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Described Neural Connections Enhance Classroom Learning of Neuroanatomy

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Title: Described Neural Connections Enhance Classroom Learning of Neuroanatomy
Language: English
Authors: Nicholas C. Hindy (ORCID 0000-0003-1643-0251), Anthony J. Bishara (ORCID 0000-0002-7771-3565), John R. Pani
Source: Anatomical Sciences Education. 2025 18(7):642-656.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 15
Publication Date: 2025
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: 2315440
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Anatomy, Undergraduate Students, Neurosciences, Learning Processes, Feedback (Response), Memory, Diagnostic Tests, Brain Hemisphere Functions, Computer Assisted Instruction, Teaching Methods, Educational Benefits, Comparative Analysis, Textbooks, Retention (Psychology)
DOI: 10.1002/ase.70051
ISSN: 1935-9772
1935-9780
Abstract: Advances in brain imaging have led to a paradigm shift in neuroscience research, moving from focusing on individual brain structures to investigating neural networks and connections. However, neuroanatomy education still tends to concentrate on discrete brain regions. Two separate experiments in undergraduate neuroscience courses investigated whether incorporating neural connectivity into neuroanatomy education would enhance learning. Students in each experiment learned to identify brain structures through computer-based training sessions that provided text-based narrative feedback about neural connections, followed by final memory tests after a 1-month delay. The first experiment included 30 students and demonstrated a long-term memory benefit associated with described neural connections, showing a medium effect size (p = 0.01, d = 0.54) comparable to the established retrieval practice effect for enhancing long-term memory (p = 0.03, d = 0.47). The second experiment replicated the benefits of described neural connections with a small effect size (p = 0.005, d = 0.28) in a larger sample of 122 students across classrooms at two universities. Furthermore, students remembered the functional outcomes of neural connections from training (p < 0.001, d = 0.46), and this generalized to clinical applications (p = 0.009, d = 0.27). In contrast, categorizing brain areas without describing neural connections (as is commonly done in introductory neuroscience textbook chapters) did not benefit either memory or generalization. Findings demonstrate that leveraging the connectivity paradigm shift in neuroscience research can enhance neuroanatomy education. Emphasizing neural connections and their functional outcomes helps simplify neuroanatomy and improve understanding and retention.
Abstractor: As Provided
Notes: https://osf.io/dnu9x
Entry Date: 2025
Accession Number: EJ1475993
Database: ERIC
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  Value: &lt;anid&gt;AN0186371261;[8z8k]01jul.25;2025Jul07.02:54;v2.2.500&lt;/anid&gt; &lt;title id=&quot;AN0186371261-1&quot;&gt;Described neural connections enhance classroom learning of neuroanatomy&#160;&lt;/title&gt; &lt;p&gt;Advances in brain imaging have led to a paradigm shift in neuroscience research, moving from focusing on individual brain structures to investigating neural networks and connections. However, neuroanatomy education still tends to concentrate on discrete brain regions. Two separate experiments in undergraduate neuroscience courses investigated whether incorporating neural connectivity into neuroanatomy education would enhance learning. Students in each experiment learned to identify brain structures through computer‐based training sessions that provided text‐based narrative feedback about neural connections, followed by final memory tests after a 1‐month delay. The first experiment included 30 students and demonstrated a long‐term memory benefit associated with described neural connections, showing a medium effect size (p = 0.01, d = 0.54) comparable to the established retrieval practice effect for enhancing long‐term memory (p = 0.03, d = 0.47). The second experiment replicated the benefits of described neural connections with a small effect size (p = 0.005, d = 0.28) in a larger sample of 122 students across classrooms at two universities. Furthermore, students remembered the functional outcomes of neural connections from training (p &amp;lt; 0.001, d = 0.46), and this generalized to clinical applications (p = 0.009, d = 0.27). In contrast, categorizing brain areas without describing neural connections (as is commonly done in introductory neuroscience textbook chapters) did not benefit either memory or generalization. Findings demonstrate that leveraging the connectivity paradigm shift in neuroscience research can enhance neuroanatomy education. Emphasizing neural connections and their functional outcomes helps simplify neuroanatomy and improve understanding and retention.&lt;/p&gt; &lt;p&gt;Keywords: computer‐aided instruction; connectivity; neuroanatomy; systems neuroscience; undergraduate education&lt;/p&gt; &lt;hd id=&quot;AN0186371261-2&quot;&gt;INTRODUCTION&lt;/hd&gt; &lt;p&gt;Neuroanatomy is foundational to the rising number of undergraduates enrolled in neuroscience courses,[&lt;reflink idref=&quot;bib1&quot; id=&quot;ref1&quot;&gt;1&lt;/reflink&gt;] but students often view neuroanatomy as difficult, dry, and irrelevant.[[&lt;reflink idref=&quot;bib2&quot; id=&quot;ref2&quot;&gt;2&lt;/reflink&gt;], [&lt;reflink idref=&quot;bib4&quot; id=&quot;ref3&quot;&gt;4&lt;/reflink&gt;]] Poor engagement leads to quick forgetting of neuroanatomy material.[[&lt;reflink idref=&quot;bib6&quot; id=&quot;ref4&quot;&gt;6&lt;/reflink&gt;]] Traditionally, the introduction of neuroanatomy in undergraduate neuroscience courses has been structured around discrete brain areas, often emphasizing their phylogenetic development or isolated functions.[[&lt;reflink idref=&quot;bib8&quot; id=&quot;ref5&quot;&gt;8&lt;/reflink&gt;]] While systematic, this approach does not reflect the growing body of research that underscores the structural and functional networks within the brain.[[&lt;reflink idref=&quot;bib10&quot; id=&quot;ref6&quot;&gt;10&lt;/reflink&gt;]]&lt;/p&gt; &lt;p&gt;Recent advances in brain imaging and analysis have spurred a paradigm shift in neuroscience research from focusing on individual brain structures to investigating neural networks and connections.[&lt;reflink idref=&quot;bib12&quot; id=&quot;ref7&quot;&gt;12&lt;/reflink&gt;] Despite its potential, the pedagogical advantages of using neural connectivity as a teaching tool in neuroscience education have yet to be investigated. We aim to improve neuroanatomy instruction in undergraduate STEM education by using neural connectivity to associate brain structures with each other and with functional outcomes.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-3&quot;&gt;Neural connectivity in neuroscience research&lt;/hd&gt; &lt;p&gt;Catalyzed by large‐scale initiatives, such as the Human Connectome Project,[[&lt;reflink idref=&quot;bib13&quot; id=&quot;ref8&quot;&gt;13&lt;/reflink&gt;]] much of neuroscience research has moved from studying individual brain structures to exploring their connections. Structural imaging methods, such as diffusion tensor imaging (DTI), reveal the white matter pathways that connect brain areas with one another and contribute to large networks.[[&lt;reflink idref=&quot;bib15&quot; id=&quot;ref9&quot;&gt;15&lt;/reflink&gt;], [&lt;reflink idref=&quot;bib17&quot; id=&quot;ref10&quot;&gt;17&lt;/reflink&gt;]] At the same time, functional neuroimaging methods, such as resting‐state[[&lt;reflink idref=&quot;bib19&quot; id=&quot;ref11&quot;&gt;19&lt;/reflink&gt;]] and task‐based[[&lt;reflink idref=&quot;bib21&quot; id=&quot;ref12&quot;&gt;21&lt;/reflink&gt;]] functional connectivity, reveal how brain areas interact within these networks. Examples of large‐scale brain networks revealed by functional connectivity include the salience network involved in orienting to behaviorally relevant stimuli in the environment,[&lt;reflink idref=&quot;bib23&quot; id=&quot;ref13&quot;&gt;23&lt;/reflink&gt;] the dorsal attention network involved in visuospatial attention,[&lt;reflink idref=&quot;bib24&quot; id=&quot;ref14&quot;&gt;24&lt;/reflink&gt;] the frontoparietal network involved in cognitive control,[&lt;reflink idref=&quot;bib25&quot; id=&quot;ref15&quot;&gt;25&lt;/reflink&gt;] and the sensorimotor network involved in coordinated motor tasks.[&lt;reflink idref=&quot;bib26&quot; id=&quot;ref16&quot;&gt;26&lt;/reflink&gt;] These networks span traditional anatomical boundaries[&lt;reflink idref=&quot;bib27&quot; id=&quot;ref17&quot;&gt;27&lt;/reflink&gt;] and provide valuable biomarkers for psychopathology.[&lt;reflink idref=&quot;bib28&quot; id=&quot;ref18&quot;&gt;28&lt;/reflink&gt;] Neural connections and disconnections revealed by DTI and measures of functional connectivity predict long‐term symptoms in stroke patients,[&lt;reflink idref=&quot;bib29&quot; id=&quot;ref19&quot;&gt;29&lt;/reflink&gt;] patient‐specific benefits of deep brain stimulation,[&lt;reflink idref=&quot;bib30&quot; id=&quot;ref20&quot;&gt;30&lt;/reflink&gt;] and individual differences in abilities and behaviors.[[&lt;reflink idref=&quot;bib31&quot; id=&quot;ref21&quot;&gt;31&lt;/reflink&gt;]]&lt;/p&gt; &lt;hd id=&quot;AN0186371261-4&quot;&gt;Neuroanatomy in undergraduate education&lt;/hd&gt; &lt;p&gt;While there have been innovations in teaching neuroanatomy, such as 3D modeling and virtual reality, these educational methods have not advanced as rapidly as brain imaging technologies in research and health care.[[&lt;reflink idref=&quot;bib33&quot; id=&quot;ref22&quot;&gt;33&lt;/reflink&gt;]] While later chapters of an introductory neuroscience textbook focus on brain functions and systems, early neuroanatomy sections of textbooks continue to emphasize traditional anatomical divisions often unrelated to structural or functional connectivity.[[&lt;reflink idref=&quot;bib35&quot; id=&quot;ref23&quot;&gt;35&lt;/reflink&gt;], [&lt;reflink idref=&quot;bib37&quot; id=&quot;ref24&quot;&gt;37&lt;/reflink&gt;]] For example, the tectum includes the superior colliculus and inferior colliculus (Figure&#160;1). However, the superior colliculus is part of the visual system, while the inferior colliculus is part of the auditory system. Divisions, such as &quot;tectum,&quot; are irrelevant to other textbook chapters that categorize experiments and clinical applications based on functional systems. In contrast to traditional anatomical boundaries, neural connectivity includes the anatomical connections among brain regions and the specific cognitive and behavioral functions that these connections serve. For instance, the superior brachium anatomically connects the superior colliculus and the lateral geniculate nucleus to control eye movements,[&lt;reflink idref=&quot;bib39&quot; id=&quot;ref25&quot;&gt;39&lt;/reflink&gt;] and the inferior brachium connects the inferior colliculus and the medial geniculate nucleus to locate sounds spatially.[&lt;reflink idref=&quot;bib40&quot; id=&quot;ref26&quot;&gt;40&lt;/reflink&gt;]&lt;/p&gt; &lt;p&gt; &lt;img src=&quot;https://imageserver.ebscohost.com/img/embimages/rdk/8Z8K/01jul25/ase70051-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS&quot; alt=&quot;ase70051-fig-0001.jpg&quot; title=&quot;1 Functional–anatomical connections versus traditional anatomical boundaries. Associations based on neural connections can be more meaningful to undergraduates than traditional neuroanatomical boundaries.&quot; /&gt; &lt;/p&gt; &lt;p&gt;&lt;/p&gt; &lt;p&gt;Focusing on traditional anatomical boundaries may be counterproductive to undergraduate neuroanatomy education, as it emphasizes features unrelated to function. Highlighting such features may impede problem‐solving performance.[&lt;reflink idref=&quot;bib41&quot; id=&quot;ref27&quot;&gt;41&lt;/reflink&gt;] For instance, novices tend to focus on surface similarity when classifying and trying to solve problems.[[&lt;reflink idref=&quot;bib42&quot; id=&quot;ref28&quot;&gt;42&lt;/reflink&gt;]] That is, they tend to focus on perceptually salient similarities, such as similarities that arise from (&lt;reflink idref=&quot;bib1&quot; id=&quot;ref29&quot;&gt;1&lt;/reflink&gt;) an idiosyncratic image, perhaps due to a particular angle; (&lt;reflink idref=&quot;bib2&quot; id=&quot;ref30&quot;&gt;2&lt;/reflink&gt;) similar shapes, such as the spherical shape of the inferior and superior colliculus; or (&lt;reflink idref=&quot;bib3&quot; id=&quot;ref31&quot;&gt;3&lt;/reflink&gt;) similar words (&quot;colliculus&quot;) or letter combinations (ending in &quot;erior&quot;). In contrast, experts tend to focus on features that connect to appropriate schemata.[[&lt;reflink idref=&quot;bib44&quot; id=&quot;ref32&quot;&gt;44&lt;/reflink&gt;]] Such schemata cue appropriate solutions to problems (e.g., If a patient has vision problems, which structures might be damaged?).&lt;/p&gt; &lt;p&gt;Focusing on neural connections in neuroanatomy instruction may implicitly help students create memory schemas by focusing on neural connections to provide critical relationships among to‐be‐learned materials.