Problem solving is a complex skill engaging multi-stepped reasoning processes to find unknown solutions. The breadth of real-world contexts requiring problem solving is mirrored by a similarly broad, yet unfocused neuroimaging literature, and the domain-general or context-specific brain networks associated with problem solving are not well understood. To more fully characterize those brain networks, we performed activation likelihood estimation meta-analysis on 280 neuroimaging problem solving experiments reporting 3166 foci from 1919 individuals across 131 papers. The general map of problem solving revealed broad fronto-cingulo-parietal convergence, regions similarly identified when considering separate mathematical, verbal, and visuospatial problem solving domain-specific analyses. Conjunction analysis revealed a common network supporting problem solving across diverse contexts, and difference maps distinguished functionally-selective sub-networks specific to task type. Our results suggest cooperation between representationally specialized sub-network and whole-brain systems provide a neural basis for problem solving, with the core network contributing general purpose resources to perform cognitive operations and manage problem demand. Further characterization of cross-network dynamics could inform neuroeducational studies on problem solving skill development.

Abstract. We present results from the University of Colorado's Partnership for Informal Science Education in the Community (PISEC) in which university participants work in afterschool programs on inquiry-based activities with primary school children from populations typically under represented in science. This university-community partnership is designed to positively impact youth, university students, and the institutions that support them while improving children's attitudes towards and understanding of science. Children worked through circuit activities adapted from the Physics and Everyday Thinking (PET) curriculum and demonstrated increased understanding of content area as well as favorable beliefs about science.

Understanding how students learn is crucial for helping them succeed. We examined brain function in 107 undergraduate students during a task known to be challenging for many students—physics problem solving—to characterize the underlying neural mechanisms and determine how these support comprehension and proficiency. Further, we applied module analysis to response distributions, defining groups of students who answered by using similar physics conceptions, and probed for brain differences linked with different conceptual approaches. We found that integrated executive, attentional, visual motion, and default mode brain systems cooperate to achieve sequential and sustained physics-related cognition. While accuracy alone did not predict brain function, dissociable brain patterns were observed when students solved problems by using different physics conceptions, and increased success was linked to conceptual coherence. Our analyses demonstrate that episodic associations and control processes operate in tandem to support physics reasoning, offering potential insight to support student learning.

Modeling Instruction (MI) for University Physics is a curricular and pedagogical approach to active learning in introductory physics. A basic tenet of science is that it is a model-driven endeavor that involves building models, then validating, deploying, and ultimately revising them in an iterative fashion. MI was developed to provide students a facsimile in the university classroom of this foundational scientific practice. As a curriculum, MI employs conceptual scientific models as the basis for the course content, and thus learning in a MI classroom involves students appropriating scientific models for their own use. Over the last 10 years, substantial evidence has accumulated supporting MI's efficacy, including gains in conceptual understanding, odds of success, attitudes toward learning, self-efficacy, and social networks centered around physics learning. However, we still do not fully understand the mechanisms of how students learn physics and develop mental models of physical phenomena. Herein, we explore the hypothesis that the MI curriculum and pedagogy promotes student engagement via conceptual model building. This emphasis on conceptual model building, in turn, leads to improved knowledge organization and problem solving abilities that manifest as quantifiable functional brain changes that can be assessed with functional magnetic resonance imaging (fMRI). We conducted a neuroeducation study wherein students completed a physics reasoning task while undergoing fMRI scanning before (pre) and after (post) completing a MI introductory physics course. Preliminary results indicated that performance of the physics reasoning task was linked with increased brain activity notably in lateral prefrontal and parietal cortices that previously have been associated with attention, working memory, and problem solving, and are collectively referred to as the central executive network. Critically, assessment of changes in brain activity during the physics reasoning task from pre-vs. post-instruction identified increased activity after the course notably in the posterior cingulate cortex (a brain region previously linked with Brewe et al.Modeling Instruction and the Brain episodic memory and self-referential thought) and in the frontal poles (regions linked with learning). These preliminary outcomes highlight brain regions linked with physics reasoning and, critically, suggest that brain activity during physics reasoning is modifiable by thoughtfully designed curriculum and pedagogy.

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39 Modesto Maidique Campus 40 11200 SW 8 th Street 41 Miami, FL 33199 42 305.348.6737 (phone) 43 305.348.6700 (fax) 44 alaird@fiu.edu 45 1 ABSTRACT 47Understanding how students learn is crucial for helping them succeed. We examined brain function in 48 107 undergraduate students during a task known to be challenging for many students -physics problem 49 solving -to characterize underlying neural mechanisms and determine how these support 50 comprehension and proficiency. Further, we applied module analysis to response distributions, defining 51 groups of students who answered using similar physics conceptions, and probed for brain differences 52 linked with different conceptual approaches. We found integrated executive, attentional, visual motion, 53 and default mode brain systems cooperate to achieve sequential and sustained physics-related 54 cognition. While accuracy alone did not predict brain function, dissociable brain patterns were observed 55 when students solved problems using different physics conceptions, and increased success was linked to 56 conceptual coherence. Our analyses demonstrate that episodic associations and control processes 57 operate in tandem to support physics reasoning, offering potential insight to support student learning. 58 59 60 61

Academic performance relies, in part, on intelligence; however, intelligence quotient (IQ) is limited in predicting academic success. Furthermore, while the search for the biological seat of intelligence predates neuroscience itself, its findings remain conflicting. Here, we assess the interplay between IQ, academic performance, and brain connectivity with behavioral and functional MRI data collected from undergraduate students as they completed an active learning or lecture-based semester-long university physics course. IQ (i.e., full-scale WAIS scores) increased significantly pre-to post-instruction, were associated with physics knowledge and reasoning measures, but were unrelated to overall course grade. IQ was related to brain connectivity during physics-related cognition, but connectivity did not mediate IQ’s association with task performance. These relations depended on students’ sex and instructional environment, providing evidence that physics classroom environment and pedagogy may have a gendered influence on students’ performance. Discussion focuses on opportunities to improve physics reasoning skills for all students.

ObjectiveNeuroscientists have sought to identify the underlying neural systems supporting mental processes involved in social cognition. These processes allow us to interact and communicate with others, form social relationships, and navigate the social world. Through the use of NIMH’s Research Domain Criteria (RDoC) framework, we evaluated consensus among hundreds of studies that examined brain activity during social and social cognition tasks to elucidate, at a large scale, regions comprising the “social brain”. In addition, we examined convergence across tasks corresponding to the four RDoC social constructs, including Affiliation and Attachment, Social Communication, Perception and Understanding of Self, and Perception and Understanding of Others. We assessed activation patterns by performing a series of coordinate-based meta-analyses using the activation likelihood estimate (ALE) method. One meta-analysis was performed on whole-brain coordinates reported from 864 fMRI contrasts of healthy social processing (239 publications; 6,232 participants) using the NiMARE Python package. The meta-analysis of all social contrasts revealed consensus in the “social brain”, including the medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), temporoparietal junction (TPJ), bilateral insula, amygdala, fusiform gyrus, precuneus, and thalamus. Additionally, four separate RDoC-based meta-analyses revealed differential convergence associated with the four social constructs. These outcomes highlight the neural support underlying these social constructs and inform future research on alterations among neurotypical and atypical populations.