Strategies for remediating the impact of math anxiety on high school math performance.

Students with math anxiety experience excessive levels of negative emotion, including intrusive and distracting thoughts, when attempting to learn about math or complete a math assignment. Consequently, math anxiety is associated with maladaptive study skills, such as avoidance of homework and test preparation, creating significant impediments for students to fulfill their potential in math classes. To combat the impact of math anxiety on academic performance, we introduced two classroom-based interventions across two samples of high school math students: one intervention focused on emotion regulation (ER) using cognitive reappraisal, a technique for reframing an anxious situation, and the other intervention encouraged students to improve their study habits. The Study Skills (SS) intervention was associated with increased grades for highly anxious students during the intervention period, whereas the ER intervention was less efficacious in countering anxiety-related decreases in grade performance. The SS intervention encouraged highly math-anxious students to incorporate self-testing and overcome avoidant behaviors, increasing academic performance and ameliorating performance deficits associated with increased anxiety that were observed in both groups prior to intervention, and that persisted in the ER group. Notably, the benefits observed for the SS group extended to the post-intervention quarter, indicating the potential lasting effects of this intervention. These results support the hypothesis that using better study strategies and encouraging more frequent engagement with math resources would help highly-anxious students habituate to their math anxiety and ameliorate the negative effects of anxiety on performance, ultimately increasing their math comprehension and academic achievement.

Relational and lexical similarity in analogical reasoning and recognition memory: Behavioral evidence and computational evaluation.

We examined the role of different types of similarity in both analogical reasoning and recognition memory. On recognition tasks, people more often falsely report having seen a recombined word pair (e.g., flower: garden) if it instantiates the same semantic relation (e.g., is a part of) as a studied word pair (e.g., house: town). This phenomenon, termed relational luring, has been interpreted as evidence that explicit relation representations-known to play a central role in analogical reasoning-also impact episodic memory. We replicate and extend previous studies, showing that relation-based false alarms in recognition memory occur after participants encode word pairs either by making relatedness judgments about individual words presented sequentially, or by evaluating analogies between pairs of word pairs. To test alternative explanations of relational luring, we implemented an established model of recognition memory, the Generalized Context Model (GCM). Within this basic framework, we compared representations of word pairs based on similarities derived either from explicit relations or from lexical semantics (i.e., individual word meanings). In two experiments on recognition memory, best-fitting values of GCM parameters enabled both similarity models (even the model based solely on lexical semantics) to predict relational luring with comparable accuracy. However, the model based on explicit relations proved more robust to parameter variations than that based on lexical similarity. We found this same pattern of modeling results when applying GCM to an independent set of data reported by Popov, Hristova, and Anders (2017). In accord with previous work, we also found that explicit relation representations are necessary for modeling analogical reasoning. Our findings support the possibility that explicit relations, which are central to analogical reasoning, also play an important role in episodic memory.

Transfer from spatial education to verbal reasoning and prediction of transfer from learning-related neural change.

Current debate surrounds the promise of neuroscience for education, including whether learning-related neural changes can predict learning transfer better than traditional performance-based learning assessments. Longstanding debate in philosophy and psychology concerns the proposition that spatial processes underlie seemingly nonspatial/verbal reasoning (mental model theory). If so, education that fosters spatial cognition might improve verbal reasoning. Here, in a quasi-experimental design in real-world STEM classrooms, a curriculum devised to foster spatial cognition yielded transfer to improved verbal reasoning. Further indicating a spatial basis for verbal transfer, students' spatial cognition gains predicted and mediated their reasoning improvement. Longitudinal fMRI detected learning-related changes in neural activity, connectivity, and representational similarity in spatial cognition-implicated regions. Neural changes predicted and mediated learning transfer. Ensemble modeling demonstrated better prediction of transfer from neural change than from traditional measures (tests and grades). Results support in-school "spatial education" and suggest that neural change can inform future development of transferable curricula.

