Academic literature on the topic 'Brain-based learning theory'
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Journal articles on the topic "Brain-based learning theory"
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Handayani, Baiq Sri, and A. D. Corebima. "Model brain based learning (BBL) and whole brain teaching (WBT) in learning." International Journal of Science and Applied Science: Conference Series 1, no. 2 (August 14, 2017): 153. http://dx.doi.org/10.20961/ijsascs.v1i2.5142.
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<p class="Abstract">The learning process is a process of change in behavior as a form of the result of learning. The learning model is a crucial component of the success of the learning process. The learning model is growing fastly, and each model has different characteristics. Teachers are required to be able to understand each model to teach the students optimally by matching the materials and the learning model. The best of the learning model is the model that based on the brain system in learning that are the model of Brain Based Learning (BBL) and the model of Whole Brain Teaching (WBT). The purposes of this article are to obtain information related to (1) the brain’s natural learning system, (2) analyze the characteristics of the model BBL and WBT based on theory, brain sections that play a role associated with syntax, similarities, and differences, (3) explain the distinctive characteristics of both models in comparison to other models. The results of this study are: (1) the brain’s natural learning system are: (a) the nerves in each hemisphere do not work independently, (b) doing more activities can connect more brain nerves, (c) the right hemisphere controls the left side motoric sensor of the body, and vice versa; (2) the characteristics of BBL and WBT are: (a) BBL is based on the brain’s structure and function, while the model WBT is based on the instructional approach, neurolinguistic, and body language, (b) the parts of the brain that work in BBL are: cerebellum, cerebral cortex, frontal lobe, limbic system, and prefrontal cortex; whereas the parts that work WBT are: prefrontal cortex, visual cortex, motor cortex, limbic system, and amygdala, (c) the similarities between them are that they both rely on the brain’s system and they both promote gesture in learning, whereas the differences are on the view of the purposes of gestures and the learning theory that they rely on. BBL relies on cognitive theory while WBT relies on social theory; (3) the typical attribute of them compared to other models are that in BBL there are classical music and gestures in the form of easy exercises, while on the WBT model there are fast instructions and movements as instructions or code of every spoken word.</p>
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Lucas, Caro, Danial Shahmirzadi, and Nima Sheikholeslami. "Introducing Belbic: Brain Emotional Learning Based Intelligent Controller." Intelligent Automation & Soft Computing 10, no. 1 (January 2004): 11–21. http://dx.doi.org/10.1080/10798587.2004.10642862.
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Sesmiarni, Zulfani. "Brain Based Teaching Model as Transformation of Learning Paradigm in Higher Education." Al-Ta lim Journal 22, no. 3 (December 10, 2015): 266–75. http://dx.doi.org/10.15548/jt.v22i3.141.
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Brain -based teaching model is a new paradigm that can facilitate students in optimizing student learning by the functioning the brain as a whole. Lessons are held today assume that all students equally so that learning provide the same services to each student in the class. With this model, the students are given different stimulation according to their abilities and needs. Base on brain learning theory -based teaching, the learning should pay attention to the five needs of the brain in general. The fifth factor is the need for a sense of comfort, the need for interaction, the need for knowledge, the need for the activity and the need for self-reflection. All these needs will be connected if the lecturers able to present emotional learning, social learning, cognitive learning, physical learning and teaching reflection. Key Word : Instrucetional, Brain Based teaching, Learning.Copyright © 2015 by Al-Ta'lim All right reserved
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Kovalenko, Nataliya. "Astronomy: learning theories applicable for education in planetarium environment." EPJ Web of Conferences 200 (2019): 01014. http://dx.doi.org/10.1051/epjconf/201920001014.
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How do people learn in general and study astronomy in particular? To develop a coherent educational policy we need an appropriate theory. Does learning consist of the incremental addition of individual “bits” of information into the mind? Or is learning an active process that transforms the mind of the learner? Among different theories on how people learn are: Behaviorism, Neuroscience, Right Brain vs. Left Brain, Communities of Practice, Control Theory, Observational Learning (Social learning theory), Vygotsky and Social Cognition, Learning Styles, Piaget's theory, Constructivism, Brain-based Learning, Multiple Intelligences. These theories are described in brief. All of the above mentioned learning theories may be applicable to some extent in the case of astronomy education in a planetarium environment. Especially the Multiple Intelligences theory can be tested perfectly while teaching in Planetarium and thus should be taken into more thorough consideration. It is discussed what a planetarium may offer to the audience with different types of intelligences, according to the Multiple Intelligences approach.