[&lt;reflink idref=&quot;bib46&quot; id=&quot;ref33&quot;&gt;46&lt;/reflink&gt;] This approach aligns with a constructivist framework for education.[[&lt;reflink idref=&quot;bib47&quot; id=&quot;ref34&quot;&gt;47&lt;/reflink&gt;]] Providing relational contexts (spatial, causal, and functional associations) for new knowledge can facilitate the construction of coherent mental models, which are particularly challenging when learning about the complex and abstract relationships between brain structures. Emphasizing neural connectivity in neuroanatomy education can scaffold students&#39; knowledge and make the content more manageable, meaningful, and memorable.&lt;/p&gt; &lt;p&gt;Causal relationships between neural connections and mental or behavioral outcomes may encourage a student to construct a rich mental model of the brain that connects spatial location, causes, and functional outcomes of different neural connections,[[&lt;reflink idref=&quot;bib49&quot; id=&quot;ref35&quot;&gt;49&lt;/reflink&gt;]] leading to a single integrated representation of neuroanatomy rather than many disconnected ones.[&lt;reflink idref=&quot;bib51&quot; id=&quot;ref36&quot;&gt;51&lt;/reflink&gt;] Learning about neural connectivity may also help students scaffold new learning on existing knowledge.[&lt;reflink idref=&quot;bib52&quot; id=&quot;ref37&quot;&gt;52&lt;/reflink&gt;] For example, most undergraduates are familiar with the cerebellum but not the red nucleus.[&lt;reflink idref=&quot;bib53&quot; id=&quot;ref38&quot;&gt;53&lt;/reflink&gt;] If students learn that the red nucleus connects with the cerebellum to coordinate actions, they can remember it by associating an unfamiliar brain area with a familiar one. Even without explicitly teaching students how to construct these schemata, instruction focusing on neural connections in neuroanatomy instruction may enhance learning by providing critical relationships among to‐be‐learned materials.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-6&quot;&gt;Computer‐based neuroanatomy learning&lt;/hd&gt; &lt;p&gt;We used an interactive graphical software system called Show Me the Brain!! (SMtB) for each training session and the final memory tests[&lt;reflink idref=&quot;bib54&quot; id=&quot;ref39&quot;&gt;54&lt;/reflink&gt;] (J.R. Pani Instructional Systems, USA). SMtB is a revised and expanded version of a system that has been thoroughly evaluated in the context of university instruction in various undergraduate courses and validated in several laboratory studies[[&lt;reflink idref=&quot;bib55&quot; id=&quot;ref40&quot;&gt;55&lt;/reflink&gt;], [&lt;reflink idref=&quot;bib57&quot; id=&quot;ref41&quot;&gt;57&lt;/reflink&gt;]] and the classroom.[&lt;reflink idref=&quot;bib59&quot; id=&quot;ref42&quot;&gt;59&lt;/reflink&gt;] It includes unprecedented accuracy in the three‐dimensional (3D) graphical representation of the human brain and depicts both cortical and subcortical brain structures. The 3D models are based on source material from the Visible Human 2.0 (VH2) cryosections[&lt;reflink idref=&quot;bib60&quot; id=&quot;ref43&quot;&gt;60&lt;/reflink&gt;] (National Library of Medicine, USA). The images in SMtB consist of high‐resolution MRI scans, the VH2 cryosections, and the 3D computer models developed from the VH2 cryosections. Only the 3D computer models were used in the current experiments.&lt;/p&gt; &lt;p&gt;The SMtB software system includes an interactive 3D viewer and an HTML window (Figure&#160;2A). When SMtB is in &quot;Test Mode,&quot; an answer menu also appears. The 3D model of the brain can be controlled by directly clicking and moving it around or by clicking HTML links in the same way one would control a web page. These links specify which brain areas are visible in the display, the initial viewpoint parameters of the brain, the available answer options, and feedback that can be displayed after a user answers a question.&lt;/p&gt; &lt;p&gt; &lt;img src=&quot;https://imageserver.ebscohost.com/img/embimages/rdk/8Z8K/01jul25/ase70051-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS&quot; alt=&quot;ase70051-fig-0002.jpg&quot; title=&quot;2 Show Me the Brain!! (SMtB) interface and experiment procedure. (A) SMtB includes an interactive 3D viewer, a window with HTML links, and an answer menu that appears in &amp;quot;Test Mode.&amp;quot; In each experiment, participants began each training trial by clicking an item link. The 3D model of the test item appeared, and participants clicked the brain structure label in the answer menu. After the 12‐s item display, the highlighted structure turned either green (correct) or red (incorrect). The correct label was displayed as feedback, and a described neural connection was presented above the structure name for structures in the &amp;quot;described connection&amp;quot; conditions. (B) We conducted each experiment as five sessions spaced across 7.5 weeks.&quot; /&gt; &lt;/p&gt; &lt;p&gt;&lt;/p&gt; &lt;hd id=&quot;AN0186371261-8&quot;&gt;Retrieval practice for learning neuroanatomy&lt;/hd&gt; &lt;p&gt;Learning is significantly enhanced when students engage in retrieval practice—actively recalling information from memory—rather than merely rehearsing or reviewing material presented to them.[[&lt;reflink idref=&quot;bib61&quot; id=&quot;ref44&quot;&gt;61&lt;/reflink&gt;], [&lt;reflink idref=&quot;bib63&quot; id=&quot;ref45&quot;&gt;63&lt;/reflink&gt;]] This cognitive strategy, also known as the testing effect, has proven to be a powerful learning tool across various areas of undergraduate education.[[&lt;reflink idref=&quot;bib64&quot; id=&quot;ref46&quot;&gt;64&lt;/reflink&gt;]] Retrieval practice may strengthen neural pathways and synaptic connections associated with the information, consolidating memory through Hebbian learning mechanisms (&quot;neurons that fire together, wire together&quot;).[[&lt;reflink idref=&quot;bib66&quot; id=&quot;ref47&quot;&gt;66&lt;/reflink&gt;]]. This makes retrieval practice particularly effective for durable learning.[&lt;reflink idref=&quot;bib68&quot; id=&quot;ref48&quot;&gt;68&lt;/reflink&gt;] By actively retrieving information, students improve long‐term memory retention and deepen their understanding more effectively than with other forms of active learning,[&lt;reflink idref=&quot;bib62&quot; id=&quot;ref49&quot;&gt;62&lt;/reflink&gt;] especially when retrieval practice is spaced over extended periods.[&lt;reflink idref=&quot;bib69&quot; id=&quot;ref50&quot;&gt;69&lt;/reflink&gt;]&lt;/p&gt; &lt;p&gt;In medical education, retrieval practice has demonstrated significant benefits,[[&lt;reflink idref=&quot;bib70&quot; id=&quot;ref51&quot;&gt;70&lt;/reflink&gt;]] and its effectiveness extends specifically to neuroanatomy instruction.[&lt;reflink idref=&quot;bib72&quot; id=&quot;ref52&quot;&gt;72&lt;/reflink&gt;] Recognizing its proven efficacy, retrieval practice is central to the classroom experiments described here and serves as a benchmark for evaluating the impact of connectivity‐based training on student learning outcomes.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-9&quot;&gt;Introduction to experiments&lt;/hd&gt; &lt;p&gt;Two experiments in undergraduate classrooms explored how emphasizing neural connectivity can enhance students&#39; learning of neuroanatomy. We hypothesized that neuroanatomy instruction emphasizing connectivity would improve students&#39; ability to associate brain structures with one another and their functions, leading to improved long‐term retention and generalization of neuroanatomical information. To test this hypothesis, the experiments involved within‐subjects designs in which the same students were exposed to multiple training conditions, with different brain structures assigned to each condition. In these experiments, students completed training sessions focused on identifying brain structures while receiving text‐based narrative feedback about neural connections. The first experiment compared the effects of described neural connections to retrieval practice effects on the long‐term memory of neuroanatomy. The second experiment aimed to replicate and extend the findings from the first experiment in a larger sample of undergraduates, a broader range of outcome measures to assess long‐term memory, and a new independent variable—anatomical categorization—to test whether simply grouping neuroanatomical content into functional systems (&lt;emph&gt;linguistic&lt;/emph&gt;, &lt;emph&gt;limbic&lt;/emph&gt;, &lt;emph&gt;motor&lt;/emph&gt;, and &lt;emph&gt;visual&lt;/emph&gt;) would support knowledge retention in the absence of described neural connections.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-10&quot;&gt;MATERIALS AND METHODS&lt;/hd&gt; &lt;p&gt;Experiments 1 and 2 employed identical materials and training procedures but differed in participants, secondary training manipulations (retrieval practice or categorization), and the scope of the final memory tests. We outline the general training procedure and differentiate between Experiment 1 and Experiment 2 as necessary.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-11&quot;&gt;Participants&lt;/hd&gt; &lt;p&gt;&lt;/p&gt; &lt;hd id=&quot;AN0186371261-12&quot;&gt;Experiment 1&lt;/hd&gt; &lt;p&gt;Thirty undergraduate students at the College of Charleston participated in the experiment as part of a behavioral neuroscience course in September and October of 2021. Most students were in their second or third year majoring in psychology. Students participated in the experiment for course credit, and the number of students in the course determined the sample size. For all analyses, we included only students who completed all three training sessions and the final memory test, resulting in a final sample size of 25 participants (20 female and five male). The College of Charleston Institutional Review Board approved the study protocol (#2021‐08‐007).&lt;/p&gt; &lt;hd id=&quot;AN0186371261-13&quot;&gt;Experiment 2&lt;/hd&gt; &lt;p&gt;A total of 122 undergraduates participated in the experiment (29 students from the College of Charleston and 93 students from the University of Louisville) as part of introductory neuroscience courses in September and October of 2022. Students at each institution participated in the experiment for course credit, with the sample size determined by the number of instructors who volunteered to participate in the study and the number of students in the classes. Most students at each institution were in their second or third year majoring in psychology. For all analyses, we included only students who completed all three training sessions and all six memory tests. This resulted in a final sample size of 104 participants. The final sample included 24 participants from the College of Charleston (20 female and 4 male) and 80 participants from the University of Louisville (57 female and 23 male). The study protocol was approved by institutional review boards at both the College of Charleston (#2021‐08‐007) and the University of Louisville (#714502).&lt;/p&gt; &lt;hd id=&quot;AN0186371261-14&quot;&gt;Materials&lt;/hd&gt; &lt;p&gt;The Show Me the Brain!! (SMtB) interactive graphical software system (J.R. Pani Instructional Systems, USA) was used for the pre‐exposure session, all training sessions, and several of the final memory tests. SMtB will be freely available as an open‐source package on GitHub. Students in each experiment accessed SMtB using their personal computers at home or on campus. Students at the College of Charleston used Amazon AppStream 2.0 (Amazon Web Services, Inc., USA)[&lt;reflink idref=&quot;bib73&quot; id=&quot;ref53&quot;&gt;73&lt;/reflink&gt;] to connect to SMtB. Students at the University of Louisville connected to SMtB through Microsoft Azure Labs (Microsoft Corporation, USA).[&lt;reflink idref=&quot;bib74&quot; id=&quot;ref54&quot;&gt;74&lt;/reflink&gt;] Both AppStream 2.0 and Azure Labs permit running a computer application from the internet cloud as if installed on the user&#39;s computer.&lt;/p&gt; &lt;p&gt;Stimuli included 3D graphical models of 48 brain structures and text‐based narrative feedback about labels, neural connections, and functional outcomes. Participants were trained and tested on associating the label of a brain structure with its graphical model. In the &quot;described connection&quot; conditions, students also learned about connected brain structures and the functional outcomes of neural connections. Table&#160;1 lists examples of the brain structures and their described neural connections. Table&#160;S1 in the Supplemental Material lists all the brain structures and their described neural connections.&lt;/p&gt; &lt;p&gt;1 TABLE Example brain structures and described neural connections.