School Climate, Cortical Structure, and Socioemotional Functioning: Associations across Family Income Levels.

School climates are important for children's socioemotional development and may also serve as protective factors in the context of adversity. Nevertheless, little is known about the potential neural mechanisms of such associations, as there has been limited research concerning the relation between school climate and brain structure, particularly for brain regions relevant for mental health and socioemotional functioning. Moreover, it remains unclear whether the role of school climate differs depending on children's socioeconomic status. We addressed these questions in baseline data for 9- to 10-year-olds from the Adolescent Brain and Cognitive Development study (analytic sample for socioemotional outcomes, n = 8887), conducted at 21 sites across the United States. Cortical thickness, cortical surface area, and subcortical volume were derived from T1-weighted brain magnetic resonance imaging. School climate was measured by youth report, and socioemotional functioning was measured by both youth and parent report. A positive school climate and higher family income were associated with lower internalizing and externalizing symptoms, with no evidence of moderation. There were no associations between school climate and cortical thickness or subcortical volume, although family income was positively associated with hippocampal volume. For cortical surface area, however, there was both a positive association with family income and moderation: There was an interaction between school climate and income for total cortical surface area and locally in the lateral orbitofrontal cortex. In all cases, there was an unexpected negative association between school climate and cortical surface area in the lower-income group. Consequently, although the school climate appears to be related to better socioemotional function for all youth, findings suggest that the association between a positive school environment and brain structure only emerges in the context of socioeconomic stress and adversity. Longitudinal data are needed to understand the role of these neural differences in socioemotional functioning over time.

The Association Between Emotion Regulation, Physiological Arousal, and Performance in Math Anxiety.

Emotion regulation (ER) strategies may reduce the negative relationship between math anxiety and mathematics accuracy, but different strategies may differ in their effectiveness. We recorded electrodermal activity (EDA) to examine the effect of physiological arousal on performance during different applied ER strategies. We explored how ER strategies might affect the decreases in accuracy attributed to physiological arousal in high math anxious (HMA) individuals. Participants were instructed to use cognitive reappraisal (CR), expressive suppression (ES), or a "business as usual" strategy. During the ES condition, HMA individuals showed decreases in math accuracy associated with increased EDA, compared to low math anxious (LMA) individuals. For both HMA and LMA groups, CR reduced the association between physiological arousal and math accuracy, such that even elevated physiological arousal levels no longer had a negative association with math accuracy. These results show that CR provides a promising technique for ameliorating the negative relationship between math anxiety and math accuracy.

Anxiety, not regulation tendency, predicts how individuals regulate in the laboratory: An exploratory comparison of self-report and psychophysiology.

Anxiety influences how individuals experience and regulate emotions in a variety of ways. For example, individuals with lower anxiety tend to cognitively reframe (reappraise) negative emotion and those with higher anxiety tend to suppress negative emotion. Research has also investigated these individual differences with psychophysiology. These lines of research assume coherence between how individuals regulate outside the laboratory, typically measured with self-report, and how they regulate during an experiment. Indeed, performance during experiments is interpreted as an indication of future behavior outside the laboratory, yet this relationship is seldom directly explored. To address this gap, we computed psychophysiological profiles of uninstructed (natural) regulation in the laboratory and explored the coherence between these profiles and a) self-reported anxiety and b) self-reported regulation tendency. Participants viewed negative images and were instructed to reappraise, suppress or naturally engage. Electrodermal and facial electromyography signals were recorded to compute a multivariate psychophysiological profile of regulation. Participants with lower anxiety exhibited similar profiles when naturally regulating and following instructions to reappraise, suggesting they naturally reappraised more. Participants with higher anxiety exhibited similar profiles when naturally regulating and following instructions to suppress, suggesting they naturally suppressed more. However, there was no association between self-reported reappraisal or suppression tendency and psychophysiology. These exploratory results indicate that anxiety, but not regulation tendency, predicts how individuals regulate emotion in the laboratory. These findings suggest that how individuals report regulating in the real world does not map on to how they regulate in the laboratory. Taken together, this underscores the importance of developing emotion-regulation interventions and paradigms that more closely align to and predict real-world outcomes.