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AlGhraibeh, Ahmad Mohamed Awad. "Learning and Thinking Styles Based on Whole Brain Theory in Relation to Emotional Intelligence." OALib 02, no. 05 (2015): 1–14. http://dx.doi.org/10.4236/oalib.1101436.
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Sesmiarni, Zulfani. "Model Brain Based Teaching Sebagai Transformasi Paradigma Pembelajaran di Perguruan Tinggi." Tadris: Jurnal Keguruan dan Ilmu Tarbiyah 1, no. 2 (December 19, 2016): 93. http://dx.doi.org/10.24042/tadris.v1i2.1059.
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Brain-based teaching model is a new paradigm that can facilities students in optimal study by using the whole brain function of the students. The common learning that we deal today is concern that all students is equal so that the learning give the same treatment to all students in class. Therefore, with this learning model students are given different stimulus based on their ability and needed. So, based on learning theory of brain-based teaching the learning must give attention on five brain needed on general. Those five factors are the need of comfortable, the need of interact, the need of knowledge, the need of activity and the need of self-reflection. All of those factors can be fulfilled if the lecturer able to apply emotional learning, social learning, cognitive learning, physical learning, and reflection learning.Model pembelajaran brain based teaching adalah sebuah paradigma baru yang dapat memfasilitasi peserta didik dalam optimalisasi pembelajaran dengan menggunakan fungsi keseluruhan otak pada peserta didik. Pembelajaran umum yang kita hadapi saat ini menitik beratkan pada persamaan dalam memandang peserta didik yang kemudian pembelajaran hanya memberikan perlakuan yang sama kepada peserta didik dalam satu kelas. Oleh karena itu, dengan model pembelajaran seperti ini diberikan rangsangan yang berbeda berdasarkan kemampuan mereka dan kebutuh. Jadi, berdasarkan teori brain based teaching pendidik harus memberikan perhatian pada lima aspek yang dibutuhkan secara umum. Kelima faktor tersebut adalah: kebutuhan kenyamanan, kebutuhan bagaimana berinteraksi, kebutuhan ilmu pengetahuan, kebutuhan beraktivitas dan kebutuhan merefleksi diri. Semua faktor tersebut dapat terpenuhi jika pendidik mampu menerapkan pembelajaran emosional, pembelajaran sosial, pembelajaran kognitif, pembelajaran fisik, dan pembelajaran yang bersifat reflektif.Keywords: brain-based teaching, teaching model, a new paradigm
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Fitch, W. Tecumseh, and Angela D. Friederici. "Artificial grammar learning meets formal language theory: an overview." Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1598 (July 19, 2012): 1933–55. http://dx.doi.org/10.1098/rstb.2012.0103.
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Formal language theory (FLT), part of the broader mathematical theory of computation, provides a systematic terminology and set of conventions for describing rules and the structures they generate, along with a rich body of discoveries and theorems concerning generative rule systems. Despite its name, FLT is not limited to human language, but is equally applicable to computer programs, music, visual patterns, animal vocalizations, RNA structure and even dance. In the last decade, this theory has been profitably used to frame hypotheses and to design brain imaging and animal-learning experiments, mostly using the ‘artificial grammar-learning’ paradigm. We offer a brief, non-technical introduction to FLT and then a more detailed analysis of empirical research based on this theory. We suggest that progress has been hampered by a pervasive conflation of distinct issues, including hierarchy, dependency, complexity and recursion. We offer clarifications of several relevant hypotheses and the experimental designs necessary to test them. We finally review the recent brain imaging literature, using formal languages, identifying areas of convergence and outstanding debates. We conclude that FLT has much to offer scientists who are interested in rigorous empirical investigations of human cognition from a neuroscientific and comparative perspective.
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Lakoff, George. "The role of the brain in the metaphorical mathematical cognition." Behavioral and Brain Sciences 31, no. 6 (December 2008): 658–59. http://dx.doi.org/10.1017/s0140525x08005748.
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AbstractRips et al. appear to discuss, and then dismiss with counterexamples, the brain-based theory of mathematical cognition given in Lakoff and Núñez (2000). Instead, they present another theory of their own that they correctly dismiss. Our theory is based on neural learning. Rips et al. misrepresent our theory as being directly about real-world experience and mappings directly from that experience.
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Cao, Benchun, Yanchun Liang, Shinichi Yoshida, and Renchu Guan. "Facial Expression Decoding based on fMRI Brain Signal." International Journal of Computers Communications & Control 14, no. 4 (August 5, 2019): 475–88. http://dx.doi.org/10.15837/ijccc.2019.4.3433.