&lt;/p&gt; &lt;p&gt; &lt;ephtml&gt; &amp;lt;table&amp;gt;&amp;lt;thead valign=&quot;bottom&quot;&amp;gt;&amp;lt;tr&amp;gt;&amp;lt;th align=&quot;left&quot;&amp;gt;Category&amp;lt;/th&amp;gt;&amp;lt;th align=&quot;left&quot;&amp;gt;Brain structure&amp;lt;/th&amp;gt;&amp;lt;th align=&quot;left&quot;&amp;gt;Described neural connection&amp;lt;/th&amp;gt;&amp;lt;/tr&amp;gt;&amp;lt;/thead&amp;gt;&amp;lt;tbody valign=&quot;top&quot;&amp;gt;&amp;lt;tr&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Visual&amp;lt;/td&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Fusiform gyrus&amp;lt;/td&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Connects with the inferior temporal gyrus to recognize objects&amp;lt;/td&amp;gt;&amp;lt;/tr&amp;gt;&amp;lt;tr&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Inferior temporal gyrus&amp;lt;/td&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Connects with the fusiform gyrus to recognize objects&amp;lt;/td&amp;gt;&amp;lt;/tr&amp;gt;&amp;lt;tr&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Superior colliculus&amp;lt;/td&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Connects with the lateral geniculate nucleus to control eye movements&amp;lt;/td&amp;gt;&amp;lt;/tr&amp;gt;&amp;lt;tr&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Lateral geniculate nucleus&amp;lt;/td&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Connects with the superior colliculus to control eye movements&amp;lt;/td&amp;gt;&amp;lt;/tr&amp;gt;&amp;lt;tr&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Linguistic&amp;lt;/td&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Middle frontal gyrus&amp;lt;/td&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Connects with Brodmann area 47 to retrieve words&amp;lt;/td&amp;gt;&amp;lt;/tr&amp;gt;&amp;lt;tr&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Brodmann area 47&amp;lt;/td&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Connects with the middle frontal gyrus to retrieve words&amp;lt;/td&amp;gt;&amp;lt;/tr&amp;gt;&amp;lt;tr&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Inferior colliculus&amp;lt;/td&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Connects with the medial geniculate nucleus to localize sounds&amp;lt;/td&amp;gt;&amp;lt;/tr&amp;gt;&amp;lt;tr&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Medial geniculate nucleus&amp;lt;/td&amp;gt;&amp;lt;td align=&quot;left&quot;&amp;gt;Connects with the inferior colliculus to localize sounds&amp;lt;/td&amp;gt;&amp;lt;/tr&amp;gt;&amp;lt;/tbody&amp;gt;&amp;lt;/table&amp;gt; &lt;/ephtml&gt; &lt;/p&gt; &lt;p&gt;1 &lt;emph&gt;Note&lt;/emph&gt;: Described neural connections were shown as text‐based narrative feedback after training trials. In Experiment 1, we selected brain structures within each condition from multiple categories (visual, linguistic, limbic, and motor). In Experiment 2, brain structures were either drawn from multiple anatomical categories (&quot;uncategorized&quot; conditions) or a single category (&quot;categorized&quot; conditions). Table&#160;S1 in the Supplemental Material includes all 48 brain structures and the corresponding described neural connections.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-15&quot;&gt;Experiment timeline&lt;/hd&gt; &lt;p&gt;Each experiment spanned five sessions across 7.5 weeks, including a pre‐exposure session, three training sessions, and a final memory test (Figure&#160;2B). The timeline of events in training and testing was informed by previous studies on spaced retrieval practice.[[&lt;reflink idref=&quot;bib70&quot; id=&quot;ref55&quot;&gt;70&lt;/reflink&gt;], [&lt;reflink idref=&quot;bib72&quot; id=&quot;ref56&quot;&gt;72&lt;/reflink&gt;], [&lt;reflink idref=&quot;bib75&quot; id=&quot;ref57&quot;&gt;75&lt;/reflink&gt;]] Participants were allowed a 24‐h time window to complete each session from their personal computers. The training procedures for Experiments 1 and 2 were identical, with the exception of the training manipulations described below and that Experiment 2 involved multiple‐choice with four response options instead of two response options.&lt;/p&gt; &lt;p&gt;Students did not have access to SMtB outside the designated sessions, and the memory tests were graded based on completion rather than accuracy to deter students from independently studying the brain structures by other means. More importantly, each experiment involved a within‐subjects design in which every participant experienced every condition, with the assignment of brain structures to conditions counterbalanced across participants. Students studying outside the neuroanatomy training sessions may have led us to underestimate differences among the training conditions. However, critically, differences among students studying outside the training sessions should not have affected one condition differently from any other.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-16&quot;&gt;Pre‐exposure session&lt;/hd&gt; &lt;p&gt;In the pre‐exposure session, the names of all 48 brain structures appeared as links on the right side of the screen and as answer options on the left side. Participants were not tested on the names of the brain structures during this session. Instead, they clicked the link for each structure name to display an interactive 3D brain model. One structure was highlighted in yellow, and the surrounding non‐highlighted structures provided a visual scene. The name of the highlighted structure appeared below the brain model. Participants selected the structure name from an answer menu containing all 48 structures and clicked a &quot;Submit Answer&quot; button for each brain structure.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-17&quot;&gt;Training sessions&lt;/hd&gt; &lt;p&gt;Each of the three subsequent training sessions comprised 96 trials, including two trials for each of the 48 brain structures. The trials within a training session were organized into eight sections of 12 trials each, allowing students the flexibility to complete the laboratory at their own pace within the 24‐h open window. Because of the fixed trial duration and number of trials for each training session, stimulus exposure was exactly 28.8 min for each condition across the three training sessions. However, students could take as much time as needed between the 96 trials and eight sections of each training session. The median total time to complete each training session was about 60 min, with an interquartile range of 50 to 75 min.&lt;/p&gt; &lt;p&gt;Each trial lasted 24 s. First, a highlighted brain structure appeared with its surrounding structures for 12 s, while alternative structure labels appeared as options in an answer menu to the left of the brain 3D model. These answer options included two different structure labels for each training trial in Experiment 1 and four different labels for each trial in Experiment 2. To prevent students from guessing the correct answers, foils in each experiment were randomly selected from a group of 15–17 similar structures for each correct structure label (Table&#160;S2 in the Supplemental Material). Participants clicked the structure label that matched the highlighted structure in the 3D model and then clicked a &quot;Submit Answer&quot; button. After the 12‐s item display, the highlighted structure turned either green (correct) or red (incorrect), and the correct name of the brain structure appeared as feedback for 12 s. Depending on the training condition, the structure name appeared by itself during the feedback display or with a described neural connection appearing below it. The initial camera angle was the same for each repetition of a particular brain structure, but students could rotate the brain model and view it from multiple angles. Figure&#160;2A depicts an example section containing 12 item links.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-18&quot;&gt;Manipulation of described neural connections&lt;/hd&gt; &lt;p&gt;Both experiments included described neural connections as an independent variable. In the &quot;described connection&quot; conditions, the correct label of the structure appeared during the feedback part of a trial alongside a brief description of a neural connection with another brain structure and the functional outcome of this connection (e.g., &quot;Connects with the lateral geniculate nucleus to control eye movements&quot;). In the &quot;label‐only&quot; conditions, the correct label of the displayed brain structure appeared in training trials with no other information. After a one‐month delay for each experiment, we evaluated students&#39; long‐term memory of the 48 brain structures based on structure identification in each experiment. The second experiment included a broader range of outcome measures to assess long‐term memory, including visual transfer and clinical applications.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-19&quot;&gt;Experiment‐specific training manipulations&lt;/hd&gt; &lt;p&gt;Training for each experiment involved an orthogonal second manipulation within a 2 &#215; 2 factorial design, but differed with respect to the secondary training manipulation. Experiment 1 included retrieval practice as a secondary training manipulation, while Experiment 2 included anatomical categorization as a secondary training manipulation.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-20&quot;&gt;Experiment 1&lt;/hd&gt; &lt;p&gt;Two types of item display distinguished &quot;retrieval practice&quot; conditions from &quot;passive study&quot; conditions (Figure&#160;3B). In the retrieval practice conditions, the brain model appeared by itself for 12 s while the participant selected the label of the highlighted brain structure from two options. After 12 s, the structure label (and a described neural connection in half the trials) appeared for 12 s while the highlighted brain structure remained visible. Passive study trials were identical in procedure to the retrieval practice trials, except that the structure label appeared below the 3D brain model in both the first and last 12‐s periods. Additionally, passive study trials were randomly intermixed with retrieval practice trials to ensure student participants remained engaged throughout the training.&lt;/p&gt; &lt;p&gt; &lt;img src=&quot;https://imageserver.ebscohost.com/img/embimages/rdk/8Z8K/01jul25/ase70051-fig-0003.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS&quot; alt=&quot;ase70051-fig-0003.jpg&quot; title=&quot;3 Training conditions and performance. (A) We manipulated the described neural connections in each experiment through text‐based narrative feedback displayed at the end of each trial in both experiments. In Experiment 1, we varied retrieval practice by either including or omitting the name of the brain structure at the beginning of each trial. All conditions in Experiment 2 involved retrieval practice. (B) In Experiment 1, training accuracy reached a ceiling for the passive study conditions. For the retrieval practice conditions, we observed a significant improvement in accuracy across the three training sessions. In contrast, retrieval practice accuracy showed no differences or interactions with the described connection and label‐only conditions. (C) Training accuracy in Experiment 2 improved across sessions, but no other significant main effects or interactions were noted.&quot; /&gt; &lt;/p&gt; &lt;p&gt;&lt;/p&gt; &lt;hd id=&quot;AN0186371261-22&quot;&gt;Experiment 2&lt;/hd&gt; &lt;p&gt;To manipulate categorization, we compared training in which functionally related structures and connections were learned contiguously and in the context of a category label to when the structures and their connections were intermixed and uncategorized. In &quot;categorized&quot; conditions, brain structures and neural connections belonging to a single category (visual, motor, limbic, or linguistic; see Table&#160;1 and Table&#160;S1 in the Supplemental Material) were always practiced within the same training sections, while the category label (e.g., &quot;Visual System&quot;) appeared at the top of the computer screen. In &quot;uncategorized&quot; conditions, brain structures and neural connections from two different categories (e.g., visual and motor) were intermixed within each training section, and &quot;Miscellaneous Structures&quot; was displayed at the top of the computer screen. All training trials in Experiment 2 used the retrieval practice method used in Experiment 1.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-23&quot;&gt;Final memory tests&lt;/hd&gt; &lt;p&gt;&lt;/p&gt; &lt;hd id=&quot;AN0186371261-24&quot;&gt;Experiment 1&lt;/hd&gt; &lt;p&gt;Each participant completed a final memory test 4.5 weeks after the last training session. The final memory test was completed within SMtB, and brain structures appeared in the same orientation and with the same surrounding structures as during the training sessions. In the test, numbered item links (e.g., &quot;Item 1&quot;) randomly corresponded to each of the 48 brain structures. Participants clicked each link to display a 3D model of the highlighted brain structure and surrounding structures. Participants identified each brain structure by clicking its name within an answer menu that included the names of all 48 brain structures as options. No feedback was given. Each time participants clicked &quot;Submit Answer,&quot; the 3D model returned to its original state without any structures highlighted.