lecture, while in Study 2, consistent with prior work, rates of mind wandering remained relatively stable. Interestingly, media multitasking - but not mind wandering - was associated with negative learning outcomes.

Previous research has demonstrated reliable fluctuations in attentional processes during the course of the day. Everyday life experience sampling, during which participants respond to “probes” delivered at random intervals throughout the day on their mobile devices, is an effective tool for capturing such diurnal fluctuations in a naturalistic way. The existence of diurnal fluctuations in the case of mind-wandering, however, has not been examined to date. We did so in two studies. In the first study, we employed everyday experience sampling to obtain self-reports from 146 university students who rated the degree of free movement in their thoughts multiple times per day over five days. These time course data were analyzed using multilevel modelling. Freely moving thought was found to fluctuate reliably over the course of the day, with lower ratings reported in the early morning and afternoon and higher ratings around midday and evening. In the second study, we replicated these effects with a reanalysis of data from a past everyday experience-sampling study. We also demonstrated differences in parameter values for the models representing freely moving thought and two common conceptualizations of mind-wandering: task-unrelated thought and stimulus-independent thought. Taken together, the present results establish and replicate a complex pattern of change over the course of the day in how freely thought moves, while also providing further evidence that freedom of movement is dissociable from other dimensions of thought such as its task-relatedness and stimulus-dependence. Future research should focus on probing possible mechanisms behind circadian fluctuations of thought dynamics. Electronic supplementary material The online version of this article (10.1186/s41235-018-0141-4) contains supplementary material, which is available to authorized users.

Much of our knowledge about the correlates of mind-wandering comes from assessing task-unrelated thought. Less is known about the correlates of freely moving thought, a dimension that assesses the degree to which thoughts arise and unfold with low levels of guidance or constraints. Task-unrelated thought is consistently associated with more negative affective valence compared with being on task; however, it is unclear whether freely moving thought shares the same relationship with affect. We conducted two ecological momentary assessment studies in the context of everyday life and found that the two dimensions have different affective correlates. In Study 1, task-unrelated thought was associated with less positive concurrent valence than being on-task. However, freely moving thought was associated with more positive concurrent valence and was predictive of more positive valence at a subsequent timepoint. Freely moving thought, but not task-unrelated thought, also positively predicted concurrent arousal. Computational sentiment analyses of participants' thought descriptions offered convergent evidence of differential relationships between the two thought dimensions and affect. Study 2 used different measurement scales to assess whether (a) the pattern of findings replicated and (b) if the effects were robust to changes in measurement. The main findings were replicated: task-unrelated thought was negatively associated with concurrent valence, whereas freely moving thought was positively associated with concurrent valence. However, freely moving thought did not predict subsequent valence and was not related to concurrent arousal. The most robust findings related to concurrent valence. Although mind-wandering has acquired a relatively negative reputation to date, our findings suggest that there might be positive aspects that remain unexplored. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Humans spend much of their lives engaging with their internal train of thoughts. Traditionally, research focused on whether or not these thoughts are related to ongoing tasks, and has identified reliable and distinct behavioral and neural correlates of task-unrelated and task-related thought. A recent theoretical framework highlighted a different aspect of thinking—how it dynamically moves between topics. However, the neural correlates of such thought dynamics are unknown. The current study aimed to determine the electrophysiological signatures of these dynamics by recording electroencephalogram (EEG) while participants performed an attention task and periodically answered thought-sampling questions about whether their thoughts were 1) task-unrelated, 2) freely moving, 3) deliberately constrained, and 4) automatically constrained. We examined three EEG measures across different time windows as a function of each thought type: stimulus-evoked P3 event-related potentials and non–stimulus-evoked alpha power and variability. Parietal P3 was larger for task-related relative to task-unrelated thoughts, whereas frontal P3 was increased for deliberately constrained compared with unconstrained thoughts. Frontal electrodes showed enhanced alpha power for freely moving thoughts relative to non-freely moving thoughts. Alpha-power variability was increased for task-unrelated, freely moving, and unconstrained thoughts. Our findings indicate distinct electrophysiological patterns associated with task-unrelated and dynamic thoughts, suggesting these neural measures capture the heterogeneity of our ongoing thoughts.

