Caitlin Mills

Interactions between the default network and dorsal attention network vary across default subsystems, time, and cognitive states

ABSTRACT 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.

What Emotions Do Novices Experience during Their First Computer Programming Learning Session

We conducted a study to track the emotions, their behavioral correlates, and relationship with performance when novice programmers learned the basics of computer programming in the Python language. Twenty-nine participants without prior programming experience completed the study, which consisted of a 25 minute scaffolding phase (with explanations and hints) and a 15 minute fadeout phase (no explanations or hints) with a computerized learning environment. Emotional states were tracked via retrospective self-reports in which learners viewed videos of their faces and computer screens recorded during the learning session and made judgments about their emotions at approximately 100 points. The results indicated that flow/engaged (23%), confusion (22%), frustration (14%), and boredom (12%) were the major emotions students experienced, while curiosity, happiness, anxiety, surprise, anger, disgust, fear, and sadness were comparatively rare. The emotions varied as a function of instructional scaffolds and were systematically linked to different student behaviors (idling, constructing code, running code). Boredom, flow/engaged, and confusion were also correlated with performance outcomes. Implications of our findings for affect-sensitive learning interventions are discussed.

To Quit or Not to Quit: Predicting Future Behavioral Disengagement from Reading Patterns

This research predicted behavioral disengagement using quitting behaviors while learning from instructional texts. Supervised machine learning algorithms were used to predict if students would quit an upcoming text by analyzing reading behaviors observed in previous texts. Behavioral disengagement quitting at any point during the text was predicted with an accuracy of 76.5% 48% above chance, before students even began engaging with the text. We also predicted if a student would quit reading on the first page of a text or continue reading past the first page with an accuracy of 88.5% 29% above chance, as well as if students would quit sometime after the first page with an accuracy of 81.4% 51% greater than chance. Both actual quits and predicted quits were significantly related to learning, which provides some evidence for the predictive validity of our model. Implications and future work related to ITSs are also discussed.

On the Validity of the Autobiographical Emotional Memory Task for Emotion Induction

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.

On the influence of re-reading on mind wandering

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.

What Makes Learning Fun? Exploring the Influence of Choice and Difficulty on Mind Wandering and Engagement during Learning

Maintaining learner engagement is critical for all types of learning technologies. This study investigated how choice over a learning topic and the difficulty of the materials influenced mind wandering, engagement, and learning during a computerized learning task. 59 participants were randomly assigned to a text difficulty and choice condition (i.e., self-selected or experimenter-selected topic) and measures of mind wandering and engagement were collected during learning. Participants who studied the difficult version of the texts reported significantly higher rates of mind wandering (d = .41) and lower arousal both during (d = .52) and after the learning session (d = .48). Mind wandering and arousal were not affected by choice. However, participants who were assigned to study the topic they selected reported significantly more positive valence during (d = .57) but not after learning. These participants also scored substantially higher on a subsequent knowledge test (d = 1.27). These results suggest that choice and text difficulty differentially impact mind wandering, engagement, and learning and provide important considerations for the design of ITSs and serious games with a reading component.

Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks

Significance The frontoparietal control network (FPCN) contributes to executive control, the ability to deliberately guide action based on goals. While the FPCN is often viewed as a unitary domain general system, it is possible that the FPCN contains a fine-grained internal organization, with separate zones involved in different types of executive control. Here, we use graph theory and meta-analytic functional profiling to demonstrate that the FPCN is composed of two separate subsystems: FPCNA is connected to the default network and is involved in the regulation of introspective processes, whereas FPCNB is connected to the dorsal attention network and is involved in the regulation of perceptual attention. These findings offer a distinct perspective on the systems-level circuitry underlying cognitive control. 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.

Mind wandering during film comprehension: The role of prior knowledge and situational interest

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.

Mind Wandering During Learning with an Intelligent Tutoring System

Mind wandering (zoning out) can be detrimental to learning outcomes in a host of educational activities, from reading to watching video lectures, yet it has received little attention in the field of intelligent tutoring systems (ITS). In the current study, participants self-reported mind wandering during a learning session with Guru, a dialogue-based ITS for biology. On average, participants interacted with Guru for 22 minutes and reported an average of 11.5 instances of mind wandering, or one instance every two minutes. The frequency of mind wandering was compared across five different phases of Guru (Common-Ground-Building Instruction, Intermittent Summary, Concept Map, Scaffolded Dialogue, and Cloze task), each requiring different learning strategies. The rate of mind wandering per minute was highest during the Common-Ground-Building Instruction and Scaffolded Dialogue phases of Guru. Importantly, there was significant negative correlation between mind wandering and learning, highlighting the need to address this phenomena during learning with ITSs.

Automatic evaluation of learner self-explanations and erroneous responses for dialogue-based ITSs

Self-explanations (SE) are an effective method to promote learning because they can help students identify gaps and inconsistencies in their knowledge and revise their faulty mental models. Given this potential, it is beneficial for intelligent tutoring systems (ITS) to promote SEs and adaptively respond based on SE quality. We developed and evaluated classification models using combinations of SE content (e.g., inverse weighted word-overlap) and contextual cues (e.g., SE response time, topic being discussed). SEs were coded based on correctness and presence of different types of errors. We achieved some success at classifying SE quality using SE content and context. For correct vs. incorrect discrimination, context-based features were more effective, whereas content-based features were more effective when classifying different types of errors. Implications for automatic assessment of learner SEs by ITSs are discussed.