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-25&quot;&gt;Experiment 2&lt;/hd&gt; &lt;p&gt;Instead of one final memory test, Experiment 2 included six memory tests (Figure&#160;5A). Tests were completed on personal computers within the same 24‐h time window as one another and in a fixed order (i.e., we tested all structures in Test 1 and then all in Test 2) to minimize contamination effects from earlier tests to later tests. Student participants performed Tests 1 and 2 within SMtB with brain structures appearing in the same orientation and with the same surrounding structures as during the training sessions. In Test 1 (cued recall), participants viewed each 3D brain structure and labeled it by typing its name into an answer box. Student responses could be in upper or lower‐case letters. Incorrect spellings were allowed if the Levenshtein distance was two or fewer single‐character edits,[&lt;reflink idref=&quot;bib76&quot; id=&quot;ref58&quot;&gt;76&lt;/reflink&gt;] and it contained the correct number in the case of Brodmann areas (e.g., &quot;Brodmann Area 45&quot;). Test 2 (multiple choice) was identical to the final memory test in Experiment 1.&lt;/p&gt; &lt;p&gt; &lt;img src=&quot;https://imageserver.ebscohost.com/img/embimages/rdk/8Z8K/01jul25/ase70051-fig-0004.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS&quot; alt=&quot;ase70051-fig-0004.jpg&quot; title=&quot;4 Experiment 1 long‐term memory. (A) Long‐term memory for each brain area was assessed using a multiple‐choice test with 48 response options. (B) We observed significant advantages in long‐term memory associated with both the described neural connections and retrieval practice. 95% confidence intervals and the significance marker reflect the main effect difference between the described connection and label‐only conditions. (C) Effect sizes for the two training manipulations. Error bars display the 95% confidence interval for each effect size. *p &amp;lt; 0.05.&quot; /&gt; &lt;/p&gt; &lt;p&gt;&lt;/p&gt; &lt;p&gt;Students completed Tests 3 to 6 outside of SMtB in Qualtrics (Qualtrics International Inc., USA) with screenshots taken from SMtB. In Test 3 (near visual transfer), participants viewed each 3D brain structure from a different camera angle and with different surrounding brain areas than in the training sessions. They labeled it by clicking within a menu with four alternative labels as options. In Test 4 (far visual transfer), participants viewed a 2D cryosection with the target brain structure highlighted and labeled the structure by clicking within a menu with four alternative labels as options. In Test 5 (functional outcomes), participants identified the function of each brain structure by clicking within a menu that included four alternative functions as options. For example, participants indicated &quot;spatially locate sounds&quot; for &quot;Inferior Colliculus + Medial Geniculate Nucleus.&quot; In Test 6 (clinical applications), participants read a scenario for each trial and then clicked which of four neural connections was most likely damaged within a menu with four alternative neural connections as options. For example, participants indicated &quot;Transverse Temporal Gyrus + Superior Temporal Gyrus&quot; for the clinical scenario, &quot;After a stroke, Elizabeth can no longer distinguish between musical tones.&quot; We measured performance for each test as percent correct.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-27&quot;&gt;Data analysis&lt;/hd&gt; &lt;p&gt;Data from the final memory tests and the training sessions were analyzed through repeated‐measures ANOVAs implemented in MATLAB (The MathWorks, Inc., USA).[&lt;reflink idref=&quot;bib77&quot; id=&quot;ref59&quot;&gt;77&lt;/reflink&gt;] Tests were evaluated against a two‐tailed &lt;emph&gt;p&lt;/emph&gt; &amp;lt; 0.05 level of significance, and effect sizes are reported as Cohen&#39;s &lt;emph&gt;d&lt;/emph&gt; for repeated measures.[&lt;reflink idref=&quot;bib78&quot; id=&quot;ref60&quot;&gt;78&lt;/reflink&gt;] Trials without a response (approximately 3% of trials in Experiment 1 and about 4% in Experiment 2) were removed from the training dataset for each experiment before data analysis.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-28&quot;&gt;RESULTS&lt;/hd&gt; &lt;p&gt;&lt;/p&gt; &lt;hd id=&quot;AN0186371261-29&quot;&gt;Training performance&lt;/hd&gt; &lt;p&gt;&lt;/p&gt; &lt;hd id=&quot;AN0186371261-30&quot;&gt;Experiment 1&lt;/hd&gt; &lt;p&gt;Accuracy was at ceiling throughout training with passive study ( &lt;ephtml&gt; &amp;lt;math altimg=&quot;urn:x-wiley:19359772:media:ase70051:ase70051-math-0001&quot; display=&quot;inline&quot; overflow=&quot;scroll&quot; xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;&amp;gt;&amp;lt;semantics&amp;gt;&amp;lt;mrow&amp;gt;&amp;lt;mover accent=&quot;true&quot;&amp;gt;&amp;lt;mi&amp;gt;x&amp;lt;/mi&amp;gt;&amp;lt;mo&amp;gt;&amp;amp;#175;&amp;lt;/mo&amp;gt;&amp;lt;/mover&amp;gt;&amp;lt;/mrow&amp;gt;&amp;lt;annotation encoding=&quot;application/x-tex&quot;&amp;gt;$$ \overline{x} $$&amp;lt;/annotation&amp;gt;&amp;lt;/semantics&amp;gt;&amp;lt;/math&amp;gt; &lt;/ephtml&gt;  &amp;gt; 97% for each condition). We anticipated that the accuracy for the passive study trials during training would be nearly 100%, as participants were given the correct answer at the start of each trial. For retrieval practice, participants improved across training sessions and were significantly more accurate in Session 3 than in Sessions 1 and 2 (&lt;emph&gt;p&lt;/emph&gt;s &amp;lt; 0.01, &lt;emph&gt;d&lt;/emph&gt; = 0.73 and 0.36, respectively). Training accuracy with retrieval practice did not differ between or interact with the described connection and label‐only conditions (&lt;emph&gt;p&lt;/emph&gt;s &amp;gt; 0.36). Figure&#160;3B displays accuracy for each Experiment 1 training session and condition.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-31&quot;&gt;Experiment 2&lt;/hd&gt; &lt;p&gt;Participants improved across training sessions for all conditions, showing higher accuracy in Sessions 2 and 3 than in Session 1 (&lt;emph&gt;p&lt;/emph&gt;s &amp;lt; 0.001, &lt;emph&gt;d&lt;/emph&gt; = 0.54 and 0.40, respectively). Accuracy did not reliably differ among conditions during the training sessions, and there were no significant interactions between training session and condition (&lt;emph&gt;p&lt;/emph&gt;s &amp;gt; 0.10). Figure&#160;3C displays the accuracy for each training session in Experiment 2.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-32&quot;&gt;Final memory tests&lt;/hd&gt; &lt;p&gt;&lt;/p&gt; &lt;hd id=&quot;AN0186371261-33&quot;&gt;Experiment 1&lt;/hd&gt; &lt;p&gt;As shown in Figure&#160;4, both described connections and retrieval practice improved final memory test performance. A repeated‐measures ANOVA revealed significant main effects for both described neural connections (described connection vs. label‐only: &lt;emph&gt;F&lt;/emph&gt; (&lt;reflink idref=&quot;bib1&quot; id=&quot;ref61&quot;&gt;1&lt;/reflink&gt;, 24) = 7.38, &lt;emph&gt;p&lt;/emph&gt; = 0.01, &lt;emph&gt;d&lt;/emph&gt; = 0.54) and retrieval practice (retrieval practice vs. passive study: &lt;emph&gt;F&lt;/emph&gt; (&lt;reflink idref=&quot;bib1&quot; id=&quot;ref62&quot;&gt;1&lt;/reflink&gt;, 24) = 5.47, &lt;emph&gt;p&lt;/emph&gt; = 0.03, &lt;emph&gt;d&lt;/emph&gt; = 0.47). There was no evidence of an interaction between factors (described neural connections &#215; retrieval practice: &lt;emph&gt;F&lt;/emph&gt; (&lt;reflink idref=&quot;bib1&quot; id=&quot;ref63&quot;&gt;1&lt;/reflink&gt;, 24) = 0.00, &lt;emph&gt;p&lt;/emph&gt; = 0.96). Performance did not differ between the female and male students, and gender did not significantly interact with either of the training manipulations (&lt;emph&gt;p&lt;/emph&gt;s &amp;gt; 0.05).&lt;/p&gt; &lt;hd id=&quot;AN0186371261-34&quot;&gt;Experiment 2&lt;/hd&gt; &lt;p&gt;Described neural connections benefited performance on most memory tests (positive blue bars in Figure&#160;5C). Critically, this included significant main effects for both measures of structure identification: cued recall (&lt;emph&gt;F&lt;/emph&gt; (&lt;reflink idref=&quot;bib1&quot; id=&quot;ref64&quot;&gt;1&lt;/reflink&gt;, 102) = 8.08, &lt;emph&gt;p&lt;/emph&gt; = 0.005, &lt;emph&gt;d&lt;/emph&gt; = 0.28) and multiple choice (&lt;emph&gt;F&lt;/emph&gt; (&lt;reflink idref=&quot;bib1&quot; id=&quot;ref65&quot;&gt;1&lt;/reflink&gt;, 102) = 5.75, &lt;emph&gt;p&lt;/emph&gt; = 0.02, &lt;emph&gt;d&lt;/emph&gt; = 0.24). The significant main effect of described connections in the multiple‐choice test was a direct replication of the described neural connections effect in Experiment 1. Additionally, although described neural connections did not significantly affect performance on tests of near visual transfer (&lt;emph&gt;F&lt;/emph&gt; (&lt;reflink idref=&quot;bib1&quot; id=&quot;ref66&quot;&gt;1&lt;/reflink&gt;, 102) = 3.64, &lt;emph&gt;p&lt;/emph&gt; = 0.06, &lt;emph&gt;d&lt;/emph&gt; = 0.19) or far visual transfer (&lt;emph&gt;F&lt;/emph&gt; (&lt;reflink idref=&quot;bib1&quot; id=&quot;ref67&quot;&gt;1&lt;/reflink&gt;, 102) = 0.90, &lt;emph&gt;p&lt;/emph&gt; = 0.35, &lt;emph&gt;d&lt;/emph&gt; = 0.09), they benefited performance on tests of functional outcome (&lt;emph&gt;F&lt;/emph&gt; (&lt;reflink idref=&quot;bib1&quot; id=&quot;ref68&quot;&gt;1&lt;/reflink&gt;, 102) = 21.78, &lt;emph&gt;p&lt;/emph&gt; &amp;lt; 0.001, &lt;emph&gt;d&lt;/emph&gt; = 0.46) and clinical application (&lt;emph&gt;F&lt;/emph&gt; (&lt;reflink idref=&quot;bib1&quot; id=&quot;ref69&quot;&gt;1&lt;/reflink&gt;, 102) = 7.19, &lt;emph&gt;p&lt;/emph&gt; = 0.009, &lt;emph&gt;d&lt;/emph&gt; = 0.27).&lt;/p&gt; &lt;p&gt;Categorization had negligible effects on any of the final memory tests (green bars in Figure&#160;5C). The main effect of categorization did not approach significance for cued recall, multiple choice, far visual transfer, or clinical applications (&lt;emph&gt;p&lt;/emph&gt; &amp;gt; 0.30). It also was not reliable for near visual transfer (&lt;emph&gt;F&lt;/emph&gt; (&lt;reflink idref=&quot;bib1&quot; id=&quot;ref70&quot;&gt;1&lt;/reflink&gt;, 102) = 3.29, &lt;emph&gt;p&lt;/emph&gt; = 0.07, &lt;emph&gt;d&lt;/emph&gt; = 0.19). In contrast, knowledge of functional outcomes was impaired for categorized structures compared with uncategorized structures (&lt;emph&gt;F&lt;/emph&gt; (&lt;reflink idref=&quot;bib1&quot; id=&quot;ref71&quot;&gt;1&lt;/reflink&gt;, 102) = 9.66, &lt;emph&gt;p&lt;/emph&gt; = 0.002, &lt;emph&gt;d&lt;/emph&gt; = −0.31). Here, the functional outcomes of individual connections were more confusable when participants learned them within the same section of a training session and the same category (e.g., &quot;Motor System&quot;) than when they were intermixed across categories.&lt;/p&gt; &lt;p&gt; &lt;img src=&quot;https://imageserver.ebscohost.com/img/embimages/rdk/8Z8K/01jul25/ase70051-fig-0005.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS&quot; alt=&quot;ase70051-fig-0005.jpg&quot; title=&quot;5 Experiment 2 long‐term memory. (A) Long‐term memory and knowledge generalization for each brain area were tested in six ways. (B) We observed significant advantages in long‐term memory associated with both the described neural connections and retrieval practice. Interactions between training manipulations were not reliable for any measure. 95% confidence intervals and significance markers reflect the main effect of described neural connections. (C) Effect sizes for the two training manipulations and each outcome measure with 95% confidence intervals for each effect size. *p &amp;lt; 0.05; **p &amp;lt; 0.01; ***p &amp;lt; 0.001.&quot; /&gt; &lt;/p&gt; &lt;p&gt;&lt;/p&gt; &lt;p&gt;Finally, performance did not differ across institutions or genders for any of the memory tests, and there were no statistically significant interactions between the training manipulations and either institution or gender (&lt;emph&gt;p&lt;/emph&gt;s &amp;gt; 0.05). Additionally, no within‐subjects interactions were statistically significant for any of the memory tests (&lt;emph&gt;p&lt;/emph&gt;s &amp;gt; 0.05).&lt;/p&gt; &lt;hd id=&quot;AN0186371261-36&quot;&gt;DISCUSSION&lt;/hd&gt; &lt;p&gt;Providing information about neural connections and their functional outcomes enhances students&#39; ability to identify brain structures a month after training (both experiments). Training data indicated similar initial understanding across conditions, implying that observed performance differences in the final test reflect long‐term retention rather than initial learning (both experiments). This improvement in long‐term retention is comparable in effect size to that of retrieval practice (Experiment 1), and it generalizes to reasoning about cognitive and behavioral deficits in stroke patients, but it does not generalize to a new visual perspective or modality (Experiment 2). Providing information about neural connections differs from categorizing structures into labeled systems. Categorizing structures under labels like &quot;Visual System&quot; did not enhance long‐term memory and impaired functional outcome knowledge (Experiment 2). Below, we explore potential psychological mechanisms that explain how learning neural connections enhances students&#39; long‐term memory of neuroanatomy. We then pose empirical questions for future research on connected neuroanatomy instruction.