Off-task thought has been found to occur at high rates and is related to impairment in ADHD. However, off-task thought is heterogenous and it remains unclear which specific dimensions of off-task thought are more prevalent in this disorder. It is therefore important to dissociate different aspects of off-task thought in order to better understand the mechanisms underlying impairment. The current study focused on the dimension of constrained (focused) to freely moving off-task thought. Self-report and neurophysiological measures during a computerized attention tasks provided convergent evidence that individuals with ADHD not only have more off-task thought than those without, but also engaged in a greater proportion of freely moving off-task thought than non-ADHD controls. Overall, this work demonstrated differences in both the quantity and type of off-task thought in adults with ADHD. It provides novel insight into both the phenomenology of off-task thought, as well as potential mechanisms underlying impairment in ADHD.

Mind wandering is often characterized by attention oriented away from an external task towards our internal, self-generated thoughts. This universal phenomenon has been linked to numerous disruptive functional outcomes, including performance errors and negative affect. Despite its prevalence and impact, studies to date have yet to identify robust behavioral signatures, making unobtrusive, yet reliable detection of mind wandering a difficult but important task for future applications. Here we examined whether electrophysiological measures can be used in machine learning models to accurately predict mind wandering states. We recorded scalp EEG from participants as they performed an auditory target detection task and self-reported whether they were on task or mind wandering. We successfully classified attention states both within (person-dependent) and across (person-independent) individuals using event-related potential (ERP) measures. Non-linear and linear machine learning models detected mind wandering above-chance within subjects: support vector machine (AUC = 0.715) and logistic regression (AUC = 0.635). Importantly, these models also generalized across subjects: support vector machine (AUC = 0.613) and logistic regression (AUC = 0.609), suggesting we can reliably predict a given individual’s attention state based on ERP patterns observed in the group. This study is the first to demonstrate that machine learning models can generalize to “never-seen-before” individuals using electrophysiological measures, highlighting their potential for real-time prediction of covert attention states.

Lecturing in a classroom environment is challenging - instructors are tasked with maintaining students’ attention for extended periods of time while they are speaking. Previous work investigating the influence of speech on attention, however, has not yet been extended to instructor speech in live classroom lectures. In the current study, we automatically extracted acoustic features from live lectures to determine their association with rates of classroom mind-wandering (i.e., lack of student attention). Results indicated that five speech features reliably predicted classroom mind-wandering rates (Harmonics-to-Noise Ratio, Formant 1 Mean, Formant 2 Mean, Formant 3 Mean, and Jitter Standard Deviation). These speaker correlates of mind-wandering may be a foundation for developing a system to provide feedback in real-time for lecturers online and in the classroom. Such a system may prove to be highly beneficial in developing real-time tools to retain student attention, as well as informing other applications outside of the classroom.

The question of how to evaluate creativity in the context of creative writing has been a subject of ongoing discussion. A key question is whether something as elusive as creativity can be evaluated in a systematic way that goes beyond subjective judgments. To answer this question, we tested whether human evaluations of the creativity of short stories can be predicted by: (1) established measures of creativity and (2) computerized linguistic analyses of the stories. We conducted two studies, in which college students (with and without interest and experience in creative writing) wrote short stories based on a writing prompt. Independent raters (six in Study 1, five in Study 2) assessed the stories using an evaluation rubric specifically designed to assess aspects of creativity, on which they showed high interrater reliability. We provide evidence of convergent validity, in that the rubric evaluations correlated with established creativity measures, including measures of divergent thinking, associative fluency, and self-reported creative behavior and achievements. Linguistic properties of the short stories were analyzed with two computerized text analysis tools: Coh-Metrix, which analyzes aspects of text cohesion and readability, and Linguistic Inquiry and Word Count, which identifies meaningful psychological categories of the text content. Linguistic features predicted the human ratings of creativity to a significant degree. These results provide novel evidence that creative writing can be evaluated reliably and in a systematic way that captures objective features of the text. The results further establish our evaluation rubric as a useful tool to assess creative writing.