Individual Differences in the Neural Localization of Relational Networks of Semantic Concepts.

Semantic concepts relate to each other to varying degrees to form a network of zero-order relations, and these zero-order relations serve as input into networks of general relation types as well as higher order relations. Previous work has studied the neural mapping of semantic concepts across domains, although much work remains to be done to understand how the localization and structure of those architectures differ depending on various individual differences in attentional bias toward different content presentation formats. Using an item-wise model of semantic distance of zero-order relations (Word2vec) between stimuli (presented both in word and picture forms), we used representational similarity analysis to identify individual differences in the neural localization of semantic concepts and how those localization differences can be predicted by individual variance in the degree to which individuals attend to word information instead of pictures. Importantly, there were no reliable representations of this zero-order semantic relational network when looking at the full group, and it was only through considering individual differences that a stable localization difference became evident. These results indicate that individual differences in the degree to which a person habitually attends to word information instead of picture information substantially affects the neural localization of zero-order semantic representations.

Neural evidence for cognitive reappraisal as a strategy to alleviate the effects of math anxiety.

Math anxiety (MA) describes feelings of tension, apprehension and fear that interfere with math performance. High MA (HMA) is correlated with negative consequences, including lower math grades, and ultimately an avoidance of quantitative careers. Given these adverse consequences, it is essential to explore effective intervention strategies to reduce MA. In the present functional magnetic resonance imaging (fMRI) study, we investigated the efficacy of cognitive reappraisal as a strategy to alleviate the effects of MA. Cognitive reappraisal, an emotion regulation strategy, has been shown to decrease negative affect and amygdala responsivity to stimuli that elicit negative emotion. We compared a reappraisal strategy to participants' natural strategy for solving math problems and analogies. We found that HMA individuals showed an increase in accuracy and a decrease in negative affect during the reappraisal condition as compared to the control condition. During math reappraise trials, increased activity in a network of regions associated with arithmetic correlated with improved performance for HMA individuals. These results suggest that increased engagement of arithmetic regions underlies the performance increases we identify in HMA students when they use reappraisal to augment their math performance. Overall, cognitive reappraisal is a promising strategy for enhancing math performance and reducing anxiety in math anxious individuals.

Developing a neurally informed ontology of creativity measurement.

A central challenge for creativity research-as for all areas of experimental psychology and cognitive neuroscience-is to establish a mapping between constructs and measures (i.e., identifying a set of tasks that best captures a set of creative abilities). A related challenge is to achieve greater consistency in the measures used by different researchers; inconsistent measurement hinders progress toward shared understanding of cognitive and neural components of creativity. New resources for aggregating neuroimaging data, and the emergence of methods for identifying structure in multivariate data, present the potential for new approaches to address these challenges. Identifying meta-analytic structure (i.e., similarity) in neural activity associated with creativity tasks might help identify subsets of these tasks that best reflect the similarity structure of creativity-relevant constructs. Here, we demonstrated initial proof-of-concept for such an approach. To build a model of similarity between creativity-relevant constructs, we first surveyed creativity researchers. Next, we used NeuroSynth meta-analytic software to generate maps of neural activity robustly associated with tasks intended to measure the same set of creativity-relevant constructs. A representational similarity analysis-based approach identified particular constructs-and particular tasks intended to measure those constructs-that positively or negatively impacted the model fit. This approach points the way to identifying optimal sets of tasks to capture elements of creativity (i.e., dimensions of similarity space among creativity constructs), and has long-term potential to meaningfully advance the ontological development of creativity research with the rapid growth of creativity neuroscience. Because it relies on neuroimaging meta-analysis, this approach has more immediate potential to inform longer-established fields for which more extensive sets of neuroimaging data are already available.