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-37&quot;&gt;Constructing mental models through described neural connections&lt;/hd&gt; &lt;p&gt;In each experiment, information about neural connections and their functional outcomes facilitated the memory of structure names, even though knowledge about connections is not strictly necessary to identify individual brain structures. This advantage for long‐term memory is consistent with research on the psychology of learning and theories centered on levels of processing and elaborative encoding.[[&lt;reflink idref=&quot;bib79&quot; id=&quot;ref72&quot;&gt;79&lt;/reflink&gt;]] Elaborative encoding in each experiment could include associations between pairs of structures,[&lt;reflink idref=&quot;bib81&quot; id=&quot;ref73&quot;&gt;81&lt;/reflink&gt;] knowledge of how sets of brain areas are related to each other,[&lt;reflink idref=&quot;bib46&quot; id=&quot;ref74&quot;&gt;46&lt;/reflink&gt;] and causal models of how multiple brain areas interact to produce functional outcomes.[&lt;reflink idref=&quot;bib82&quot; id=&quot;ref75&quot;&gt;82&lt;/reflink&gt;] More generally, reading about neural connections and their functional outcomes may help students construct integrated mental models of the brain.&lt;/p&gt; &lt;p&gt;While described neural connections may help students construct new mental models of the brain, they may also assist students in integrating new information into existing mental models. Reading about neural connections and their functional outcomes can help students incorporate new information into existing cognitive frameworks. One example of this is making neuroanatomy more relevant to the real‐world understanding of brain function. In addition, it may be easier for learners to build mental models of neuroanatomy when the information to be learned shares spatial, causal, or goal‐related contexts with existing knowledge.[[&lt;reflink idref=&quot;bib50&quot; id=&quot;ref76&quot;&gt;50&lt;/reflink&gt;], [&lt;reflink idref=&quot;bib83&quot; id=&quot;ref77&quot;&gt;83&lt;/reflink&gt;], [&lt;reflink idref=&quot;bib85&quot; id=&quot;ref78&quot;&gt;85&lt;/reflink&gt;]] Learning about neural connections may better enable students&#39; existing models to provide scaffolding, supporting the integration of new information into existing cognitive frameworks. For example, a student who knows that the superior colliculus supports eye movements can learn more quickly that the lateral geniculate nucleus is also part of the visual system. Likewise, students can remember the functional outcomes of neural connections and apply this knowledge to identify potentially damaged neural connections in a clinical context.&lt;/p&gt; &lt;p&gt;Notably, the procedure described here focuses on neural connections causing functional outcomes without specifying the causal sequence between brain regions. While reciprocal connectivity is consistent with fMRI measures of functional connectivity based on temporal correlations among brain structures,[&lt;reflink idref=&quot;bib86&quot; id=&quot;ref79&quot;&gt;86&lt;/reflink&gt;] future studies could extend to causal relationships within the brain (Structure A &lt;emph&gt;causes&lt;/emph&gt; Structure B to cause an outcome). Such specification could further help knowledge transfer to clinical applications because specific parts of a causal and temporal chain may account for neuropsychological symptoms. For example, in a clinical situation, a neurologist may need to determine the underlying cause of a patient&#39;s visual impairment. Understanding the neural connections involved in visual processing, such as the pathway from the retina to the primary visual cortex, is crucial for diagnosing and treating the condition.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-38&quot;&gt;Curricular integration through described neural connections&lt;/hd&gt; &lt;p&gt;We showed that encouraging students to build mental models based on neural connections helps them learn and retain fundamental neuroanatomy. Such integration can provide a rich contextual resource for the information being learned in the course. In introductory neuroscience classes, where functional neuroanatomy is an element of the broader course content,[[&lt;reflink idref=&quot;bib87&quot; id=&quot;ref80&quot;&gt;87&lt;/reflink&gt;]] the material covered throughout the course may provide particular support and incentive for emphasizing neural connections in the comprehension and retention of neuroanatomy.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-39&quot;&gt;Separate effects of retrieval practice&lt;/hd&gt; &lt;p&gt;Retrieval practice benefited memory in Experiment 1 with a similar effect size to the learning benefit provided by described neural connections. The reliable main effect of retrieval practice replicates an extensive literature on retrieval practice effects in other domains[[&lt;reflink idref=&quot;bib62&quot; id=&quot;ref81&quot;&gt;62&lt;/reflink&gt;], [&lt;reflink idref=&quot;bib64&quot; id=&quot;ref82&quot;&gt;64&lt;/reflink&gt;], [&lt;reflink idref=&quot;bib89&quot; id=&quot;ref83&quot;&gt;89&lt;/reflink&gt;]] and at least one other study in neuroanatomy instruction.[&lt;reflink idref=&quot;bib72&quot; id=&quot;ref84&quot;&gt;72&lt;/reflink&gt;] The memory benefit of retrieval practice was additive with the learning benefit of described neural connections. The lack of interaction between these manipulations could suggest separate learning mechanisms. However, it is essential to consider the details of how described neural connections and retrieval practice were implemented.&lt;/p&gt; &lt;p&gt;Critically, elaborative encoding of neural connections occurred &lt;emph&gt;after&lt;/emph&gt; retrieval practice of the structure name. While previous studies have reported that retrieval practice and elaborative encoding can interact when elaborative encoding is performed before retrieval practice,[[&lt;reflink idref=&quot;bib90&quot; id=&quot;ref85&quot;&gt;90&lt;/reflink&gt;]] additive effects, such as those found in Experiment 1, have been observed in research in which elaborative encoding is performed after retrieval practice.[[&lt;reflink idref=&quot;bib92&quot; id=&quot;ref86&quot;&gt;92&lt;/reflink&gt;]]&lt;/p&gt; &lt;hd id=&quot;AN0186371261-40&quot;&gt;Encouraging knowledge transfer&lt;/hd&gt; &lt;p&gt;Reading about neural connections led to the successful transfer of knowledge from functional information to clinical applications. Students effectively applied knowledge of functional outcomes to problem‐solving in clinical situations involving damaged neural connections. Knowledge transfer to clinical applications required inferring the functional consequences of losing a connection.&lt;/p&gt; &lt;p&gt;Reading about neural connections did not enhance the visual transfer necessary to recognize brain structures from different camera angles and visual contexts that include different surrounding brain structures. The lack of visual transfer observed for the near and far visual transfer tests warrants further investigation. Near and far visual transfer may be more effectively encouraged by including more varied brain imagery during training and feedback about neural connections. A strength of SMtB is the ability to examine structures from different angles and with various collections of surrounding structures.[[&lt;reflink idref=&quot;bib58&quot; id=&quot;ref87&quot;&gt;58&lt;/reflink&gt;]] Future studies could specifically target visual transfer by beginning each trial with the brain in a different orientation or with other surrounding structures.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-41&quot;&gt;Minimal effects of categorization&lt;/hd&gt; &lt;p&gt;Categorizing brain structures by functional system labels alone did not enhance students&#39; memory or generalization of neuroanatomical knowledge. Generally, students learn information better when items are organized according to meaningful relationships rather than perceived as arbitrary collections.[[&lt;reflink idref=&quot;bib94&quot; id=&quot;ref88&quot;&gt;94&lt;/reflink&gt;], [&lt;reflink idref=&quot;bib96&quot; id=&quot;ref89&quot;&gt;96&lt;/reflink&gt;]] However, the categorization implemented in the current experiments involved system labels (e.g., &quot;Visual System,&quot; &quot;Motor System&quot;) without explicitly presenting the underlying neural connections or hierarchical relationships among structures. This limited internal organization may have rendered the categories too abstract, thereby increasing cognitive load rather than providing meaningful scaffolding for learning.[[&lt;reflink idref=&quot;bib97&quot; id=&quot;ref90&quot;&gt;97&lt;/reflink&gt;]] Indeed, rather than aiding memory, categorization impaired the ability to remember specific functional outcomes associated with individual neural connections. Such impairment likely arose because students struggled to effectively integrate the abstract category labels with the functional information, thereby hindering comprehension and application. Future research could explore more structured hierarchical categorizations or explicitly linking system labels with concrete neural connections to reduce cognitive load and enhance students&#39; ability to integrate and apply their neuroanatomical knowledge.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-42&quot;&gt;Insights from training data&lt;/hd&gt; &lt;p&gt;Significant effects emerged in the final memory tests (after a 4.5‐week delay) that were not observed during the training sessions. Notably, Experiment 1 training involved two alternative multiple‐choice questions, while Experiment 2 training used four alternative multiple‐choice questions. These formats are easier and less sensitive than the 48‐alternative multiple‐choice and cued recall formats used in the final memory tests. However, both experiments demonstrated reliable improvements in accuracy across training sessions, indicating meaningful learning over time. Therefore, the pattern across training and final memory tests suggests that the benefits of the described neural connections may be specific to long‐term retention rather than initial learning.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-43&quot;&gt;Limitations of the current study&lt;/hd&gt; &lt;p&gt;Results suggest that described neural connections with functional outcomes can enhance long‐term memory of neuroanatomy. However, neural connections can be represented in multiple ways beyond verbal descriptions of function. For instance, the white matter fiber tracts that form neural connections have names, and they can be visualized using diffusion tractography,[&lt;reflink idref=&quot;bib99&quot; id=&quot;ref91&quot;&gt;99&lt;/reflink&gt;] fMRI functional connectivity,[&lt;reflink idref=&quot;bib31&quot; id=&quot;ref92&quot;&gt;31&lt;/reflink&gt;] anterograde and retrograde tracing,[&lt;reflink idref=&quot;bib100&quot; id=&quot;ref93&quot;&gt;100&lt;/reflink&gt;] electrophysiology,[&lt;reflink idref=&quot;bib101&quot; id=&quot;ref94&quot;&gt;101&lt;/reflink&gt;] and optogenetics.[&lt;reflink idref=&quot;bib102&quot; id=&quot;ref95&quot;&gt;102&lt;/reflink&gt;] Likewise, mental models of systems and events are multidimensional representations with functional, spatial, temporal, causal, and other contextual features.[[&lt;reflink idref=&quot;bib84&quot; id=&quot;ref96&quot;&gt;84&lt;/reflink&gt;]] Additional components of neural connectivity could help students construct richly connected mental models of the brain that enhance long‐term memory.&lt;/p&gt; &lt;p&gt;From the perspective of Bloom&#39;s taxonomy of educational objectives,[[&lt;reflink idref=&quot;bib103&quot; id=&quot;ref97&quot;&gt;103&lt;/reflink&gt;], [&lt;reflink idref=&quot;bib105&quot; id=&quot;ref98&quot;&gt;105&lt;/reflink&gt;]] the training manipulations and memory tests were limited to knowledge (Level 1 of Bloom&#39;s taxonomy) and comprehension (Level 2), without extending to application or analysis (Levels 3 and 4, respectively). Specifically, the tests in Experiment 1 were at Level 1 of Bloom&#39;s taxonomy. Experiment 2 reaches Level 2 (Functional Outcomes and Clinical Applications) and possibly Level 3 (Visual Transfer) of the Blooming Anatomy Tool. The Clinical Application question falls short, as removing the first half of the sentence would still produce the correct answer. For a robust clinical application, it would be helpful to display an MRI showing a lesion in the area of the inferior colliculus and medial geniculate nucleus.&lt;/p&gt; &lt;p&gt;Most students at each institution were female. Each course was offered in the psychology department of the respective institution, and the ratio of male to female students in each study aligned with the gender demographics of psychology majors at each university. While we might otherwise predict a gender difference for some of the memory tests in the near transfer condition that required some degree of mental rotation,[&lt;reflink idref=&quot;bib106&quot; id=&quot;ref99&quot;&gt;106&lt;/reflink&gt;] gender differences were not observed in this study.