Age of Exposure 2.0: Estimating word complexity using iterative models of word embeddings.

Age of acquisition (AoA) is a measure of word complexity which refers to the age at which a word is typically learned. AoA measures have shown strong correlations with reading comprehension, lexical decision times, and writing quality. AoA scores based on both adult and child data have limitations that allow for error in measurement, and increase the cost and effort to produce. In this paper, we introduce Age of Exposure (AoE) version 2, a proxy for human exposure to new vocabulary terms that expands AoA word lists through training regressors to predict AoA scores. Word2vec word embeddings are trained on cumulatively increasing corpora of texts, word exposure trajectories are generated by aligning the word2vec vector spaces, and features of words are derived for modeling AoA scores. Our prediction models achieve low errors (from 13% with a corresponding R2 of .35 up to 7% with an R2 of .74), can be uniformly applied to different AoA word lists, and generalize to the entire vocabulary of a language. Our method benefits from using existing readability indices to define the order of texts in the corpora, while the performed analyses confirm that the generated AoA scores accurately predicted the difficulty of texts (R2 of .84, surpassing related previous work). Further, we provide evidence of the internal reliability of our word trajectory features, demonstrate the effectiveness of the word trajectory features when contrasted with simple lexical features, and show that the exclusion of features that rely on external resources does not significantly impact performance.

Challenges and solutions to employing natural language processing and machine learning to measure patients' health literacy and physician writing complexity: The ECLIPPSE study.

OBJECTIVE: In the National Library of Medicine funded ECLIPPSE Project (Employing Computational Linguistics to Improve Patient-Provider Secure Emails exchange), we attempted to create novel, valid, and scalable measures of both patients' health literacy (HL) and physicians' linguistic complexity by employing natural language processing (NLP) techniques and machine learning (ML). We applied these techniques to > 400,000 patients' and physicians' secure messages (SMs) exchanged via an electronic patient portal, developing and validating an automated patient literacy profile (LP) and physician complexity profile (CP). Herein, we describe the challenges faced and the solutions implemented during this innovative endeavor. MATERIALS AND METHODS: To describe challenges and solutions, we used two data sources: study documents and interviews with study investigators. Over the five years of the project, the team tracked their research process using a combination of Google Docs tools and an online team organization, tracking, and management tool (Asana). In year 5, the team convened a number of times to discuss, categorize, and code primary challenges and solutions. RESULTS: We identified 23 challenges and associated approaches that emerged from three overarching process domains: (1) Data Mining related to the SM corpus; (2) Analyses using NLP indices on the SM corpus; and (3) Interdisciplinary Collaboration. With respect to Data Mining, problems included cleaning SMs to enable analyses, removing hidden caregiver proxies (e.g., other family members) and Spanish language SMs, and culling SMs to ensure that only patients' primary care physicians were included. With respect to Analyses, critical decisions needed to be made as to which computational linguistic indices and ML approaches should be selected; how to enable the NLP-based linguistic indices tools to run smoothly and to extract meaningful data from a large corpus of medical text; and how to best assess content and predictive validities of both the LP and the CP. With respect to the Interdisciplinary Collaboration, because the research required engagement between clinicians, health services researchers, biomedical informaticians, linguists, and cognitive scientists, continual effort was needed to identify and reconcile differences in scientific terminologies and resolve confusion; arrive at common understanding of tasks that needed to be completed and priorities therein; reach compromises regarding what represents "meaningful findings" in health services vs. cognitive science research; and address constraints regarding potential transportability of the final LP and CP to different health care settings. DISCUSSION: Our study represents a process evaluation of an innovative research initiative to harness "big linguistic data" to estimate patient HL and physician linguistic complexity. Any of the challenges we identified, if left unaddressed, would have either rendered impossible the effort to generate LPs and CPs, or invalidated analytic results related to the LPs and CPs. Investigators undertaking similar research in HL or using computational linguistic methods to assess patient-clinician exchange will face similar challenges and may find our solutions helpful when designing and executing their health communications research.

Secure Messaging with Physicians by Proxies for Patients with Diabetes: Findings from the ECLIPPSE Study.