Individual differences in white and grey matter structure associated with verbal habits of thought.

Modality specific encoding habits account for a significant portion of individual differences reflected in functional activation during cognitive processing. Yet, little is known about how these habits of thought influence long-term structural changes in the brain. Traditionally, habits of thought have been assessed using self-report questionnaires such as the visualizer-verbalizer questionnaire. Here, rather than relying on subjective reports, we measured habits of thought using a novel behavioral task assessing attentional biases toward picture and word stimuli. Hypothesizing that verbal habits of thought are reflected in the structural integrity of white matter tracts and cortical regions of interest, we used diffusion tensor imaging and volumetric analyses to assess this prediction. Using a whole-brain approach, we show that word bias is associated with increased volume in several bilateral language regions, in both white and grey matter parcels. Additionally, connectivity within white matter tracts within an a priori speech production network increased as a function of word bias. These results demonstrate long-term structural and morphological differences associated with verbal habits of thought.

Using the force: STEM knowledge and experience construct shared neural representations of engineering concepts.

How does STEM knowledge learned in school change students' brains? Using fMRI, we presented photographs of real-world structures to engineering students with classroom-based knowledge and hands-on lab experience, examining how their brain activity differentiated them from their "novice" peers not pursuing engineering degrees. A data-driven MVPA and machine-learning approach revealed that neural response patterns of engineering students were convergent with each other and distinct from novices' when considering physical forces acting on the structures. Furthermore, informational network analysis demonstrated that the distinct neural response patterns of engineering students reflected relevant concept knowledge: learned categories of mechanical structures. Information about mechanical categories was predominantly represented in bilateral anterior ventral occipitotemporal regions. Importantly, mechanical categories were not explicitly referenced in the experiment, nor does visual similarity between stimuli account for mechanical category distinctions. The results demonstrate how learning abstract STEM concepts in the classroom influences neural representations of objects in the world.

Math anxiety and executive function: Neural influences of task switching on arithmetic processing.

Math anxiety (MA) is associated with negative thoughts and emotions when encountering mathematics, often resulting in under-performance on math tasks. One hypothesized mechanism by which MA affects performance is through anxiety-related increases in working memory (WM) load, diverting resources away from mathematical computations. We examined whether this effect is specific to WM or whether the impact of MA extends to an overall depletion of executive function (EF) resources. In this fMRI experiment, we manipulated two separate factors known to impact EF demands-task-switching (TS) and increased WM load-in order to evaluate how MA relates to behavioral performance and neural activity related to mathematical calculations. Relative to a difficult non-math task (analogies), we observed MA-related deficits in math performance and reduced neural activity in a network of regions in the brain associated with arithmetic processing. In response to TS demands, higher levels of math anxiety were associated with a pattern of avoidance and disengagement. When switching from the control task, high math anxiety (HMA) was associated with disengagement from math trials, speeding through these trials, and exhibiting reduced neural activity in regions associated with arithmetic processing. The effects of math anxiety and WM were most pronounced at the lowest levels of WM load. Overall, the results of this study indicate that the effects of MA are broader than previously demonstrated and provide further insight into how EF deficits in MA might impact recruitment of neural resources that are important for successful math computations.

Mental models use common neural spatial structure for spatial and abstract content.

Mental models provide a cognitive framework allowing for spatially organizing information while reasoning about the world. However, transitive reasoning studies often rely on perception of stimuli that contain visible spatial features, allowing the possibility that associated neural representations are specific to inherently spatial content. Here, we test the hypothesis that neural representations of mental models generated through transitive reasoning rely on a frontoparietal network irrespective of the spatial nature of the stimulus content. Content within three models ranges from expressly visuospatial to abstract. All mental models participants generated were based on inferred relationships never directly observed. Here, using multivariate representational similarity analysis, we show that patterns representative of mental models were revealed in both superior parietal lobule and anterior prefrontal cortex and converged across stimulus types. These results support the conclusion that, independent of content, transitive reasoning using mental models relies on neural mechanisms associated with spatial cognition.