&lt;/p&gt; &lt;p&gt;Our findings suggest that connectivity‐based neuroanatomy instruction can be successfully integrated into introductory neuroscience courses typically taken by second‐ or third‐year undergraduates. Future studies could examine samples of more experienced students, such as those enrolled in medical schools, whose prior knowledge and cognitive strategies may differ significantly due to their focused biomedical training.[[&lt;reflink idref=&quot;bib107&quot; id=&quot;ref100&quot;&gt;107&lt;/reflink&gt;]] Specifically, medical students might employ distinct learning approaches even in their initial training stages, potentially influencing their ability to integrate and apply functional neural connections effectively.[[&lt;reflink idref=&quot;bib34&quot; id=&quot;ref101&quot;&gt;34&lt;/reflink&gt;], [&lt;reflink idref=&quot;bib109&quot; id=&quot;ref102&quot;&gt;109&lt;/reflink&gt;]] Investigating these differences could help optimize instructional approaches tailored to diverse educational backgrounds and learning contexts.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-44&quot;&gt;CONCLUSIONS&lt;/hd&gt; &lt;p&gt;Neuroscience research has shifted from studying individual brain structures to exploring their connections. Incorporating information about neural connections into neuroanatomy education may facilitate a deeper understanding and longer‐lasting memory of the material. Classroom‐based findings here challenge the traditional emphasis within neuroanatomy education on individual brain structures in favor of a more integrated learning approach that reflects the brain&#39;s interconnected nature. Focusing on neural connections can improve undergraduate neuroscience education by leveraging the connectivity paradigm shift in neuroscience research.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-45&quot;&gt;AUTHOR CONTRIBUTIONS&lt;/hd&gt; &lt;p&gt;Nicholas C. Hindy, Anthony J. Bishara, and John R. Pani designed the experiments, reviewed the analyses, discussed the results, and wrote the manuscript. John R. Pani created the &quot;Show Me the Brain!!&quot; software platform for neuroanatomy training and final memory tests. Nicholas C. Hindy coordinated data collection and performed the analyses.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-46&quot;&gt;ACKNOWLEDGMENTS&lt;/hd&gt; &lt;p&gt;The authors thank Dr. Brendan Depue for data collection at the University of Louisville and the undergraduate students who participated in these classroom‐based experiments as part of their introductory neuroscience coursework.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-47&quot;&gt;FUNDING INFORMATION&lt;/hd&gt; &lt;p&gt;This work was supported by the National Science Foundation&#39;s Improving Undergraduate STEM Education (IUSE) initiative (NSF Award no.: 2315440: An Online Platform for Learning Neuroanatomy from Neural Connectivity; PI: Nicholas Hindy).&lt;/p&gt; &lt;hd id=&quot;AN0186371261-48&quot;&gt;CONFLICT OF INTEREST STATEMENT&lt;/hd&gt; &lt;p&gt;The authors declare no conflicts of interest.&lt;/p&gt; &lt;hd id=&quot;AN0186371261-49&quot;&gt;DATA AVAILABILITY STATEMENT&lt;/hd&gt; &lt;p&gt;Data and other materials for each experiment are publicly available in an Open Science Framework repository that can be accessed at https://osf.io/dnu9x.&lt;/p&gt; &lt;p&gt;GRAPH: Table&#160;S1.Table&#160;S2.&lt;/p&gt; &lt;ref id=&quot;AN0186371261-50&quot;&gt; &lt;title&gt; REFERENCES &lt;/title&gt; &lt;blist&gt; &lt;bibl id=&quot;bib1&quot; idref=&quot;ref1&quot; type=&quot;bt&quot;&gt;1&lt;/bibl&gt; &lt;bibtext&gt; Rochon C, Otazu G, Kurtzer IL, Stout RF Jr, Ramos RL. Quantitative indicators of continued growth in undergraduate neuroscience education in the US. J Undergrad Neurosci Educ. 2019 ; 18 (1): A51.&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibl id=&quot;bib2&quot; idref=&quot;ref2&quot; type=&quot;bt&quot;&gt;2&lt;/bibl&gt; &lt;bibtext&gt; Javaid MA, Chakraborty S, Cryan JF, Schellekens H, Toulouse A. Understanding neurophobia: reasons behind impaired understanding and learning of neuroanatomy in cross‐disciplinary healthcare students. Anat Sci Educ. 2018 ; 11 (1): 81 – 93. https://doi.org/10.1002/ase.1711&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibl id=&quot;bib3&quot; idref=&quot;ref31&quot; type=&quot;bt&quot;&gt;3&lt;/bibl&gt; &lt;bibtext&gt; Fantaneanu TA, Moreau K, Eady K, Clarkin C, DeMeulemeester C, Maclean H, et al. Neurophobia inception: a study of trainees&#39; perceptions of neurology education. Can J Neurol Sci. 2014 ; 41 (4): 421 – 429. https://doi.org/10.1017/S0317167100018479&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibl id=&quot;bib4&quot; idref=&quot;ref3&quot; type=&quot;bt&quot;&gt;4&lt;/bibl&gt; &lt;bibtext&gt; Abushouk AI, Duc NM. Curing neurophobia in medical schools: evidence‐based strategies. Med Educ Online. 2016 ; 21 (1): 32476. https://doi.org/10.3402/meo.v21.32476&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibl id=&quot;bib5&quot; type=&quot;bt&quot;&gt;5&lt;/bibl&gt; &lt;bibtext&gt; Hall S, Stephens J, Parton W, Myers M, Harrison C, Elmansouri A, et al. Identifying medical student perceptions on the difficulty of learning different topics of the undergraduate anatomy curriculum. Med Sci Educ. 2018 ; 28 (3): 469 – 472. https://doi.org/10.1007/s40670‐018‐0572‐z&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibl id=&quot;bib6&quot; idref=&quot;ref4&quot; type=&quot;bt&quot;&gt;6&lt;/bibl&gt; &lt;bibtext&gt; Custers EJFM. Long‐term retention of basic science knowledge: a review study. Adv Health Sci Educ. 2010 ; 15 (1): 109 – 128. https://doi.org/10.1007/s10459‐008‐9101‐y&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibl id=&quot;bib7&quot; type=&quot;bt&quot;&gt;7&lt;/bibl&gt; &lt;bibtext&gt; D&#39;Eon MF. Knowledge loss of medical students on first year basic science courses at the University of Saskatchewan. BMC Med Educ. 2006 ; 6 (1): 1 – 6. https://doi.org/10.1186/1472‐6920‐6‐5&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibl id=&quot;bib8&quot; idref=&quot;ref5&quot; type=&quot;bt&quot;&gt;8&lt;/bibl&gt; &lt;bibtext&gt; Sugand K, Abrahams P, Khurana A. The anatomy of anatomy: a review for its modernization. Anat Sci Educ. 2010 ; 3 (2): 83 – 93. https://doi.org/10.1002/ase.139&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibl id=&quot;bib9&quot; type=&quot;bt&quot;&gt;9&lt;/bibl&gt; &lt;bibtext&gt; Arantes M, Andrade JP, Barbosa J, Ferreira MA. Curricular changes: the impact on medical students knowledge of neuroanatomy. BMC Med Educ. 2020 ; 20 (1): 20. https://doi.org/10.1186/s12909‐020‐1965‐4&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Bassett DS, Sporns O. Network neuroscience. Nat Neurosci. 2017 ; 20 (3): 353 – 364. https://doi.org/10.1038/nn.4502&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Thiebaut de Schotten M, Forkel SJ. The emergent properties of the connected brain. Science. 2022 ; 378 (6619): 505 – 510. https://doi.org/10.1126/science.abq2591&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Raichle ME. A paradigm shift in functional brain imaging. J Neurosci. 2009 ; 29 (41): 12729 – 12734. https://doi.org/10.1523/JNEUROSCI.4366‐09.2009&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Elam JS, Glasser MF, Harms MP, Sotiropoulos SN, Andersson JL, Burgess GC, et al. The human connectome project: a retrospective. NeuroImage. 2021 ; 244 : 118543. https://doi.org/10.1016/j.neuroimage.2021.118543&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Glasser MF, Smith SM, Marcus DS, Andersson JL, Auerbach EJ, Behrens TE, et al. The human connectome project&#39;s neuroimaging approach. Nat Neurosci. 2016 ; 19 (9): 1175 – 1187. https://doi.org/10.1038/nn.4361&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, et al. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging. 2001 ; 13 (4): 534 – 546. https://doi.org/10.1002/jmri.1076&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Tournier JD, Mori S, Leemans A. Diffusion tensor imaging and beyond. Magn Reson Med. 2011 ; 65 (6): 1532 – 1556. https://doi.org/10.1002/mrm.22924&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Sporns O. Networks of the brain. Cambridge, MA : MIT Press ; 2010. https://doi.org/10.7551/mitpress/9780262014694.001.0001&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Catani M, De Schotten MT. Atlas of human brain connections. Oxford, UK : Oxford University Press ; 2012. https://doi.org/10.1093/med/9780199541164.001.0001&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Razi A, Seghier ML, Zhou Y, McColgan P, Zeidman P, Park HJ, et al. Large‐scale DCMs for resting‐state fMRI. Netw Neurosci. 2017 ; 1 (3): 222 – 241. https://doi.org/10.1162/NETN_a_00015&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Lee MH, Smyser CD, Shimony JS. Resting‐state fMRI: a review of methods and clinical applications. Am J Neuroradiol. 2013 ; 34 (10): 1866 – 1872. https://doi.org/10.3174/ajnr.A3263&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Cole MW, Ito T, Cocuzza C, Sanchez‐Romero R. The functional relevance of task‐state functional connectivity. J Neurosci. 2021 ; 41 (12): 2684 – 2702. https://doi.org/10.1523/JNEUROSCI.1713‐20.2021&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Hindy NC, Avery EW, Turk‐Browne NB. Hippocampal‐neocortical interactions sharpen over time for predictive actions. Nat Commun. 2019 ; 10 (1): 1 – 13. https://doi.org/10.1038/s41467‐019‐12016‐9&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Seeley WW. The salience network: a neural system for perceiving and responding to homeostatic demands. J Neurosci. 2019 ; 39 (50): 9878 – 9882. https://doi.org/10.1523/JNEUROSCI.1138‐17.2019&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Fox MD, Corbetta M, Snyder AZ, Vincent JL, Raichle ME. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc Natl Acad Sci. 2006 ; 103 (26): 10046 – 10051. https://doi.org/10.1073/pnas.0604187103&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Dodds CM, Morein‐Zamir S, Robbins TW. Dissociating inhibition, attention, and response control in the frontoparietal network using functional magnetic resonance imaging. Cereb Cortex. 2011 ; 21 (5): 1155 – 1165. https://doi.org/10.1093/cercor/bhq187&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Rocca MA, Absinta M, Valsasina P, Ciccarelli O, Marino S, Rovira A, et al. Abnormal connectivity of the sensorimotor network in patients with MS: a multicenter fMRI study. Hum Brain Mapp. 2009 ; 30 (8): 2412 – 2425. https://doi.org/10.1002/hbm.20679&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Uddin LQ, Yeo BTT, Spreng RN. Towards a universal taxonomy of macro‐scale functional human brain networks. Brain Topogr. 2019 ; 32 (6): 926 – 942. https://doi.org/10.1007/s10548‐019‐00744‐6&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Marek S, Dosenbach NUF. The frontoparietal network: function, electrophysiology, and importance of individual precision mapping. Dialogues Clin Neurosci. 2018 ; 20 (2): 133 – 140. https://doi.org/10.31887/DCNS.2018.20.2/smarek&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Forkel SJ, Friedrich P, Thiebaut de Schotten M, Howells H. White matter variability, cognition, and disorders: a systematic review. Brain Struct Funct. 2022 ; 227 (2): 529 – 544. https://doi.org/10.1007/s00429‐021‐02382‐w&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Hollunder B, Ostrem JL, Sahin IA, Rajamani N, Oxenford S, Butenko K, et al. Mapping dysfunctional circuits in the frontal cortex using deep brain stimulation. Nat Neurosci. 2024 ; 27 (3): 573 – 586. https://doi.org/10.1038/s41593‐024‐01570‐1&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, Chun MM, et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci. 2015 ; 18 (11): 1664 – 1671. https://doi.org/10.1038/nn.4135&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Sui J, Jiang R, Bustillo J, Calhoun V. Neuroimaging‐based individualized prediction of cognition and behavior for mental disorders and health: methods and promises. Biol Psychiatry. 2020 ; 88 (11): 818 – 828. https://doi.org/10.1016/j.biopsych.2020.02.016&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Zimmerman WD, Pergakis MB, Gorman EF, Motta M, Jin PH, Salas RME, et al. Scoping review: innovations in clinical neurology education. Neurol Educ. 2023 ; 2 (1): e200048. https://doi.org/10.1212/NE9.0000000000200048&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Sotgiu MA, Mazzarello V, Bandiera P, Madeddu R, Montella A, Moxham B. Neuroanatomy, the Achille&#39;s heel of medical students. A systematic analysis of educational strategies for the teaching of neuroanatomy. Anat Sci Educ. 2020 ; 13 (1): 107 – 116. https://doi.org/10.1002/ase.1866&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Breedlove SM, Watson NV. Behavioral Neuroscience. 10th ed. Sunderland, MA : Sinauer Associates, Incorporated Publishers ; 2023.