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The analysis of facial expressions is a hot topic in brain-computer interface research. To determine the facial expressions of the subjects under the corresponding stimulation, we analyze the fMRI images acquired by the Magnetic Resonance. There are six kinds of facial expressions: "anger", "disgust", "sadness", "happiness", "joy" and "surprise". We demonstrate that brain decoding is achievable through the parsing of two facial expressions ("anger" and "joy"). Support vector machine and extreme learning machine are selected to classify these expressions based on time series features. Experimental results show that the classification performance of the extreme learning machine algorithm is better than support vector machine. Among the eight participants in the trials, the classification accuracy of three subjects reached 70-80%, and the remaining five subjects also achieved accuracy of 50-60%. Therefore, we can conclude that the brain decoding can be used to help analyzing human facial expressions.
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Miller, Raissa. "Neuroeducation: Integrating Brain-Based Psychoeducation into Clinical Practice." Journal of Mental Health Counseling 38, no. 2 (April 1, 2016): 103–15. http://dx.doi.org/10.17744/mehc.38.2.02.
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Understanding and integrating neuroscience research into clinical practice represents a rapidly growing area in mental health. An expanding body of neuroscience literature increasingly informs clinical practice by validating theory, guiding clinical assessment and conceptualization, directing effective interventions, and facilitating cross-disciplinary communication. Little attention, however, has been given to the use of neuroeducation with clients. In this article, the author provides mental health counselors with a definition of neuroeducation and a rationale for incorporating neuroeducation into clinical practice. The author identifies common neuroeducation topics and offers activity suggestions to illustrate their use in counseling. Finally, the author offers best practices for implementing neuroeducation, including attention to counselor competence, client readiness, and neuroscience of learning principles. Implications for research are also discussed.
Dissertations / Theses on the topic "Brain-based learning theory"
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Laflamme, Denise Marie. "The brain-based theory of learning and multimedia." CSUSB ScholarWorks, 1994. https://scholarworks.lib.csusb.edu/etd-project/1002.
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For this project the brain-based theory of learning, an eclectic theory that incorporates the cognitive and humanistic views was researched. Multimedia, a technology which supports the principles of brain-based learning, was then selected as the vehicle to present historical materials to students.
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Pennington, Eva Patrice. "Brain-based learning theory the incorporation of movement to increase learning /." Lynchburg, Va. : Liberty University, 2010. http://digitalcommons.liberty.edu.
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Tompkins, Abreena Walker. "Brain-based learning theory an online course design model /." Lynchburg, Va. : Liberty University, 2007. http://digitalcommons.liberty.edu.
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Schmitt, Ara J. Swerdlik Mark E. Wodrich David L. "The ability of theory based assessment to discriminate among children with brain impairments." Normal, Ill. Illinois State University, 2001. http://wwwlib.umi.com/cr/ilstu/fullcit?p3064501.
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Thesis (Ph. D.)--Illinois State University, 2001.Title from title page screen, viewed March 14, 2006. Dissertation Committee: Mark E. Swerdlik (chair), David L. Wodrich (co-chair), Valeri Farmer-Dougan, Alvin House. Includes bibliographical references (leaves [106]-117) and abstract. Also available in print.
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Castro, R. Raquel. "From Theory to Practice: A First Look at Success for Life - A Brain Research-Based Early Childhood Program." Thesis, University of North Texas, 1998. https://digital.library.unt.edu/ark:/67531/metadc6153/.
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Success For Life (SFL) is a brain research-based program for children, birth through age six. This research examined the development and implementation of SFL in 13 early childhood settings. Participants were 24 female early childhood teachers and 146 (73 male) children. Teachers included seven infant, four toddler, nine preschool and four kindergarten teachers. Children included infants(n=29), toddlers(n=27), and prek/kindergartners (n=90). A Request for Proposals was disseminated to identify possible implementation sites. After participation was confirmed, teachers attended a full day's training which included a description of brain development/function, the latest brain research, how to implement SFL and other logistics of the study. Program implementation occurred over approximately four months. A field site coordinator visited each site bimonthly to provide on-going technical assistance. This was an intervention project with a pre and post implementation design. Four instruments were used: a teacher questionnaire, a classroom environment measure, a child measure and teacher journals. Results suggested that teachers became more knowledgeable about brain development research and about how children grow and learn. Teachers were better able to make connections between brain research findings and how to apply these findings to their programs and daily activities. Likewise, the environment measure indicated that teachers were better able to arrange environments for learning. They reported that children showed significant increases in skills development and performance in the following areas: physical mastery, social relations/interactions, cognitive development, and language/communications. Additionally, teachers reported improvements in emotional expression and well-being among infants and toddlers. Toddlers and preschoolers showed significant increases in creative/ artistic expression. Finally, teachers indicated that preschoolers showed increases in initiative, use of logic/mathematics skills, and musical coordination and movement. Research findings suggest that Success For Life is able to bridge the gap between theory and practice and benefits children, teachers and programs.