BACKGROUND: Little is known about patients who have caregiver proxies communicate with healthcare providers via portal secure messaging (SM). Since proxy portal use is often informal (e.g., sharing patient accounts), novel methods are needed to estimate the prevalence of proxy-authored SMs. OBJECTIVE: (1) Develop an algorithm to identify proxy-authored SMs, (2) apply this algorithm to estimate predicted proxy SM (PPSM) prevalence among patients with diabetes, and (3) explore patient characteristics associated with having PPSMs. DESIGN: Retrospective cohort study. PARTICIPANTS: We examined 9856 patients from Diabetes Study of Northern California (DISTANCE) who sent ≥ 1 English-language SM to their primary care physician between July 1, 2006, and Dec. 31, 2015. MAIN MEASURES: Using computational linguistics, we developed ProxyID, an algorithm that identifies phrases frequently found in registered proxy SMs. ProxyID was validated against blinded expert categorization of proxy status among an SM sample, then applied to identify PPSM prevalence across patients. We examined patients' sociodemographic and clinical characteristics according to PPSM penetrance, "none" (0%), "low" (≥ 0-50%), and "high" (≥ 50-100%). KEY RESULTS: Only 2.3% of patients had ≥ 1 registered proxy-authored SM. ProxyID demonstrated moderate agreement with expert classification (Κ = 0.58); 45.7% of patients had PPSMs (40.2% low and 5.5% high). Patients with high percent PPSMs were older than those with low percent and no PPSMs (66.5 vs 57.4 vs 56.2 years, p < 0.001) had higher rates of limited English proficiency (16.1% vs 3.2% vs 3.5%, p < 0.05), lower self-reported health literacy (3.83 vs 4.43 vs 4.44, p < 0.001), and more comorbidities (Charlson index 3.78 vs 2.35 vs 2.18, p < 0.001). CONCLUSIONS: Among patients with diabetes, informal proxy SM use is more common than registered use and prevalent among socially and medically vulnerable patients. Future research should explore whether proxy portal use improves patient and/or caregiver outcomes and consider policies that integrate caregivers in portal communication.

Cohesion network analysis of CSCL participation.

The broad use of computer-supported collaborative-learning (CSCL) environments (e.g., instant messenger-chats, forums, blogs in online communities, and massive open online courses) calls for automated tools to support tutors in the time-consuming process of analyzing collaborative conversations. In this article, the authors propose and validate the cohesion network analysis (CNA) model, housed within the ReaderBench platform. CNA, grounded in theories of cohesion, dialogism, and polyphony, is similar to social network analysis (SNA), but it also considers text content and discourse structure and, uniquely, uses automated cohesion indices to generate the underlying discourse representation. Thus, CNA enhances the power of SNA by explicitly considering semantic cohesion while modeling interactions between participants. The primary purpose of this article is to describe CNA analysis and to provide a proof of concept, by using ten chat conversations in which multiple participants debated the advantages of CSCL technologies. Each participant's contributions were human-scored on the basis of their relevance in terms of covering the central concepts of the conversation. SNA metrics, applied to the CNA sociogram, were then used to assess the quality of each member's degree of participation. The results revealed that the CNA indices were strongly correlated to the human evaluations of the conversations. Furthermore, a stepwise regression analysis indicated that the CNA indices collectively predicted 54% of the variance in the human ratings of participation. The results provide promising support for the use of automated computational assessments of collaborative participation and of individuals' degrees of active involvement in CSCL environments.

Sentiment Analysis and Social Cognition Engine (SEANCE): An automatic tool for sentiment, social cognition, and social-order analysis.

This study introduces the Sentiment Analysis and Cognition Engine (SEANCE), a freely available text analysis tool that is easy to use, works on most operating systems (Windows, Mac, Linux), is housed on a user's hard drive (as compared to being accessed via an Internet interface), allows for batch processing of text files, includes negation and part-of-speech (POS) features, and reports on thousands of lexical categories and 20 component scores related to sentiment, social cognition, and social order. In the study, we validated SEANCE by investigating whether its indices and related component scores can be used to classify positive and negative reviews in two well-known sentiment analysis test corpora. We contrasted the results of SEANCE with those from Linguistic Inquiry and Word Count (LIWC), a similar tool that is popular in sentiment analysis, but is pay-to-use and does not include negation or POS features. The results demonstrated that both the SEANCE indices and component scores outperformed LIWC on the categorization tasks.

The tool for the automatic analysis of text cohesion (TAACO): Automatic assessment of local, global, and text cohesion.