&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Gazzaniga M, Ivry RB, Mangun GR. Cognitive neuroscience: the biology of the mind. 5th ed. New York, London : W. W. Norton &amp;amp; Company ; 2018 768 p.&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Kalat JW. Biological psychology. 14th ed. Boston, MA : Cengage Learning ; 2024.&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Kolb B, Whishaw IQ, Teskey GC. An introduction to brain and behavior. 7th ed. New York, NY : Worth Publishers ; 2022.&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Buttner‐Ennever JA, Cohen B, Horn AKE, Reisine H. Efferent pathways of the nucleus of the optic tract in monkey and their role in eye movements. J Comp Neurol. 1996 ; 373 (1): 90 – 107.&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Ryugo DK, Milinkeviciute G. Differential projections from the cochlear nucleus to the inferior colliculus in the mouse. Front Neural Circuits. 2023 ; 17 : 1229746. https://doi.org/10.3389/fncir.2023.1229746&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Catrambone R. The subgoal learning model: creating better examples so that students can solve novel problems. J Exp Psychol Gen. 1998 ; 127 (4): 355 – 376. https://doi.org/10.1037/0096‐3445.127.4.355&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Chi MT, Feltovich PJ, Glaser R. Categorization and representation of physics problems by experts and novices. Cogn Sci. 1981 ; 5 (2): 121 – 152. https://doi.org/10.1207/s15516709cog0502_2&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Hardiman PT, Dufresne R, Mestre JP. The relation between problem categorization and problem solving among experts and novices. Mem Cogn. 1989 ; 17 (5): 627 – 638. https://doi.org/10.3758/BF03197085&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Dufresne RJ, Gerace WJ, Hardiman PT, Mestre JP. Constraining novices to perform expertlike problem analyses: effects on schema acquisition. J Learn Sci. 1992 ; 2 (3): 307 – 331. https://doi.org/10.1207/s15327809jls0203_3&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Schmidt HG, Rikers RMJP. How expertise develops in medicine: knowledge encapsulation and illness script formation. Med Educ. 2007 ; 41 (12): 1133 – 1139. https://doi.org/10.1111/j.1365‐2923.2007.02915.x&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Bransford JD, Johnson MK. Contextual prerequisites for understanding: some investigations of comprehension and recall. J Verbal Learn Verbal Behav. 1972 ; 11 (6): 717 – 726. https://doi.org/10.1016/S0022‐5371(72)80006‐9&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Jonassen DH, Rohrer‐Murphy L. Activity theory as a framework for designing constructivist learning environments. Educ Technol Res Dev. 1999 ; 47 (1): 61 – 79. https://doi.org/10.1007/BF02299477&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Hmelo‐Silver CE, Pfeffer MG. Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cogn Sci. 2004 ; 28 (1): 127 – 138. https://doi.org/10.1207/s15516709cog2801_7&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Zwaan RA. Situation models, mental simulations, and abstract concepts in discourse comprehension. Psychon Bull Rev. 2016 ; 23 (4): 1028 – 1034. https://doi.org/10.3758/s13423‐015‐0864‐x&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Zwaan RA, Radvansky GA. Situation models in language comprehension and memory. Psychol Bull. 1998 ; 123 : 162 – 185. https://doi.org/10.1037/0033‐2909.123.2.162&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Rudy JW, Sutherland RJ. Configural and elemental associations and the memory coherence problem. J Cogn Neurosci. 1992 ; 4 (3): 208 – 216. https://doi.org/10.1162/jocn.1992.4.3.208&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Lin TC, Hsu YS, Lin SS, Changlai ML, Yang KY, Lai TL. A review of empirical evidence on scaffolding for science education. Int J Sci Math Educ. 2012 ; 10 (2): 437 – 455. https://doi.org/10.1007/s10763‐011‐9310‐7&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Basile GA, Quartu M, Bertino S, Serra MP, Boi M, Bramanti A, et al. Red nucleus structure and function: from anatomy to clinical neurosciences. Brain Struct Funct. 2021 ; 226 (1): 69 – 91. https://doi.org/10.1007/s00429‐020‐02171‐x&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Pani JR. Show Me the Brain!! [software sytem] J.R. Pani Instructional Systems; 2025. Described at: https://showmethebrain.com&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Chariker JH, Naaz F, Pani JR. Computer‐based learning of neuroanatomy: a longitudinal study of learning, transfer, and retention. J Educ Psychol. 2011 ; 103 (1): 19 – 31. https://doi.org/10.1037/a0021604&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Chariker JH, Naaz F, Pani JR. Item difficulty in the evaluation of computer‐based instruction: an example from neuroanatomy. Anat Sci Educ. 2012 ; 5 (2): 63 – 75. https://doi.org/10.1002/ase.1260&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Naaz F, Chariker JH, Pani JR. Computer‐based learning: graphical integration of whole and sectional neuroanatomy improves long‐term retention. Cogn Instr. 2014 ; 32 (1): 44 – 64. https://doi.org/10.1080/07370008.2013.857672&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Pani JR, Chariker JH, Naaz F. Computer‐based learning: interleaving whole and sectional representation of neuroanatomy. Anat Sci Educ. 2013 ; 6 (1): 11 – 18. https://doi.org/10.1002/ase.1297&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Pani JR, Chariker JH, Naaz F, Mattingly W, Roberts J, Sephton SE. Learning with interactive computer graphics in the undergraduate neuroscience classroom. Adv Health Sci Educ. 2014 ; 19 (4): 507 – 528. https://doi.org/10.1007/s10459‐013‐9483‐3&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Ratiu P, Hillen B, Glaser J, Jenkins DP. Visible human 2.0‐the next generation. Stud Health Technol Inform. 2003 ; 94 : 275 – 281. https://doi.org/10.3233/978‐1‐60750‐938‐7‐275&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Carrier M, Pashler H. The influence of retrieval on retention. Mem Cogn. 1992 ; 20 (6): 633 – 642. https://doi.org/10.3758/BF03202713&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Roediger HL, Karpicke JD. Test‐enhanced learning: taking memory tests improves long‐term retention. Psychol Sci. 2006 ; 17 (3): 249 – 255. https://doi.org/10.1111/j.1467‐9280.2006.01693.x&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Rowland CA. The effect of testing versus restudy on retention: a meta‐analytic review of the testing effect. Psychol Bull. 2014 ; 140 (6): 1432 – 1463. https://doi.org/10.1037/a0037559&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Butler AC, Marsh EJ, Slavinsky JP, Baraniuk RG. Integrating cognitive science and technology improves learning in a STEM classroom. Educ Psychol Rev. 2014 ; 26 (2): 331 – 340. https://doi.org/10.1007/s10648‐014‐9256‐4&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Carpenter SK, Pan SC, Butler AC. The science of effective learning with spacing and retrieval practice. Nat Rev Psychol. 2022 ; 1 (9): 496 – 511. https://doi.org/10.1038/s44159‐022‐00089‐1&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Hebb DO. The organization of behavior: a neuropsychological theory [Internet]. Hove, UK : Psychology Press ; 1949 [cited 2025 Mar 11]. Available from: https://&lt;ulink href=&quot;http://www.taylorfrancis.com/books/mono/10.4324/9781410612403/organization&amp;amp;#8208;behavior&amp;amp;#8208;hebb&quot;&gt;www.taylorfrancis.com/books/mono/10.4324/9781410612403/organization&amp;amp;#8208;behavior&amp;amp;#8208;hebb&lt;/ulink&gt;&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Liu XL, Ranganath C, O&#39;Reilly RC. A complementary learning systems model of how sleep moderates retrieval practice effects. Psychon Bull Rev. 2024 ; 31 (5): 2022 – 2035. https://doi.org/10.3758/s13423‐024‐02489‐1&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Antony JW, Ferreira CS, Norman KA, Wimber M. Retrieval as a fast route to memory consolidation. Trends Cogn Sci. 2017 ; 21 (8): 573 – 576. https://doi.org/10.1016/j.tics.2017.05.001&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Hopkins RF, Lyle KB, Hieb JL, Ralston PAS. Spaced retrieval practice increases college students&#39; short‐ and long‐term retention of mathematics knowledge. Educ Psychol Rev. 2016 ; 28 (4): 853 – 873. https://doi.org/10.1007/s10648‐015‐9349‐8&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Larsen DP, Butler AC, Roediger Iii HL. Test‐enhanced learning in medical education. Med Educ. 2008 ; 42 (10): 959 – 966. https://doi.org/10.1111/j.1365‐2923.2008.03124.x&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Thompson CP, Hughes MA. The effectiveness of spaced learning, interleaving, and retrieval practice in radiology education: a systematic review. J Am Coll Radiol. 2023 ; 20 (11): 1092 – 1101. https://doi.org/10.1016/j.jacr.2023.06.013&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Sanders LLO, Ponte RP, Viana AB, Peixoto AA, Kubrusly M, Leit&#227;o AMF. Retrieval‐based learning in neuroanatomy classes. Rev Bras Educ Med. 2019 ; 43 : 92 – 98. https://doi.org/10.1590/1981‐52712015v43n4RB20180184ingles&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Amazon Web Services. Amazon AppStream 2.0 [Internet]. 2021 [cited 2022 Jul 19]. Available from: https://aws.amazon.com/pm/appstream2/&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Microsoft Corporation. Microsoft Azure Lab Services [Internet]. 2022 [cited 2023 Jan 16]. Available from: https://azure.microsoft.com/services/lab‐services/&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Cepeda NJ, Pashler H, Vul E, Wixted JT, Rohrer D. Distributed practice in verbal recall tasks: a review and quantitative synthesis. Psychol Bull. 2006 ; 132 (3): 354 – 380. https://doi.org/10.1037/0033‐2909.132.3.354&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Navarro G. A guided tour to approximate string matching. ACM Comput Surv. 2001 ; 33 (1): 31 – 88. https://doi.org/10.1145/375360.375365&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; The MathWorks, Inc. MATLAB (R2023a) [Internet]. 2023. Available from: https://&lt;ulink href=&quot;http://www.mathworks.com/products/matlab.html&quot;&gt;www.mathworks.com/products/matlab.html&lt;/ulink&gt;&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Cohen J. Statistical power analysis for the behavioral sciences. Abingdon, UK : Routledge ; 1988. https://doi.org/10.4324/9780203771587&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Anderson JR, Reder LM. An elaborative processing explanation of depth of processing. In: Cermak LS, Craik FIM, editors. Levels of processing in human memory. Hillsdale, NJ : Erlbaum ; 1979. p. 385 – 404.&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Craik FI. Levels of processing: past, present... and future? Memory. 2002 ; 10 (5–6): 305 – 318. https://doi.org/10.1080/09658210244000135&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Tulving E, Thomson DM. Retrieval processes in recognition memory: effects of associative context. J Exp Psychol. 1971 ; 87 (1): 116 – 124. https://doi.org/10.1037/h0030186&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Black JB, Bern H. Causal coherence and memory for events in narratives. J Verbal Learn Verbal Behav. 1981 ; 20 (3): 267 – 275. https://doi.org/10.1016/S0022‐5371(81)90337‐5&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Chi MT. Three types of conceptual change: belief revision, mental model transformation, and categorical shift. International handbook of research on conceptual change. Abingdon, UK : Routledge ; 2009. p. 89 – 110.&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Radvansky GA, Zacks JM. Event cognition. Oxford, UK : Oxford University Press ; 2014.&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Johnson‐Laird PN. Mental models: towards a cognitive science of language, inference, and consciousness. Cambridge, MA : Harvard University Press ; 1983.&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Deshpande G, Santhanam P, Hu X. Instantaneous and causal connectivity in resting state brain networks derived from functional MRI data. NeuroImage. 2011 ; 54 (2): 1043 – 1052. https://doi.org/10.1016/j.neuroimage.2010.09.024&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Klement BJ, Paulsen DF, Wineski LE. Anatomy as the backbone of an integrated first year medical curriculum: design and implementation. Anat Sci Educ. 2011 ; 4 (3): 157 – 169. https://doi.org/10.1002/ase.217&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Brooks WS, Woodley KTCP, Jackson JR, Hoesley CJ. Integration of gross anatomy in an organ system‐based medical curriculum: strategies and challenges. Anat Sci Educ. 2015 ; 8 (3): 266 – 274. https://doi.org/10.1002/ase.1483&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Bishara AJ, Jacoby LL. Aging, spaced retrieval, and inflexible memory performance. Psychon Bull Rev. 2008 ; 15 (1): 52 – 57. https://doi.org/10.3758/pbr.15.1.52&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Miyatsu T, McDaniel MA. Adding the keyword mnemonic to retrieval practice: a potent combination for foreign language vocabulary learning? Mem Cogn. 2019 ; 47 (7): 1328 – 1343. https://doi.org/10.3758/s13421‐019‐00936‐2&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Morris PE, Fritz CO, Jackson L, Nichol E, Roberts E. Strategies for learning proper names: expanding retrieval practice, meaning and imagery. Appl Cogn Psychol. 