Abstract. Material re-exposure (e.g., re-reading) is a popular mnemonic strategy, however, its utility has been questioned. We extend research on re-reading to re-watching – an emerging mnemonic technique given the increased use of recorded lectures today (e.g., in online courses). Consistent with findings from recent investigations of re-reading, there were no benefits of massed re-watching on memory for lecture material and re-watching increased rates of mind wandering. We discuss implications for understanding the cognitive consequences of re-exposure-based mnemonics.

Online education provides the opportunity to present lecture material to students in different formats or modalities, however there is debate about which lecture formats are best. Here, we conducted four experiments with 19–68 year old online participants to address the question of whether visuals of the instructor in online video lectures benefit learning. In Experiments 1 (N = 168) and 2 (N = 206) participants were presented with a lecture in one of three modalities (audio, audio with text, or audio with visuals of the instructor). Participants reported on their attentiveness – mind wandering (MW) – throughout the lecture and then completed a comprehension test. We found no evidence of an advantage for video lectures with visuals of the instructor in terms of a reduction in MW or increase in comprehension. In fact, we found evidence of a comprehension cost, suggesting that visuals of instructors in video lectures may act as a distractor. In Experiments 3 (N = 88) and 4 (N = 109) we explored learners' subjective evaluations of lecture formats across 4 different lecture formats (audio, text, audio + text, audio + instructor, audio + text + instructor). The results revealed learners not only find online lectures with visuals of the instructor more enjoyable and interesting, they believe this format most facilitates their learning. Taken together, these results suggest visuals of the instructor potentially impairs comprehension, but learners prefer and believe they learn most effectively with this format. We refer to as the Instructor Presence Effect and discuss implications for multimedia learning and instructional design.

Despite increasing scientific interest in self-generated thought-mental content largely independent of the immediate environment-there has yet to be any comprehensive synthesis of the subjective experience and neural correlates of affect in these forms of thinking. Here, we aim to develop an integrated affective neuroscience encompassing many forms of self-generated thought-normal and pathological, moderate and excessive, in waking and in sleep. In synthesizing existing literature on this topic, we reveal consistent findings pertaining to the prevalence, valence, and variability of emotion in self-generated thought, and highlight how these factors might interact with self-generated thought to influence general well-being. We integrate these psychological findings with recent neuroimaging research, bringing attention to the neural correlates of affect in self-generated thought. We show that affect in self-generated thought is prevalent, positively biased, highly variable (both within and across individuals), and consistently recruits many brain areas implicated in emotional processing, including the orbitofrontal cortex, amygdala, insula, and medial prefrontal cortex. Many factors modulate these typical psychological and neural patterns, however; the emerging affective neuroscience of self-generated thought must endeavor to link brain function and subjective experience in both everyday self-generated thought as well as its dysfunctions in mental illness.

We investigated how affective states influence expository text comprehension and whether text valence moderates the effects (i.e., mood congruency). In Experiment 1 participants were randomly assigned to a happy or sad affective state (elicited via films) before reading a positive or negative version of a scientific text on animal adaptations. Participants (n = 79) in the sad (film) group had higher scores on deep-reasoning (d = .312) but not surface-level questions on a subsequent multiple-choice comprehension assessment; there was also no evidence for mood congruence. Using a neutral version of the same text, in Experiment 2 participants (n = 52) in a fearful condition performed better on surface-level comprehension questions (d = .594) compared with a sad condition, but the groups were on par for deep-reasoning questions. Experiment 3 (n = 595) did not replicate the findings from Experiment 2 (no comprehension differences between the sad and fear groups) and there were no differences between th...