This study introduces the Tool for the Automatic Analysis of Cohesion (TAACO), a freely available text analysis tool that is easy to use, works on most operating systems (Windows, Mac, and Linux), is housed on a user's hard drive (rather than having an Internet interface), allows for the batch processing of text files, and incorporates over 150 classic and recently developed indices related to text cohesion. The study validates TAACO by investigating how its indices related to local, global, and overall text cohesion can predict expert judgments of text coherence and essay quality. The findings of this study provide predictive validation of TAACO and support the notion that expert judgments of text coherence and quality are either negatively correlated or not predicted by local and overall text cohesion indices, but are positively predicted by global indices of cohesion. Combined, these findings provide supporting evidence that coherence for expert raters is a property of global cohesion and not of local cohesion, and that expert ratings of text quality are positively related to global cohesion.

From Generating in the Lab to Tutoring Systems in Classrooms.

Work in cognitive and educational psychology examines a variety of phenomena related to the learning and retrieval of information. Indeed, Alice Healy, our honoree, and her colleagues have conducted a large body of groundbreaking research on this topic. In this article we discuss how 3 learning principles (the generation effect, deliberate practice and feedback, and antidotes to disengagement) discussed in Healy, Schneider, and Bourne (2012) have influenced the design of 2 intelligent tutoring systems that attempt to incorporate principles of skill and knowledge acquisition. Specifically, this article describes iSTART-2 and the Writing Pal, which provide students with instruction and practice using comprehension and writing strategies. iSTART-2 provides students with training to use effective comprehension strategies while self-explaining complex text. The Writing Pal provides students with instruction and practice to use basic writing strategies when writing persuasive essays. Underlying these systems are the assumptions that students should be provided with initial instruction that breaks down the tasks into component skills and that deliberate practice should include active generation with meaningful feedback, all while remaining engaging. The implementation of these assumptions is complicated by the ill-defined natures of comprehension and writing and supported by the use of various natural language processing techniques. We argue that there is value in attempting to integrate empirically supported learning principles into educational activities, even when there is imperfect alignment between them. Examples from the design of iSTART-2 and Writing Pal guide this argument.

Natural language processing in an intelligent writing strategy tutoring system.

The Writing Pal is an intelligent tutoring system that provides writing strategy training. A large part of its artificial intelligence resides in the natural language processing algorithms to assess essay quality and guide feedback to students. Because writing is often highly nuanced and subjective, the development of these algorithms must consider a broad array of linguistic, rhetorical, and contextual features. This study assesses the potential for computational indices to predict human ratings of essay quality. Past studies have demonstrated that linguistic indices related to lexical diversity, word frequency, and syntactic complexity are significant predictors of human judgments of essay quality but that indices of cohesion are not. The present study extends prior work by including a larger data sample and an expanded set of indices to assess new lexical, syntactic, cohesion, rhetorical, and reading ease indices. Three models were assessed. The model reported by McNamara, Crossley, and McCarthy (Written Communication 27:57-86, 2010) including three indices of lexical diversity, word frequency, and syntactic complexity accounted for only 6% of the variance in the larger data set. A regression model including the full set of indices examined in prior studies of writing predicted 38% of the variance in human scores of essay quality with 91% adjacent accuracy (i.e., within 1 point). A regression model that also included new indices related to rhetoric and cohesion predicted 44% of the variance with 94% adjacent accuracy. The new indices increased accuracy but, more importantly, afford the means to provide more meaningful feedback in the context of a writing tutoring system.

Automated Strategy Feedback Can Improve the Readability of Physicians' Electronic Communications to Simulated Patients.

Modern communication between health care professionals and patients increasingly relies upon secure messages (SMs) exchanged through an electronic patient portal. Despite the convenience of secure messaging, challenges include gaps between physician and patient expertise along with the asynchronous nature of such communication. Importantly, less readable SMs from physicians (e.g., too complicated) may result in patient confusion, non-adherence, and ultimately poorer health outcomes. The current simulation trial synthesizes work on patient-physician electronic communication, message readability assessments, and feedback to explore the potential for automated strategy feedback to improve the readability of physicians' SMs to patients. Within a simulated secure messaging portal featuring multiple simulated patient scenarios, computational algorithms assessed the complexity of SMs written by 67 participating physicians to patients. The messaging portal provided strategy feedback for how physician responses might be improved (e.g., adding details and information to reduce complexity). Analyses of changes in SM complexity revealed that automated strategy feedback indeed helped physicians compose and refine more readable messages. Although the effects for any individual SM were slight, the cumulative effects within and across patient scenarios showed trends of decreasing complexity. Physicians appeared to learn how to craft more readable SMs via interactions with the feedback system. Implications for secure messaging systems and physician training are discussed, along with considerations for further investigation of broader physician populations and effects on patient experience.