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White, Dan, and res cand@acu edu au. "Pedagogy – The Missing Link in Religious Education: Implications of brain-based learning theory for the development of a pedagogical framework for religious education." Australian Catholic University. School of Religious Education, 2004. http://dlibrary.acu.edu.au/digitaltheses/public/adt-acuvp60.29082005.
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Over the past three decades, the development of religious education in Australia has been largely shaped by catechetical and curriculum approaches to teaching and learning. To date, little emphasis has been placed on the pedagogical dimension of religious education. The purpose of this research project is to explore the manner in which ‘brain-based’ learning theory contributes to pedagogical development in primary religious education. The project utilises an action research methodology combining concept mapping, the application of ‘brain-based’ teaching strategies and focus group dialogue with diocesan Religious Education Coordinators (RECs). The insights derived contribute to the formulation and validation of an appropriate pedagogical model for primary religious education, entitled the ‘DEEP Framework’. The model reflects an integration of insights from brain-based theory with nuances from the contemporary Australian religious education literature. The project identifies four key, interactive principles that are crucial to pedagogical development in religious education, namely: Discernment, Enrichment, Engagement and Participation. It also recognises a fifth principle, ‘an orientation towards wholeness’, as significant in combining the various pedagogical principles into a coherent whole. The DEEP framework enables teachers to more successfully select and evaluate appropriate, interconnecting teaching strategies within the religious education classroom. The framework underpins the pedagogical rationale of the recently developed Archdiocese of Hobart religious education program and forms the basis for the implementation of a coherent professional development program across the Archdiocese.
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Cankaya, Tumer Tugce. "Using Literature To Enhance Language And Cultural Awareness." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12611953/index.pdf.
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Language teachers, including educators from all levels &ndashfrom primary to college level &ndash
and teacher trainers have all agreed with the idea that it is impossible to consider language skills as independent from each other since each of them has a great contribution to the language itself as a whole system. However
it cannot be ignored that all language teachers have also agreed with the idea that reading is a skill that has its own significance, especially in foreign language education. What makes reading so unique is that it can be considered to be the door that is about to be opening to a new world in which the target language is spoken. In that sense, as an essential skill, reading, throughout the centuries, has also had its own keys that have been difficult to be unlocked even by the native speakers of English. This matter of fact brings the question to the light that what guardians are waiting in front of this castle door especially for foreign language students. Although many scholars claim that formal aspect of language, including syntactical and lexical features, stand as great challenge, they ignore the cultural significance of a language. The relationship between language, thought and culture shows that even formal aspects are affected by culture. This fact brings the importance of &lsquo
content schema&rsquo
as well as &lsquo
formal schema&rsquo
in reading to surface. However, now the question is that how it is possible to provide students with sufficient cultural background. As foreign language students are less likely to experience exposure to foreign culture when compared to second language learners, there is an urgent need to create a &lsquo
social context&rsquo
or &lsquo
second hand reality&rsquo
in Kovalik&rsquo
s terms in classroom settings. At this point, literature is believed to give her helping hand to FL students with her wide range of texts carrying a variety of formal aspect of language, but at the same time, a range of cultural components, and thus, constructing &lsquo
schema&rsquo
for them. Apart from this, what literature offers is examined in detail within the framework of brain-based learning principles. This paper tends to contribute to this controversial issue with a case study, which aims to illustrate that the use of literature in FL settings enhance language/cultural awareness. The result of the study showed that 1) literature is a useful source to teach the formal aspects of language such as grammar and vocabulary (language awareness) 2) literature contributes to students&rsquo
cultural awareness which is essential to have language awareness 3) literature is a brain-compatible source with various advantages over the other written materials. In accordance with what is mentioned above, this study has a direct aim to show how ELT and Literature can intrude the ancient walls of this castle when work interdisciplinary.