We hypothesize that cognitively engaged readers dynamically adjust their reading times with respect to text complexity (i.e., reading times should increase for difficult sections and decrease for easier ones) and failure to do so should impair comprehension. This hypothesis is consistent with theories of text comprehension but has surprisingly been untested. We tested this hypothesis by analyzing 4 datasets in which participants (N = 484) read expository texts using a self-paced reading paradigm. Participants self-reported mind wandering in response to pseudorandom thought-probes during reading and completed comprehension assessments after reading. We computed two measures of cognitive coupling by regressing each participant's paragraph-level reading times on two measures of text complexity: Flesch-Kincaid Grade Level and Word Concreteness scores. The two coupling measures yielded convergent findings: coupling was a negative predictor of mind wandering and a positive predictor of both text- and inference-level comprehension. Goodness-of-fit, measured with Akaike information criterion, also improved after adding coupling to the reading-time only models. Furthermore, cognitive coupling mediated the relationship between mind wandering and comprehension, supporting the hypothesis that mind wandering engenders a decoupling of attention from external stimuli. (PsycINFO Database Record

Despite the ubiquitous nature of boredom, the definition, function, and correlates of boredom are still poorly understood. In this review, we summarize the "known" (consistent evidence) and "unknown" (inconsistent evidence) correlates of boredom. We show that boredom is consistently related to negative affect, task-unrelated thought, over-estimation of elapsed time, reduced agency, as well as to over- and under-stimulation. Activation of the default mode network was consistent across the few available fMRI studies, while the recruitment of other brain areas such as the hippocampus and anterior insular cortex, was a notable but less consistent correlate of boredom. Other less consistent correlates of boredom are also reviewed, such as the level of arousal and the mental attributions given to fluctuations of attention. Finally, we identify two critical factors that may contribute to current inconsistencies in the literature and may hamper further progress in the field. First, there is relatively little consistency in the way in which boredom has been operationalized across studies to date, with operationalizations of boredom ranging from negative affect paired with under-stimulation, over-stimulation, to negative affect paired with a lack of goal-directed actions. Second, preliminary evidence suggests the existence of distinct types of boredom (e.g., searching vs. apathetic) that may have different and sometimes even opposing correlates. Adopting a more precise and consistent way of operationalizing boredom, and arriving at an empirically validated taxonomy of different types of boredom, could serve to overcome the current roadblocks to facilitate further progress in our scientific understanding of boredom.

Anticorrelation between the default network (DN) and dorsal attention network (DAN) is thought to be an intrinsic aspect of functional brain organization reflecting competing functions. However, the effect size of functional connectivity (FC) between the DN and DAN has yet to be established. Furthermore, the stability of anticorrelations across distinct DN subsystems, different contexts, and time, remains unexplored. In study 1 we summarize effect sizes of DN-DAN FC from 20 studies, and in study 2 we probe the variability of DN-DAN interactions across six different cognitive states in a new data set. We show that: (i) the DN and DAN have an independent rather than anticorrelated relationship when global signal regression is not used (median effect size across studies: r=-.06; 95% CI: -.15 to .08); (ii) the DAN exhibits weak negative FC with the DN Core subsystem but is uncorrelated with the dorsomedial prefrontal and medial temporal lobe subsystems; (iii) DN-DAN interactions vary significantly across different cognitive states; (iv) DN-DAN FC fluctuates across time between periods of anticorrelation and periods of positive correlation; and (v) changes across time in the strength of DN-DAN coupling are coordinated with interactions involving the frontoparietal control network (FPCN). Overall, the observed weak effect sizes related to DN-DAN anticorrelation suggest the need to re-conceptualize the nature of interactions between these networks. Furthermore, our findings demonstrate that DN-DAN interactions are not stable, but rather, exhibit substantial variability across time and context, and are coordinated with broader network dynamics involving the FPCN.

Re-reading has been shown to have a minimal benefit on text comprehension, in comparison to reading only once or other types of study techniques (e.g., testing; self-explanation). In two experiments we examined the effect of re-reading on mind wandering. Participants read two texts, during which they responded to intermittent mind wandering probes. One text was read once and the other twice. Consistent with previous findings, there was no effect of re-reading on comprehension even though participants reported feeling more competent when they re-read the text. Critically, participants mind wandered more while re-reading. Furthermore, the effect of re-reading on mind wandering was specific to intentional forms of mind wandering rather than unintentional. The implications of these results for understanding mind wandering and the limited effectiveness of re-reading as a mnemonic are discussed.