Leveraging a multidimensional linguistic analysis of constructed responses produced by college readers.

The goal of this study was to assess the relationships between computational approaches to analyzing constructed responses made during reading and individual differences in the foundational skills of reading in college readers. We also explored if these relationships were consistent across texts and samples collected at different institutions and texts. The study made use of archival data that involved college participants who produced typed constructed responses under thinking aloud instructions reading history and science texts. They also took assessments of vocabulary knowledge and proficiency in comprehension. The protocols were analyzed to assess two different ways to determine their cohesion. One approach involved assessing how readers established connections with themselves (i.e., to other constructed responses they produced). The other approach involved assessing connections between the constructed responses and the texts that were read. Additionally, the comparisons were made by assessing both lexical (i.e., word matching) and semantic (i.e., high dimensional semantic spaces) comparisons. The result showed that both approaches for analyzing cohesion and making the comparisons were correlated with vocabulary knowledge and comprehension proficiency. The implications of the results for theory and practice are discussed.

The neural correlates of strategic reading comprehension: cognitive control and discourse comprehension.

Neuroimaging studies of text comprehension conducted thus far have shed little light on the brain mechanisms underlying strategic learning from text. Thus, the present study was designed to answer the question of what brain areas are active during performance of complex reading strategies. Reading comprehension strategies are designed to improve a reader's comprehension of a text. For example, self-explanation is a complex reading strategy that enhances existing comprehension processes. It was hypothesized that reading strategies would involve areas of the brain that are normally involved in reading comprehension along with areas that are involved in strategic control processes because the readers are intentionally using a complex reading strategy. Subjects were asked to reread, paraphrase, and self-explain three different texts in a block design fMRI study. Activation was found in both executive control and comprehension areas, and furthermore, learning from text was associated with activation in the anterior prefrontal cortex (aPFC). The authors speculate that the aPFC may play a role in coordinating the internal and external modes of thought that are necessary for integrating new knowledge from texts with prior knowledge.

The bit in the middle and why it's important: a computational analysis of the linguistic features of body paragraphs.

This study examines the relationship between the linguistic characteristics of body paragraphs of student essays and the total number of paragraphs in the essays. Results indicate a significant relationship between the total number of paragraphs and a variety of linguistic characteristics known to affect student essay scores. These linguistic characteristics (e.g., semantic overlap, syntactic complexity) contribute to two underlying factors (i.e., textual cohesion and difficulty) that are used as dependent variables in mixed-effect models. Results suggest that student essays with 5-8 paragraphs tend to be more linguistically consistent than student essays with 3, 4, and 9 paragraphs. Essays with totals of 5-8 paragraphs, considered by many educators to contain an optimal number of paragraphs, may include functionally and structurally similar paragraphs. These findings could aid writing researchers and educators in obtaining a clearer view of the relationship between the total number of paragraphs comprising an essay and the linguistic characteristics that affect essay evaluation. Consequently, writing interventions may become better equipped to pinpoint student difficulties and facilitate student writing skills by providing more detailed and informed feedback.

Before and during COVID-19: A Cohesion Network Analysis of students' online participation in moodle courses.

The COVID-19 pandemic has changed the entire world, while the impact and usage of online learning environments has greatly increased. This paper presents a new version of the ReaderBench framework, grounded in Cohesion Network Analysis, which can be used to evaluate the online activity of students as a plug-in feature to Moodle. A Recurrent Neural Network with LSTM cells that combines global features, including participation and initiation indices, with a time series analysis on timeframes is used to predict student grades, while multiple sociograms are generated to observe interaction patterns. Students' behaviors and interactions are compared before and during COVID-19 using two consecutive yearly instances of an undergraduate course in Algorithm Design, conducted in Romanian using Moodle. The COVID-19 outbreak generated an off-balance, a drastic increase in participation, followed by a decrease towards the end of the semester, compared to the academic year 2018-2019 when lower fluctuations in participation were observed. The prediction model for the 2018-2019 academic year obtained an R 2 of 0.27, while the model for the second year obtained a better R 2 of 0.34, a value arguably attributable to an increased volume of online activity. Moreover, the best model from the first academic year is partially generalizable to the second year, but explains a considerably lower variance (R 2 = 0.13). In addition to the quantitative analysis, a qualitative analysis of changes in student behaviors using comparative sociograms further supported conclusions that there were drastic changes in student behaviors observed as a function of the COVID-19 pandemic.

Precision communication: Physicians' linguistic adaptation to patients' health literacy.