2005 ; 19 (6): 779 – 798. https://doi.org/10.1002/acp.1115&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Fritz CO, Morris PE, Acton M, Voelkel AR, Etkind R. Comparing and combining retrieval practice and the keyword mnemonic for foreign vocabulary learning. Appl Cogn Psychol. 2007 ; 21 (4): 499 – 526. https://doi.org/10.1002/acp.1287&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; McDaniel MA. Combining retrieval practice with elaborative encoding: complementary or redundant? Educ Psychol Rev. 2023 ; 35 (3): 75. https://doi.org/10.1007/s10648‐023‐09784‐8&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Bower GH, Clark MC, Lesgold AM, Winzenz D. Hierarchical retrieval schemes in recall of categorized word lists. J Verbal Learn Verbal Behav. 1969 ; 8 (3): 323 – 343. https://doi.org/10.1016/S0022‐5371(69)80069‐1&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Norman G. Teaching basic science to optimize transfer. Med Teach. 2009 ; 31 (9): 807 – 811. https://doi.org/10.1080/01421590903049814&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Naim M, Katkov M, Recanatesi S, Tsodyks M. Emergence of hierarchical organization in memory for random material. Sci Rep. 2019 ; 9 (1): 10448. https://doi.org/10.1038/s41598‐019‐46908‐z&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Sweller J. Cognitive load during problem solving: effects on learning. Cogn Sci. 1988 ; 12 (2): 257 – 285. https://doi.org/10.1016/0364‐0213(88)90023‐7&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Van Merri&#235;nboer JJG, Sweller J. Cognitive load theory in health professional education: design principles and strategies: cognitive load theory. Med Educ. 2010 ; 44 (1): 85 – 93. https://doi.org/10.1111/j.1365‐2923.2009.03498.x&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Maier‐Hein KH, Neher PF, Houde JC, C&#244;t&#233; MA, Garyfallidis E, Zhong J, et al. The challenge of mapping the human connectome based on diffusion tractography. Nat Commun. 2017 ; 8 (1): 1349. https://doi.org/10.1038/s41467‐017‐01285‐x&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Zampieri N, Jessell TM, Murray AJ. Mapping sensory circuits by anterograde transsynaptic transfer of recombinant rabies virus. Neuron. 2014 ; 81 (4): 766 – 778. https://doi.org/10.1016/j.neuron.2013.12.033&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Sch&#246;lvinck ML, Leopold DA, Brookes MJ, Khader PH. The contribution of electrophysiology to functional connectivity mapping. NeuroImage. 2013 ; 80 : 297 – 306. https://doi.org/10.1016/j.neuroimage.2013.04.010&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Linders LE, Supiot LF, Du W, D&#39;Angelo R, Adan RA, Riga D, et al. Studying synaptic connectivity and strength with optogenetics and patch‐clamp electrophysiology. Int J Mol Sci. 2022 ; 23 (19): 11612. https://doi.org/10.3390/ijms231911612&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Bloom BS. Taxonomy of educational objectives: the classification of educational goals. In: Cognitive domain. London : Longmans ; 1956.&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Anderson LW, Krathwohl DR. A taxonomy for learning, teaching, and assessing: a revision of Bloom&#39;s taxonomy of educational objectives. London, UK : Longman ; 2001.&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Thompson AR, O&#39;Loughlin VD. The Blooming Anatomy Tool (BAT): a discipline‐specific rubric for utilizing Bloom&#39;s taxonomy in the design and evaluation of assessments in the anatomical sciences. Anat Sci Educ. 2015 ; 8 (6): 493 – 501. https://doi.org/10.1002/ase.1507&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Roach VA, Mi M, Mussell J, Van Nuland SE, Lufler RS, DeVeau KM, et al. Correlating spatial ability with anatomy assessment performance: a meta‐analysis. Anat Sci Educ. 2021 ; 14 (3): 317 – 329. https://doi.org/10.1002/ase.2029&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Pandey P, Zimitat C. Medical students&#39; learning of anatomy: memorisation, understanding and visualisation. Med Educ. 2007 ; 41 (1): 7 – 14. https://doi.org/10.1111/j.1365‐2929.2006.02643.x&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Woods NN, Brooks LR, Norman GR. The role of biomedical knowledge in diagnosis of difficult clinical cases. Adv Health Sci Educ. 2007 ; 12 (4): 417 – 426. https://doi.org/10.1007/s10459‐006‐9054‐y&lt;/bibtext&gt; &lt;/blist&gt; &lt;blist&gt; &lt;bibtext&gt; Bergman EM, Sieben JM, Smailbegovic I, De Bruin ABH, Scherpbier AJJA, Van Der Vleuten CPM. Constructive, collaborative, contextual, and self‐directed learning in surface anatomy education. Anat Sci Educ. 2013 ; 6 (2): 114 – 124. https://doi.org/10.1002/ase.1306&lt;/bibtext&gt; &lt;/blist&gt; &lt;/ref&gt; &lt;aug&gt; &lt;p&gt;By Nicholas C. Hindy; Anthony J. Bishara and John R. Pani&lt;/p&gt; &lt;p&gt;Reported by Author; Author; Author&lt;/p&gt; &lt;p&gt;&lt;/p&gt; &lt;p&gt;Nicholas C. Hindy, Ph.D., is an Assistant Professor of Psychology at the College of Charleston in Charleston, South Carolina. His research focuses on learning and memory, particularly within the context of neuroanatomy instruction. He teaches undergraduate courses in neuroscience and research methods.&lt;/p&gt; &lt;p&gt;Anthony J. Bishara, Ph.D., is a Professor of Psychology at the College of Charleston in Charleston, South Carolina. His research focuses on statistics, memory, judgment, and decision‐making. He teaches undergraduate courses in statistics and cognitive psychology.&lt;/p&gt; &lt;p&gt;John R. Pani, Ph.D., is Professor Emeritus of Psychological and Brain Sciences at the University of Louisville in Louisville, Kentucky, and the founder of J. R. Pani Instructional Systems, LLC. 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  Data: Described Neural Connections Enhance Classroom Learning of Neuroanatomy
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
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  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Nicholas+C%2E+Hindy%22&quot;&gt;Nicholas C. Hindy&lt;/searchLink&gt; (ORCID &lt;externalLink term=&quot;https://orcid.org/0000-0003-1643-0251&quot;&gt;0000-0003-1643-0251&lt;/externalLink&gt;)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Anthony+J%2E+Bishara%22&quot;&gt;Anthony J. Bishara&lt;/searchLink&gt; (ORCID &lt;externalLink term=&quot;https://orcid.org/0000-0002-7771-3565&quot;&gt;0000-0002-7771-3565&lt;/externalLink&gt;)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22John+R%2E+Pani%22&quot;&gt;John R. Pani&lt;/searchLink&gt;
– Name: TitleSource
  Label: Source
  Group: Src
  Data: &lt;searchLink fieldCode=&quot;SO&quot; term=&quot;%22Anatomical+Sciences+Education%22&quot;&gt;&lt;i&gt;Anatomical Sciences Education&lt;/i&gt;&lt;/searchLink&gt;. 2025 18(7):642-656.
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  Data: Wiley. Available from: John Wiley &amp; Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 15
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: National Science Foundation (NSF)
– Name: NumberContract
  Label: Contract Number
  Group: NumCntrct
  Data: 2315440
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles&lt;br /&gt;Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: &lt;searchLink fieldCode=&quot;EL&quot; term=&quot;%22Higher+Education%22&quot;&gt;Higher Education&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;EL&quot; term=&quot;%22Postsecondary+Education%22&quot;&gt;Postsecondary Education&lt;/searchLink&gt;
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: &lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Anatomy%22&quot;&gt;Anatomy&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Undergraduate+Students%22&quot;&gt;Undergraduate Students&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Neurosciences%22&quot;&gt;Neurosciences&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Learning+Processes%22&quot;&gt;Learning Processes&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Feedback+%28Response%29%22&quot;&gt;Feedback (Response)&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Memory%22&quot;&gt;Memory&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Diagnostic+Tests%22&quot;&gt;Diagnostic Tests&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Brain+Hemisphere+Functions%22&quot;&gt;Brain Hemisphere Functions&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Computer+Assisted+Instruction%22&quot;&gt;Computer Assisted Instruction&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Teaching+Methods%22&quot;&gt;Teaching Methods&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Educational+Benefits%22&quot;&gt;Educational Benefits&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Comparative+Analysis%22&quot;&gt;Comparative Analysis&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Textbooks%22&quot;&gt;Textbooks&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Retention+%28Psychology%29%22&quot;&gt;Retention (Psychology)&lt;/searchLink&gt;
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1002/ase.70051
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1935-9772&lt;br /&gt;1935-9780
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Advances in brain imaging have led to a paradigm shift in neuroscience research, moving from focusing on individual brain structures to investigating neural networks and connections. However, neuroanatomy education still tends to concentrate on discrete brain regions. Two separate experiments in undergraduate neuroscience courses investigated whether incorporating neural connectivity into neuroanatomy education would enhance learning. Students in each experiment learned to identify brain structures through computer-based training sessions that provided text-based narrative feedback about neural connections, followed by final memory tests after a 1-month delay. The first experiment included 30 students and demonstrated a long-term memory benefit associated with described neural connections, showing a medium effect size (p = 0.01, d = 0.54) comparable to the established retrieval practice effect for enhancing long-term memory (p = 0.03, d = 0.47). The second experiment replicated the benefits of described neural connections with a small effect size (p = 0.005, d = 0.28) in a larger sample of 122 students across classrooms at two universities. Furthermore, students remembered the functional outcomes of neural connections from training (p &lt; 0.001, d = 0.46), and this generalized to clinical applications (p = 0.009, d = 0.27). In contrast, categorizing brain areas without describing neural connections (as is commonly done in introductory neuroscience textbook chapters) did not benefit either memory or generalization. Findings demonstrate that leveraging the connectivity paradigm shift in neuroscience research can enhance neuroanatomy education. Emphasizing neural connections and their functional outcomes helps simplify neuroanatomy and improve understanding and retention.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: Note
  Label: Notes
  Group: Note
  Data: https://osf.io/dnu9x
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1475993
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1475993
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      – Type: doi
        Value: 10.1002/ase.70051
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 642
    Subjects:
      – SubjectFull: Anatomy
        Type: general
      – SubjectFull: Undergraduate Students
        Type: general
      – SubjectFull: Neurosciences
        Type: general
      – SubjectFull: Learning Processes
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      – SubjectFull: Feedback (Response)
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      – SubjectFull: Diagnostic Tests
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      – SubjectFull: Brain Hemisphere Functions
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      – SubjectFull: Computer Assisted Instruction
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      – SubjectFull: Teaching Methods
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      – SubjectFull: Educational Benefits
        Type: general
      – SubjectFull: Comparative Analysis
        Type: general
      – SubjectFull: Textbooks
        Type: general
      – SubjectFull: Retention (Psychology)
        Type: general
    Titles:
      – TitleFull: Described Neural Connections Enhance Classroom Learning of Neuroanatomy
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            NameFull: Nicholas C. Hindy
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              Type: published
              Y: 2025
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          Titles:
            – TitleFull: Anatomical Sciences Education
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