Individual differences in encoded neural representations within cortical speech production network.

When two individuals view the same item, they do not necessarily perceive an item in the same way. If an individual is presented with a stimulus to be recalled later, the information that is encoded is dependent on the features of the stimulus to which one attends. Past studies have shown that, on the group level, verbal and visual information (e.g., words and pictures) are encoded in disparate regions of the brain. However, this account conflates external and internal representational formats, and it also neglects individual differences in attention. In this study, we examined neural and behavioral patterns associated with individual differences in attention to verbal representations-both external and internal. We found that the encoded neural representation of semantic content (meaningful words and pictures) varied as a function of individual differences in verbal attention, independent of the stimulus presentation format. Individuals who demonstrated an attentional bias toward words showed similar multivariate BOLD activity patterns within an a priori speech production network when encoding object names as when encoding pictures of objects. This result indicates that these individuals encode both words and pictures verbally. These effects were not found for non-semantic stimuli (pronounceable non-words and nonsense pictures). Importantly, as expected, no individual differences in neural representation were found in a separate network of regions known to process semantic content independent of format. These results highlight inter-individual divergence and convergence in internal representations of encoded semantic content. SIGNIFICANCE STATEMENT: This study shows how tendencies to attend to word representations is associated with individual differences in encoded neural representations. Individuals who selectively attend to words instead of pictures process semantically meaningful information in language regions of the brain, regardless of whether the information was originally presented as a word or a picture. Though all participants encoded words and pictures similarly in regions that are known to represent domain-general semantic information, only the individuals who were biased towards word representations additionally processed both words and pictures in modality-specific verbal regions. These results demonstrate both the convergence and divergence between individuals that occurs during encoding of meaningful information.

Decoding individual differences in STEM learning from functional MRI data.

Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural 'score' to complement traditional scores of an individual's conceptual understanding. Using a novel data-driven multivariate neuroimaging approach-informational network analysis-we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science.

Putting the pieces together: Generating a novel representational space through deductive reasoning.

How does the brain represent a newly-learned mental model? Representational similarity analysis (RSA) has revealed the neural basis of common representational spaces learned early in development, such as categories of natural kinds. This study uses RSA to examine the neural implementation of a newly-learned mental model-i.e., a representational space created through deductive reasoning-and study the structure of previously found parietal activity in reasoning tasks. Specifically, all the information in this mental model could only be obtained through abstract transitive reasoning, as there were no predictive differences between observable features in the stimuli, and stimuli were counterbalanced across participants. Participants were shown unfamiliar face portraits paired with names and asked to learn about the height of each person pictured in the portraits through comparison to other individuals in the set. Participants learned the relative heights only of adjacent pairs in the set and then used transitive reasoning to generate a linear ranking of heights (e.g., "Matthew is taller than Thomas; Thomas is taller than Andrew; therefore Matthew is taller than Andrew"). During fMRI, participants recalled the approximate height of each individual based on these inferences. Using a predictive model based on the relative heights of the set of individuals, RSA revealed three brain regions in the right hemisphere that encoded this newly-learned representational space, located within the intraparietal sulcus, precuneus, and inferior frontal gyrus. These findings demonstrate the value of RSA for analyzing structure within patterns of activity and support theories asserting that logical reasoning recruits spatial processing mechanisms.

The Academic Anxiety Inventory: Evidence for Dissociable Patterns of Anxiety Related to Math and Other Sources of Academic Stress.