Little quantitative research has explored which clinician skills and behaviors facilitate communication. Mutual understanding is especially challenging when patients have limited health literacy (HL). Two strategies hypothesized to improve communication include matching the complexity of language to patients' HL ("universal tailoring"); or always using simple language ("universal precautions"). Through computational linguistic analysis of 237,126 email exchanges between dyads of 1094 physicians and 4331 English-speaking patients, we assessed matching (concordance/discordance) between physicians' linguistic complexity and patients' HL, and classified physicians' communication strategies. Among low HL patients, discordance was associated with poor understanding (P = 0.046). Physicians' "universal tailoring" strategy was associated with better understanding for all patients (P = 0.01), while "universal precautions" was not. There was an interaction between concordance and communication strategy (P = 0.021): The combination of dyadic concordance and "universal tailoring" eliminated HL-related disparities. Physicians' ability to adapt communication to match their patients' HL promotes shared understanding and equity. The 'Precision Medicine' construct should be expanded to include the domain of 'Precision Communication.'

Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study.

OBJECTIVE: To develop novel, scalable, and valid literacy profiles for identifying limited health literacy patients by harnessing natural language processing. DATA SOURCE: With respect to the linguistic content, we analyzed 283 216 secure messages sent by 6941 diabetes patients to physicians within an integrated system's electronic portal. Sociodemographic, clinical, and utilization data were obtained via questionnaire and electronic health records. STUDY DESIGN: Retrospective study used natural language processing and machine learning to generate five unique "Literacy Profiles" by employing various sets of linguistic indices: Flesch-Kincaid (LP_FK); basic indices of writing complexity, including lexical diversity (LP_LD) and writing quality (LP_WQ); and advanced indices related to syntactic complexity, lexical sophistication, and diversity, modeled from self-reported (LP_SR), and expert-rated (LP_Exp) health literacy. We first determined the performance of each literacy profile relative to self-reported and expert-rated health literacy to discriminate between high and low health literacy and then assessed Literacy Profiles' relationships with known correlates of health literacy, such as patient sociodemographics and a range of health-related outcomes, including ratings of physician communication, medication adherence, diabetes control, comorbidities, and utilization. PRINCIPAL FINDINGS: LP_SR and LP_Exp performed best in discriminating between high and low self-reported (C-statistics: 0.86 and 0.58, respectively) and expert-rated health literacy (C-statistics: 0.71 and 0.87, respectively) and were significantly associated with educational attainment, race/ethnicity, Consumer Assessment of Provider and Systems (CAHPS) scores, adherence, glycemia, comorbidities, and emergency department visits. CONCLUSIONS: Since health literacy is a potentially remediable explanatory factor in health care disparities, the development of automated health literacy indicators represents a significant accomplishment with broad clinical and population health applications. Health systems could apply literacy profiles to efficiently determine whether quality of care and outcomes vary by patient health literacy; identify at-risk populations for targeting tailored health communications and self-management support interventions; and inform clinicians to promote improvements in individual-level care.

Descriptive examination of secure messaging in a longitudinal cohort of diabetes patients in the ECLIPPSE study.

The substantial expansion of secure messaging (SM) via the patient portal in the last decade suggests that it is becoming a standard of care, but few have examined SM use longitudinally. We examined SM patterns among a diverse cohort of patients with diabetes (N = 19 921) and the providers they exchanged messages with within a large, integrated health system over 10 years (2006-2015), linking patient demographics to SM use. We found a 10-fold increase in messaging volume. There were dramatic increases overall and for patient subgroups, with a majority of patients (including patients with lower income or with self-reported limited health literacy) messaging by 2015. Although more physicians than nurses and other providers messaged throughout the study, the distribution of health professions using SM changed over time. Given this rapid increase in SM, deeper understanding of optimizing the value of patient and provider engagement, while managing workflow and training challenges, is crucial.

Strategies to read and learn: overcoming learning by consumption.

