Reflections on the past two decades of Mind, Brain, and Education

In the early 2000s, Kurt Fischer and colleagues founded the Mind, Brain, and Education (MBE) field (Blake & Gardner, 2007; Fischer & Bidell, 2006), including a flagship journal, society (International Mind, Brain, Education Society [IMBES]), and a master's degree program at the Harvard Graduate School of Education (Harvard). The MBE program was the first-of-its-kind, focused on the intersection of neurobiology, psychology, and educational research and practice (Blake & Gardner, 2007; Fischer, 2009). Between its first cohort in 2004 and its final cohort in 2022, the program graduated 668 students from around the world (see Figure 1). Contemporaneously, scholars developed MBE or related Educational Neuroscience initiatives in several US states, Canada, the United Kingdom, Austria, The Netherlands, China, Israel, across Latin America, and other locations around the world. A central question in education revolves around what instructional approaches work for whom and under which conditions. MBE seeks to address these inquiries by leveraging cross-disciplinary methods and frameworks with rigorous scientific precision. Yet, the progression of scientific knowledge is often limited by the tools at hand. Herein lies one of the most significant contributions of MBE: It acts as a catalyst for methodological innovation, highlighting the limitations of current scientific approaches in tackling some of the most pressing educational challenges. The need to model the complex probabilistic relations that underlie educational response propelled innovation in the application of sophisticated computational techniques to highly dimensional student data. To illustrate, in a recent study, we used machine learning (ML) to improve methods of prediction of instructional responses in first-grade students at risk for reading disabilities (Shangguan et al., 2023; Zhongkai et al., Under Review). Due to challenges such as incomplete data from young participants, our initial ML models struggled to forecast student outcomes accurately. It wasn't until we integrated methods adapted from the fields of natural language processing and computer vision that we saw a significant uptick in accuracy. We trained our models to recognize hidden patterns between known and unknown data points, resulting in an enhanced out-of-sample prediction accuracy of 80%. In another example, innovative ML was applied to video recordings of math lessons to identify what teacher discourse features were important for supporting a positive learning mindset in students (Hunkins, Kelly, & D'Mello, 2022). In a different domain, the need for more ecologically valid neuroimaging to measure how cognitive and neural processes that underlie learning unfold in naturalistic settings, has facilitated the optimization of portable neuroimaging methods across a diverse range of contexts. For example, portable electroencephalography has been used to study student engagement during live classroom instruction (Davidesco, Matuk, Bevilacqua, Poeppel, & Dikker, 2021; Landi et al., 2019), and functional near-infrared spectroscopy has been used to document neural signatures of reading development in children growing in environments with a high risk of illiteracy, rural Côte d'Ivoire (Jasińska & Guei, 2018). The implementation of portable neuroimaging in these settings posed methodological challenges, and the response to those challenges fostered innovation. In-school EEG data collection is noisy, so the team has leveraged high-density EEG tools, which capture thousands of data points per second, combined with ML classifiers, to dissociate signal from noise. This allowed for a more precise characterization of individual differences in the neural substrates of reading as they unfold during instruction (Davidesco et al., 2021; Landi et al., 2019). To address the challenges of setting up a portable neuroimaging laboratory in low-resource contexts, researchers developed comprehensive protocols addressing problems such as high humidity and lack of Internet access (Jasińska & Guei, 2018). The advancement of these portable neuroimaging methods and the emergence of additional technologies, such as portable magnetic resonance imaging, can enable more equitable access to neuroimaging technologies around the world. Over the past 20 years, the MBE field has facilitated the expansion of empirical, translational, and policy-relevant work through a breadth of opportunities to bridge the mind, brain, and education sciences. Using mostly lab-based cognitive neuroscience approaches (but see Janssen et al., 2021), educational neuroscience researchers have examined the cognitive and neural bases of school-relevant skills. Not meant to impact the classroom directly, this research has elucidated the neurocognitive mechanisms that underlie learning, such as basic processes in numerical cognition (Pollack & Price, 2019, 2020), the relation between math and reading (Pollack et al., 2021; Pollack & Ashby, 2018), and the connection of both with executive functioning (Marks et al., 2023). Connecting to classroom learning, MBE work has advanced understandings of education challenges across levels of analysis. For example, MBE work has evidenced the deleterious biological and psychological effects of racial/ethnic discrimination and how they undermine student performance and opportunities to thrive (Levy, Heissel, Richeson, & Adam, 2016; Shonkoff, Slopen, & Williams, 2021), and translated research for myriad education stakeholders (The Learning Scientists, 2023; https://bold.expert/). MBE's interdisciplinary approach has also informed policy. A synthesis of convergent evidence from biology, psychology, and education shows that adolescents benefit from starting school later (Watson et al., 2017), which provided strong evidence for California's 2022 implementation of later middle and high school start times (SB-328 Pupil Attendance: School Start Time, 2019). Alongside the expansion of MBE research, additional training and career development opportunities support the field's expansion, including the 2017 establishment of the IMBES Trainee Board by early career scholars (Gotlieb et al., 2019). The international proliferation of programs and centers (e.g., University of Alabama, Vanderbilt University, Vrije Universiteit Amsterdam, University of Graz, East China Normal University, University College London) has made joining the community easier. I am particularly enthusiastic about the field's potential moving forward. MBE stakeholders can expand research-practice partnership opportunities, emphasize learning from educational practice, and further increase access and representation in the questions we ask and who may answer them. Educators have long been aware of a simple fact: sparking a child's curiosity will help them learn (Deci & Ryan, 1981). Developmental psychologists have suggested that children's insatiable wonder is a critical drive in cognition that