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Tsividis, Pedro A. "Theory-based learning in humans and machines." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121813.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2019Cataloged from PDF version of thesis.
Includes bibliographical references (pages 123-130).
Humans are remarkable in their ability to rapidly learn complex tasks from little experience. Recent successes in Al have produced algorithms that can perform complex tasks well in environments whose simple dynamics are known in advance, as well as models that can learn to perform expertly in unknown environments after a great amount of experience. Despite this, no current AI models are able to learn sufficiently rich and general representations so as to support rapid, human-level learning on new, complex, tasks. This thesis examines some of the epistemic practices, representations, and algorithms that we believe underlie humans' ability to quickly learn about their world and to deploy that understanding to achieve their aims. In particular, the thesis examines humans' ability to effectively query their environment for information that helps distinguish between competing hypotheses (Chapter 2); children's ability to use higher-level amodal features of data to match causes and effects (Chapter 3); and adult human rapid-learning abilities in artificial video-game environments (Chapter 4). The thesis culminates by presenting and testing a model, inspired by human inductive biases and epistemic practices, that learns to perform complex video-game tasks at human levels with human-level amounts of experience (Chapter 5). The model is an instantiation of a more general approach, Theory-Based Reinforcement Learning, which we believe can underlie the development of human-level agents that may eventually learn and act adaptively in the real world.
by Pedro A. Tsividis.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences
Books on the topic "Brain-based learning theory"
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Darling-Kuria, Nikki. Brain-based early learning activities: Connecting theory and practice. St. Paul, MN: Redleaf Press, 2010.
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Brain-based early learning activities: Connecting theory and practice. St. Paul, MN: Redleaf Press, 2010.
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Anderson, James A. Brain Theory. Oxford University Press, 2018. http://dx.doi.org/10.1093/acprof:oso/9780199357789.003.0012.
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What form would a brain theory take? Would it be short and punchy, like Maxwell’s Equations? Or with a clear goal but achieved by a community of mechanisms—local theories—to attain that goal, like the US Tax Code. The best developed recent brain-like model is the “neural network.” In the late 1950s Rosenblatt’s Perceptron and many variants proposed a brain-inspired associative network. Problems with the first generation of neural networks—limited capacity, opaque learning, and inaccuracy—have been largely overcome. In 2016, a program from Google, AlphaGo, based on a neural net using deep learning, defeated the world’s best Go player. The climax of this chapter is a fictional example starring Sherlock Holmes demonstrating that complex associative computation in practice has less in common with accurate pattern recognition and more with abstract high-level conceptual inference.
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Rainville, Pierre. A signature of pain in the brain. Edited by Paul Farquhar-Smith, Pierre Beaulieu, and Sian Jagger. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198834359.003.0029.
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The discovery of pain biomarkers has profound implications for both pharmacology and neurobiology; in 2013, in the landmark paper discussed in this chapter, Wager et al. presented a neurologic signature of pain based on human brain imaging performed in healthy individuals administered experimental heat-pain stimuli. Using advanced analytic methods based on machine learning and multivariate pattern analysis, Wagner et al. provide very convincing support for the idea that pain is encoded in a distinctive pattern of brain activity in one or several brain areas typically referred to as the ‘pain matrix’, which acts as a saliency detection system for the body. Although the usage of such tool to infer pain in patients poses major challenges and is clearly not indicated in medico-legal contexts, the study provides experimental proof of concept in favour of a pattern theory of pain as well as for a specificity theory of pain.
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Browning, Birch P. How Students Learn. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199928200.003.0006.
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The chapter relates that learning, whether of new knowledge, a new skill, or a new attitude, has both neurological and psychological components. The learning process modifies the structure of the brain (via synaptogenesis) and is experienced by the learner as a representation. Representations enable us to recognize and then cogitate about items, experiences, and concepts . Learning is more efficient if based on prior learning, stored in patterns, and is more likely to occur if the learner finds the material meaningful or useful. The chapter describes the ways in which changing a known routine (adaptive expertise) and being aware of one’s own thought process (metacognition) effect learning. Experts are able to solve complex problems because they have developed interlocking pattern-based representations of the core understandings of their domain.
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Rauch, Sheila A. M., and Israel Liberzon. Mechanisms of Action in Psychotherapy. Edited by Israel Liberzon and Kerry J. Ressler. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190215422.003.0019.