Online lectures are an increasingly popular tool for learning, yet research on instructor visibility during an online lecture, and students’ environmental settings, has not been well-explored. The current study addresses this gap in the literature by experimentally manipulating online display format and social learning settings to understand their influence on student learning and mind-wandering experiences. Results suggest that instructor visibility within an online lecture does not impact students’ MW or retention performance. However, we found some evidence that students’ social setting during viewing has an impact on MW (p = .05). Specifically, students who watched the lecture in a classroom with others reported significantly more MW than students who watched the lecture alone. Finally, social setting also moderated the negative relationship between MW and material retention. Our results demonstrate that learning experiences during online lectures can vary based on where, and with whom, the lectures are watched.

This study examines the extent to which instructions to self-explain vs. other-explain a text lead readers to produce different forms of explanations. Natural language processing was used to examine the content and characteristics of the explanations produced as a function of instruction condition. Undergraduate students (n = 146) typed either self-explanations or other-explanations while reading a science text. The linguistic properties of these explanations were calculated using three automated text analysis tools. Machine learning classifiers in combination with the features were used to predict instruction condition (i.e., self- or other-explanation). The best machine learning model performed at rates above chance (kappa = .247; accuracy = 63%). Follow-up analyses indicated that students in the self-explanation condition generated explanations that were more cohesive and that contained words that were more related to social order (e.g., ethics). Overall, the results suggest that natural language processing techniques can be used to detect subtle differences in students' processing of complex texts.

Mind-wandering is a ubiquitous phenomenon that is negatively related to learning. The purpose of the current study is to examine mind-wandering during vicarious learning, where participants observed another student engage in a learning session with an intelligent tutoring system (ITS). Participants (N = 118) watched a prerecorded learning session with GuruTutor, a dialogue-based ITS for biology. The response accuracy of the student interacting with the tutor (i.e., the firsthand student) was manipulated across three conditions: Correct (100% accurate responses), Incorrect (0% accurate), and Mixed (50% accurate). Results indicated that Firsthand Student Expertise influenced the frequency of mind-wandering in the participants who engaged vicariously (secondhand students), such that viewing a moderately-skilled firsthand learner (Mixed correctness) reduced the rate of mind-wandering (M = 25.4%) compared to the Correct (M = 33.9%) and Incorrect conditions (M = 35.6%). Firsthand Student Expertise did not impact learning, and we also found no evidence of an indirect effect of Firsthand Student Expertise on learning through mind-wandering (Firsthand Student Expertise → Mind-wandering → Learning). Our findings provide evidence that mind-wandering is a frequent experience during online vicarious learning and offer initial suggestions for the design of vicarious learning experiences that aim to maintain learners’ attentional focus.

Background and objectives Approaching the thin-ideal and avoiding the stigma of fatness are motivational tendencies resulting from the internalisation of sociocultural appearance norms. Individual differences in subclinical levels of eating disorder symptomatology may be related to variation in motivational tendencies regarding thin vs. non-thin bodies. Methods To empirically investigate this hypothesis, the current study employed a novel touchscreen approach-avoidance task with the capacity to effectively simulate compatible approach-avoidance movements. Eighty-four undergraduate females pulled closer or pushed away images depicting either bodies or objects, in response to weight category (underweight bodies vs. overweight bodies) and object category (kitchen items vs. office tools), by means of arm movements. Results Unexpectedly, results revealed relatively faster approach of overweight bodies and relatively faster avoidance of underweight bodies. Moreover, speeded approach towards overweight bodies, relative to underweight bodies, correlated positively with elevated eating disorder symptomatology. Limitations The current sample was restricted to undergraduate women. Conclusions The current study provides initial evidence for the utility of a touchscreen-based measure of approach-avoidance tendencies in body image, albeit comparison with other bias assessments would be necessary. Moreover, our findings suggest that a greater tendency to approach overweight bodies is associated with elevated eating disorder symptomatology. Future extensions of the current work are necessary to clarify the function of motivational tendencies in the body image context.