supports intuitive theory building and scientific reasoning (Gopnik, 2016; Keil, 2022; Schulz, 2012). Robotics, ML, and artificial general intelligence researchers have begun to build models with “curiosity” rewards to bootstrap learning (see Haber, 2022 for recent review). Psychological and computational theories link it to our need to resolve uncertainty and fill knowledge gaps (Gottlieb, Oudeyer, Lopes, & Baranes, 2013; Loewenstein, 1994). But until recently, formalizing exactly what curiosity is and how it relates directly to learning and the brain has remained less well understood (Kidd & Hayden, 2015). One of the most exciting developments in MBE has been new brain measures that might “tap into” and even provide explanation for the function and mechanisms underlying our drive to resolve uncertainty. In particular, EEG studies (measuring the electrical signals in the brain) have revealed several important connections between the theta oscillation brain signal and behavior: theta oscillations are involved in memory formation and reward; they are predictive of learning success; and, even in infancy, they are heightened preceding events that are predicted to reveal new information (see Begus & Bonawitz, 2020 for review). Such measures allow us to decouple previously confounded measures of curiosity and learning, such as the child's exploration (which can be influenced by factors beyond curiosity) and information gathered (which affects the inferences that can be drawn). We can then also understand how the child's current beliefs and preceding experiences (e.g., established knowledge, goals, and rapport with a teacher, see Bonawitz & Shafto, 2016) affect a learner's expectation of information and curiosity. And we can study more precisely how brain measures that might reflect curiosity then relate to attention, memory, and learning. Such explanatory models will allow us to understand better how to support curiosity in all learners. It is a very exciting time, as we are just at the precipice of building causal, theory-driven models of the role of curiosity in the mind and brain for education. Since the early 2000s, the field of MBE has continued to provide novel insights and perspectives across research-practice-clinic settings. The power of this research has been showcased in how we conceptualize learning differences, reading acquisition, and brain plasticity. With a focus on learning disabilities or difficulties, I draw on the example of dyslexia, a common reading disability that impacts the accuracy and/or fluency of single-word reading (Lyon et al., 2001; Lyon et al., 2003). One of the earliest contributions of cognitive neuroscience research was in establishing that dyslexia was indeed “neurobiological in origin,” in that brain signatures for reading differ between groups of readers with and without dyslexia. Although seemingly elemental, this insight continues to act as a framework essential in reframing attribution for the difficulties faced by struggling readers (i.e., it was not based on laziness, limited aptitude, etc. as many continue to believe; Cortiella & Horowitz, 2014; Horowitz, Rawe, & Whittaker, 2017). Furthermore, the accumulated evidence that the “reading brain” is plastic, and changes in ways specific to the reading programs used (Eden et al., 2004; Yoncheva, Wise, & McCandliss, 2015), has redoubled the importance of providing evidence-based reading instruction to developing readers. MBE research has also converged with theories of learning (e.g., dynamic skills theory; Fischer & Bidell, 2006) to reveal that skill acquisition is dynamic, with the impact of reading instruction differing by student and environmental characteristics, as well as by academic calendar phases (i.e., during summer vacation; Romeo et al., 2018; Meisler et al., 2023). Insights from studies on reading abilities among individuals with distinct neural architecture (e.g., relying on a single hemisphere as a consequence of epilepsy-related surgery) have likewise emphasized the resilience of students and the multiple pathways to reading progress (Christodoulou et al., 2021; Katzir, Christodoulou, & DeBode, 2017). Across these types of contributions, MBE has advanced how we conceptualize learning and teaching. Looking ahead, MBE can harness the vast foundational knowledge now established to explore more complex and nuanced studies of children in contexts (e.g., examining the interactions of who a child is with what is being asked and in what context). Another potential for innovation is advancing toward MBE research that shifts away from group-based inquiries to individual differences models. Most importantly, MBE research will matter in the future when it is disseminated effectively, in a manner inclusive and representative of our populations of interest, and meaningful for the communities we serve as scientists. When the MBE field started to emerge, it had a number of bold ideas and hypotheses. For example, people were excited about the prospect of using neuroimaging to diagnose learning differences, characterize individual learning trajectories, or as a tool to identify who will subsequently struggle with learning to read or math. Furthermore, there was excitement about a direct translation of neuroimaging findings into the classroom using “brain-based curricula.” Over the last 20 years, the field has learned a lot about neuroimaging methodology and its limitations and has also started to ask more refined questions and build relationships among the various stakeholders in the scientific and educational settings. As a result, evidence-based translational knowledge and scientifically trained changemakers have emerged (Solari et al., 2020; Ansari & Coch, 2006; Sheridan & McLaughlin, 2022; Thomas et al., 2019; Blakemore & Bunge, 2012; Gabrieli, 2016). In my opinion, the most significant impact of MBE on educational practice is at least twofold. First, we now understand that the development of academic skills and their precursors starts as early as in utero. Therefore, we need to understand a child's entire developmental trajectory (both generally and in key skills such as language and reading) in the context of variable environments to determine how, when, and where brains learn best (Ozernov-Palchik, Yu, Wang, & Gaab, 2016). Second, inspired by neuroscientific evidence that brain differences underlying disorders such as developmental dyslexia are present as early as et al., et al., 2017), the of is and early of children at risk for developmental of academic skills is starting to et al., 2021; & 2022; & 2022; & 2021; et al., 2016). in the United have for to identify children at risk for disabilities Education to continue to from a model to a to shifts that in other fields that to such as (e.g., for et al., The field of MBE with excitement as neuroimaging techniques allowed for and of the brain bases of learning, how that from through and how that in different of learners. 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