OBJECTIVES: This article discusses the need for, and value of, providing students with instruction in how to use comprehension strategies as well as the effectiveness of inducing strategy use through cognitive disequilibrium. The leading assumption that guides this article is that learning facts and figures is not enough. Students need to build deep knowledge that is interconnected, coherent and includes understanding of potential causal mechanisms. Doing so requires going beyond the printed page by generating inferences and developing coherent explanations. Inferences and explanations allow the student to make links between concepts in the material and, importantly, to make connections to prior knowledge. These connections render students' understanding of new material more coherent and, in consequence, deeper and more stable. DISCUSSION: This article describes two means of inducing students to construct a deeper understanding of new material. One means of challenging students is through cohesion gaps in a text (or a lecture) that require the student to generate inferences to understand the relationships between concepts. Although low-knowledge readers are not able to generate these inferences, relatively high-knowledge readers (e.g. medical students) are more likely to successfully generate inferences to bridge conceptual gaps, and doing so results in a deeper understanding of the material. A second means of inducing active processing is to provide students with instruction and practice in how to use comprehension strategies. This article describes methods of providing such instruction, including the intelligent tutoring system, iSTART. CONCLUSIONS: The overarching goal of the research described in this article is to scaffold students towards ideal learning strategies. This cannot happen simply by telling students about good strategies. It is ineffective to inform a student that the content will be better understood if it is explained or evaluated. Such an approach is a victim of learning by consumption attitudes towards education.

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study.

Limited health literacy is a barrier to optimal healthcare delivery and outcomes. Current measures requiring patients to self-report limitations are time-consuming and may be considered intrusive by some. This makes widespread classification of patient health literacy challenging. The objective of this study was to develop and validate "literacy profiles" as automated indicators of patients' health literacy to facilitate a non-intrusive, economic and more comprehensive characterization of health literacy among a health care delivery system's membership. To this end, three literacy profiles were generated based on natural language processing (combining computational linguistics and machine learning) using a sample of 283,216 secure messages sent from 6,941 patients to their primary care physicians. All patients were participants in Kaiser Permanente Northern California's DISTANCE Study. Performance of the three literacy profiles were compared against a gold standard of patient self-reported health literacy. Associations were analyzed between each literacy profile and patient demographics, health outcomes and healthcare utilization. T-tests were used for numeric data such as A1C, Charlson comorbidity index and healthcare utilization rates, and chi-square tests for categorical data such as sex, race, poor adherence and severe hypoglycemia. Literacy profiles varied in their test characteristics, with C-statistics ranging from 0.61-0.74. Relations between literacy profiles and health outcomes revealed patterns consistent with previous health literacy research: patients identified via literacy profiles indicative of limited health literacy: (a) were older and more likely of minority status; (b) had poorer medication adherence and glycemic control; and (c) exhibited higher rates of hypoglycemia, comorbidities and healthcare utilization. This represents the first successful attempt to employ natural language processing to estimate health literacy. Literacy profiles can offer an automated and economical way to identify patients with limited health literacy and greater vulnerability to poor health outcomes.

Suppressing irrelevant information: knowledge activation or inhibition?

In 3 experiments, the authors examined the role of knowledge activation in the suppression of contextually irrelevant meanings for ambiguous homographs. In Experiments 1 and 2, participants with greater baseball knowledge, regardless of reading skill, more quickly suppressed the irrelevant meaning of ambiguous words in baseball-related, but not general-topic, sentences. Experiment 3 demonstrated that participants with greater general knowledge, regardless of reading skill, more quickly suppressed the irrelevant meaning of the ambiguous words in general-topic sentences. As predicted by D. S. McNamara's (1997) knowledge-based account of suppression, ambiguity effects are influenced by greater activation of knowledge related to the intended meaning of the homograph. These results challenge inhibition (e.g. M. A. Gernsbacher, K. R. Varner. & M. Faust, 1990) as the sole mechanism responsible for the suppression of irrelevant information.

The nature of mind wandering during reading varies with the cognitive control demands of the reading strategy.

Prior studies of mind wandering find the default network active during mind wandering, but these studies have yielded mixed results concerning the role of cognitive control brain regions during mind wandering. Mind wandering often interferes with reading comprehension, and prior neuroimaging studies of discourse comprehension and strategic reading comprehension have shown that there are at least two networks of brain regions that support strategic discourse comprehension: a domain-general control network and a network of regions supporting coherence-building comprehension processes. The present study was designed to further examine the neural correlates of mind wandering by examining mind wandering during strategic reading comprehension. Participants provided ratings of mind wandering frequency that were used to investigate interactions between the strategy being performed and brain regions whose activation was modulated by wind wandering. The results support prior findings showing that cognitive control regions are at times more active during mind wandering than during a task with low control demands, such as rereading. This result provides an initial examination of the neural correlates of mind wandering during discourse comprehension and shows that the processes being engaged by the primary task need to be considered when studying mind wandering. The results also replicate, in a different learning domain, prior findings of key brain areas associated with different reading strategies.