Contemporary investigations regard creativity as a dynamic form of cognition that involves movement between the dissociable stages of creative generation and creative evaluation. Our recently proposed Dynamic Framework of Thought (Christoff et al., 2016) offered a conceptualization of these stages in terms of an interplay between sources of constraint and variability on thought. This initial conceptualization, however, has yet to be fully explicated and given targeted discussion. Here, we refine this framework’s account of creativity by highlighting the dynamic nature of creative thought, both within and between the stages of creative generation and evaluation. In particular, we emphasize that creative generation in particular is best regarded as a product of multiple, varying mental states, rather than being a singular mental state in and of itself. We also propose that the psychedelic state is a mental state with high potential for facilitating creative generation and update the Dynamic Framework of Thought to incorporate this state. This paper seeks to highlight the dynamic nature of the neurocognitive processes underlying creative thinking and to draw attention to the potential utility of psychedelic substances as experimental tools in the neuroscience of creativity.

Recent applications of dynamic network analyses to functional neuroimaging data have revealed relationships between a number of cognition conditions and the dynamic reconfiguration of brain networks. Here we critically review such applications of network neuroscience to intelligence. After providing an overview of network neuroscience, we center our discussion around the recently proposed Network Neuroscience Theory of Intelligence (Barbey, 2017). We evaluate and review existing empirical support for the theses made by this theory and argue that while studies strongly suggest their plausibility, evidence to date has largely been indirect. We propose avenues for future research to directly evaluate these theses by overcoming the methodological and analytical shortcomings of previous studies. In doing so, our goal is to stimulate future empirical investigations and present valuable ways forward in the network neuroscience of intelligence.

Boredom has recently piqued cognitive neuroscientific interest, but remains a challenge to scientific investigation in this field. We propose that to advance this research, we should (i) seek greater consistency of operationalization and measurement across studies and participants; and (ii) appreciate the temporal instability of boredom and its ensuing dynamics.

The frontoparietal control network (FPCN) plays a central role in executive control. It has been predominantly viewed as a unitary domain general system. Here, we examined patterns of FPCN functional connectivity (FC) across multiple conditions of varying cognitive demands, to test for FPCN heterogeneity. We identified two distinct subsystems within the FPCN based on hierarchical clustering and machine learning classification analyses of within-FPCN FC patterns. These two FPCN subsystems exhibited distinct patterns of FC with the default network (DN) and the dorsal attention network (DAN). FPCNA exhibited stronger connectivity with the DN than the DAN, whereas FPCNB exhibited the opposite pattern. This twofold FPCN differentiation was observed across four independent datasets, across nine different conditions (rest and eight tasks), at the level of individual-participant data, as well as in meta-analytic coactivation patterns. Notably, the extent of FPCN differentiation varied across conditions, suggesting flexible adaptation to task demands. Finally, we used meta-analytic tools to identify several functional domains associated with the DN and DAN that differentially predict activation in the FPCN subsystems. These findings reveal a flexible and heterogeneous FPCN organization that may in part emerge from separable DN and DAN processing streams. We propose that FPCNA may be preferentially involved in the regulation of introspective processes, whereas FPCNB may be preferentially involved in the regulation of visuospatial perceptual attention.

The last decade has seen an increase in studies investigating the neural correlates of mind wandering. Many studies have implicated the default network (DN), particularly the medial temporal lobe (MTL) subsystem of the DN (DNMTL), as a primary network activated when someone's mind is wandering ([

Attention is critical for learning, but difficult to sustain. Here, we investigated the possibility of integrating a short mindfulness exercise into an ITS to reduce mind wandering (MW)—an attentional shift from task-relevant to task-irrelevant thoughts—and improve learning. Participants were randomly assigned to engage with a 5-min auditory mindfulness exercise (experimental group), a relaxation exercise (active control group), or nothing (control group) before completing a 20-min (SD = 5.77) learning session from GuruTutor, a conversational biology ITS. Participants self-reported MW in response to pseudo-random thought probes during learning and completed pre- and posttest assessments that measured learning. On average, participants reported MW on 25% (SD = 20%) of probes, but there was no evidence that MW rates or learning varied across conditions. Our findings suggest that integrating this mindfulness exercise did not improve attention or learning within the ITS.