Anxiety about mathematics can have detrimental effects on performance and understanding, yet little research has investigated how math anxiety is related to other types of anxiety. Here we develop the Academic Anxiety Inventory (AAI), an efficient and valid self-report measure designed to test math anxiety, as well as differentiate anxiety associated with mathematics from other contributions of anxiety across various academic domains. In Study 1, we isolated items that independently measure each domain of anxiety, reducing the overlapping variance between math anxiety and other constructs, and determining which components can or cannot be differentiated. Studies 2 and 3 demonstrate that the AAI is consistent and reliable for undergraduate and adolescent populations. In Study 3, anxiety-related performance deficits in a high school math class were associated with scores the AAI-Math subscale. In Study 4, the AAI-Math subscale was associated with perceptions of increased mathematical complexity, decreased estimations of accuracy, and increased negative emotion when participants viewed mathematical expressions. Across four studies, we demonstrate the AAI is a reliable and valid measure of math anxiety and other domains of academic anxiety, providing an efficient questionnaire to determine areas in which students may require extra support in order to reach their full potential.

Avoiding math on a rapid timescale: Emotional responsivity and anxious attention in math anxiety.

Math anxiety (MA) is characterized by negative feelings towards mathematics, resulting in avoidance of math classes and of careers that rely on mathematical skills. Focused on a long timescale, this research may miss important cognitive and affective processes that operate moment-to-moment, changing rapid reactions even when a student simply sees a math problem. Here, using fMRI with an attentional deployment paradigm, we show that MA influences rapid spontaneous emotional and attentional responses to mathematical stimuli upon brief presentation. Critically, participants viewed but did not attempt to solve the problems. Indicating increased threat reactivity to even brief presentations of math problems, increased MA was associated with increased amygdala response during math viewing trials. Functionally and anatomically defined amygdala ROIs yielded similar results, indicating robustness of the finding. Similar to the pattern of vigilance and avoidance observed in specific phobia, behavioral results of the attentional paradigm demonstrated that MA is associated with attentional disengagement for mathematical symbols. This attentional avoidance is specific to math stimuli; when viewing negatively-valenced images, MA is correlated with attentional engagement, similar to other forms of anxiety. These results indicate that even brief exposure to mathematics triggers a neural response related to threat avoidance in highly MA individuals.

Grounded understanding of abstract concepts: The case of STEM learning.

Characterizing the neural implementation of abstract conceptual representations has long been a contentious topic in cognitive science. At the heart of the debate is whether the "sensorimotor" machinery of the brain plays a central role in representing concepts, or whether the involvement of these perceptual and motor regions is merely peripheral or epiphenomenal. The domain of science, technology, engineering, and mathematics (STEM) learning provides an important proving ground for sensorimotor (or grounded) theories of cognition, as concepts in science and engineering courses are often taught through laboratory-based and other hands-on methodologies. In this review of the literature, we examine evidence suggesting that sensorimotor processes strengthen learning associated with the abstract concepts central to STEM pedagogy. After considering how contemporary theories have defined abstraction in the context of semantic knowledge, we propose our own explanation for how body-centered information, as computed in sensorimotor brain regions and visuomotor association cortex, can form a useful foundation upon which to build an understanding of abstract scientific concepts, such as mechanical force. Drawing from theories in cognitive neuroscience, we then explore models elucidating the neural mechanisms involved in grounding intangible concepts, including Hebbian learning, predictive coding, and neuronal recycling. Empirical data on STEM learning through hands-on instruction are considered in light of these neural models. We conclude the review by proposing three distinct ways in which the field of cognitive neuroscience can contribute to STEM learning by bolstering our understanding of how the brain instantiates abstract concepts in an embodied fashion.

Verbal and visual cognition: Individual differences in the lab, in the brain, and in the classroom.

In many ways, individuals vary in their thought processes, and in their cognitive strengths and weaknesses. Among the findings revealed by individual differences research, one major dividing line highlighted recurrently by decades of experimental studies is that between linguistically-mediated cognitive operations (verbal cognition), versus cognition, which primarily operates on visual - or visuospatial - representations (visual cognition). In this article, we review findings from three research areas-cognitive abilities, working memory, and task strategies-focusing on individual differences in verbal and visual cognition. In each area we highlight behavioral, neuroimaging, and classroom-based findings, bridging the perspectives of these different methodologies.