Classroom teachers utilize many nonverbal activities, such as gesturing and walking, to maintain student attention. Quantifying instructor behaviors in a live classroom environment has traditionally been done through manual coding, a prohibitively time-consuming process which precludes providing timely, fine-grained feedback to instructors. Here we propose an automated method for assessing teachers’ non-verbal behaviors using video-based motion estimation tailored for classroom applications. Motion was estimated by subtracting background pixels that varied little from their mean values, and then noise was reduced using filters designed specifically with the movements and speeds of teachers in mind. Camera pan and zoom events were also detected, using a method based on tracking the correlations between moving points in the video. Results indicated the motion estimation method was effective for predicting instructors’ non-verbal behaviors, including gestures (kappa = .298), walking (kappa = .338), and camera pan (an indicator of instructor movement; kappa = .468), all of which are plausibly related to student attention. We also found evidence of predictive validity, as these automated predictions of instructor behaviors were correlated with students’ mean self-reported level of attention (e.g., r = .346 for walking), indicating that the proposed method captures the association between instructors’ non-verbal behaviors and student attention. We discuss the potential for providing timely, fine-grained, automated feedback to teachers, as well as opportunities for future classroom studies using this method.

This study assessed the occurrence and factors that influence mind wandering (MW) in the domain of film comprehension. The cascading model of inattention assumes that a stronger mental representation (i.e., a situation model) during comprehension results in less MW. Accordingly, a suppression hypothesis suggests that MW would decrease as a function of having the knowledge of the plot of a film prior to viewing, because the prior-knowledge would help to strengthen the situation model during comprehension. Furthermore, an interest-moderation hypothesis would predict that the suppression effect of prior-knowledge would only emerge when there was interest in viewing the film. In the current experiment, 108 participants either read a short story that depicted the plot (i.e., prior-knowledge condition) or read an unrelated story of equal length (control condition) prior to viewing the short film (32.5 minutes) entitled The Red Balloon. Participants self-reported their interest in viewing the film immediately before the film was presented. MW was tracked using a self-report method targeting instances of MW with metacognitive awareness. Participants in the prior-knowledge condition reported less MW compared with the control condition, thereby supporting the suppression hypothesis. MW also decreased over the duration of the film, but only for those with prior-knowledge of the film. Finally, prior-knowledge effects on MW were only observed when interest was average or high, but not when interest was low.

Attention is critical to learning. Hence, advanced learning technologies should benefit from mechanisms to monitor and respond to learners' attentional states. We study the feasibility of integrating commercial off-the-shelf (COTS) eye trackers to monitor attention during interactions with a learning technology called GuruTutor. We tested our implementation on 135 students in a noisy computer-enabled high school classroom and were able to collect a median 95% valid eye gaze data in 85% of the sessions where gaze data was successfully recorded. Machine learning methods were employed to develop automated detectors of mind wandering (MW) -- a phenomenon involving a shift in attention from task-related to task-unrelated thoughts that is negatively correlated with performance. Our student-independent, gaze-based models could detect MW with an accuracy (F1 of MW = 0.59) significantly greater than chance (F1 of MW = 0.24). Predicted rates of mind wandering were negatively related to posttest performance, providing evidence for the predictive validity of the detector. We discuss next steps towards developing gaze-based, attention-aware, learning technologies that can be deployed in noisy, real-world environments.

Mind wandering is frequently defined as task-unrelated or perceptually decoupled thought. However, these definitions may not capture the dynamic features of a wandering mind, such as its tendency to 'move freely'. Here we test the relationship between three theoretically dissociable dimensions of thought: freedom of movement in thought, task-relatedness, and perceptual decoupling (i.e., lack of awareness of surroundings). Using everyday life experience sampling, thought probes were randomly delivered to participants' phones for ten days. Results revealed weak intra-individual correlations between freedom of movement in thought and task-unrelatedness, as well as perceptual decoupling. Within our dataset, over 40% of thoughts would have been misclassified under the assumption that off-task thought is inherently freely moving. Overall, freedom of movement appears to be an independent dimension of thought that is not captured by the two most common measures of mind wandering. Future work focusing on the dynamics of thought may be crucial for improving our understanding of the wandering mind.

Current learning technologies have no direct way to assess students' mental effort: are they in deep thought, struggling to overcome an impasse, or are they zoned out? To address this challenge, we propose the use of EEG-based cognitive load detectors during learning. Despite its potential, EEG has not yet been utilized as a way to optimize instructional strategies. We take an initial step towards this goal by assessing how experimentally manipulated (easy and difficult) sections of an intelligent tutoring system (ITS) influenced EEG-based estimates of students' cognitive load. We found a main effect of task difficulty on EEG-based cognitive load estimates, which were also correlated with learning performance. Our results show that EEG can be a viable source of data to model learners' mental states across a 90-minute session.

Spontaneous thinking is a ubiquitous aspect of our mental life and has increasingly become a hot topic of research in cognitive neuroscience. To date, functional neuroimaging studies of spontaneous thought have revealed general brain recruitment centered on a combination of default mode network and executive regions. Despite recent findings about general brain recruitment, very little is known about how these regions are recruited dynamically over time. The current research addresses this gap in the literature by using EEG to investigate the fine-grained temporal dynamics of brain activity underlying spontaneous thoughts. We employed the first-person reports of experienced meditators to index the onset of spontaneous thoughts, and examined brain electrical activity preceding indications of spontaneous thought onset. An independent component analysis-based source localization procedure recovered sources very similar to those previously found with fMRI (Ellamil et al. in NeuroImage 136:186–196, 2016). In addition, phase synchrony analyses revealed a temporal trajectory that begins with default network midline and salience network connectivity, followed by the incorporation of language and executive regions during the period from thought generation to appraisal.

Writing training systems have been developed to provide students with instruction and deliberate practice on their writing. Although generally successful in providing accurate scores, a common criticism of these systems is their lack of personalization and adaptive instruction. In particular, these systems tend to place the strongest emphasis on delivering accurate scores, and therefore, tend to overlook additional indices that may contribute to students' success, such as their affective states during writing practice. This study takes an initial step toward addressing this gap by building a predictive model of students' affect using information that can potentially be collected by computer systems. We used individual difference measures, text indices, and keystroke analyses to predict engagement and boredom in 132 writing sessions. The results suggest that these three categories of indices were successful in modeling students' affective states during writing. Taken together, indices related to students' academic abilities, text properties, and keystroke logs were able classify high and low engagement and boredom in writing sessions with accuracies between 76.5% and 77.3%. These results suggest that information readily available in writing training systems can inform affect detectors and ultimately improve student models within intelligent tutoring systems.

The Autobiographical Emotional Memory Task (AEMT), which involves recalling and writing about intense emotional experiences, is a widely used method to experimentally induce emotions. The validity of this method depends upon the extent to which it can induce specific desired emotions (intended emotions), while not inducing any other (incidental) emotions at different levels across one (or more) conditions. A review of recent studies that used this method indicated that most studies exclusively monitor post-writing ratings of the intended emotions, without assessing the possibility that the method may have differentially induced other incidental emotions as well. We investigated the extent of this issue by collecting both pre- and post-writing ratings of incidental emotions in addition to the intended emotions. Using methods largely adapted from previous studies, participants were assigned to write about a profound experience of anger or fear (Experiment 1) or happiness or sadness (Experiment 2). In line with previous research, results indicated that intended emotions (anger and fear) were successfully induced in the respective conditions in Experiment 1. However, disgust and sadness were also induced while writing about an angry experience compared to a fearful experience. Similarly, although happiness and sadness were induced in the appropriate conditions, Experiment 2 indicated that writing about a sad experience also induced disgust, fear, and anger, compared to writing about a happy experience. Possible resolutions to avoid the limitations of the AEMT to induce specific discrete emotions are discussed.