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Therapy at its core is based on learning, and learning at its core is biological. Experience that is not in some way encoded in the brain and/or body is lost. This chapter provides a discussion of mechanisms of therapy research in PTSD in which the goal is to understand how PTSD therapy works. First, the chapter reviews what a mechanism is and how therapeutic mechanisms are examined. It then discusses the importance of therapeutic mechanisms research within the broader realm of mental health research. It focuses on prolonged exposure (PE) therapy for PTSD as an example of application of mechanisms research methodology and begins with the presentation of a theoretical model that builds on previous theory and mechanisms research to date. While much of this model is theoretical, the goal is to show how mechanisms research may apply to clinical practice to improve precision, efficiency, and efficacy.
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Vieira, Kate. Writing for Love and Money. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190877316.001.0001.
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This book tells the story of how families separated across borders write—and learn new ways of writing—in pursuit of both love and money. Over the past decades, global economic inequality has continued to promote the growth of labor migration. According to the UN, 244 million people currently live outside the countries of their birth. The human drama behind these numbers is that labor migration often separates parents from children, brothers from sisters, lovers from each other. Migration, undertaken in response to problems of the pocketbook, also poses problems for the heart. Based on field research and interviews with transnational families in Latin America (Brazil), Eastern Europe (Latvia), and North America (United States), Writing for Love and Money: How Migration Drives Literacy Learning in Transnational Families shows how families separated across borders turn to writing to address these problems. They are writing to sustain meaningful relationships across distance and to better their often impoverished circumstances. The book reveals that, despite policymakers’ concerns about brain drain, immigrants’ departures do not leave their homelands wholly educationally hobbled. Instead migration promotes experiences of literacy learning in transnational families as they write to reach the two life goals that globalization consistently threatens: economic solvency and familial intimacy. The book thus shows how migration itself can be a source of technologically savvy, emotionally attuned, globally conscious, and entrepreneurial literacy learning.
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Rosenberg, Paul B. Treatment of Cognitive Impairment. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199959549.003.0007.
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There are lifestyle strategies that may help preserve cognition in old age and in MCI. While the evidence is still suggestive rather than definitive it is strong enough to make suggestions to patients and families. Cognitive interventions such as computer-based cognitive stimulation and brain fitness programs may be helpful, although more generalized cognitive activities such as taking a college course or learning a new skill may be equally helpful. Aerobic exercise has the best track record to date among lifestyle interventions. Having a variety of leisure activities that combine psychological, physical, and social activities is advised. As far as well can tell, diets that are helpful for preventing heart disease such as the Mediterranean diet also may be good for cognition. The mechanisms for many of these strategies likely involve 1) the brain compensating for circuit loss by engaging new circuits to solve problems and 2) improvements in vascular health.
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Leibo, Joel Z., and Tomaso Poggio. Perception. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0025.
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This chapter provides an overview of biological perceptual systems and their underlying computational principles focusing on the sensory sheets of the retina and cochlea and exploring how complex feature detection emerges by combining simple feature detectors in a hierarchical fashion. We also explore how the microcircuits of the neocortex implement such schemes pointing out similarities to progress in the field of machine vision driven deep learning algorithms. We see signs that engineered systems are catching up with the brain. For example, vision-based pedestrian detection systems are now accurate enough to be installed as safety devices in (for now) human-driven vehicles and the speech recognition systems embedded in smartphones have become increasingly impressive. While not being entirely biologically based, we note that computational neuroscience, as described in this chapter, makes up a considerable portion of such systems’ intellectual pedigree.
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Butz, Martin V., and Esther F. Kutter. How the Mind Comes into Being. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.001.0001.
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For more than 2000 years Greek philosophers have thought about the puzzling introspectively assessed dichotomy between our physical bodies and our seemingly non-physical minds. How is it that we can think highly abstract thoughts, seemingly fully detached from actual, physical reality? Despite the obvious interactions between mind and body (we get tired, we are hungry, we stay up late despite being tired, etc.), until today it remains puzzling how our mind controls our body, and vice versa, how our body shapes our mind. Despite a big movement towards embodied cognitive science over the last 20 years or so, introductory books with a functional and computational perspective on how human thought and language capabilities may actually have come about – and are coming about over and over again – are missing. This book fills that gap. Starting with a historical background on traditional cognitive science and resulting fundamental challenges that have not been resolved, embodied cognitive science is introduced and its implications for how human minds have come and continue to come into being are detailed. In particular, the book shows that evolution has produced biological bodies that provide “morphologically intelligent” structures, which foster the development of suitable behavioral and cognitive capabilities. While these capabilities can be modified and optimized given positive and negative reward as feedback, to reach abstract cognitive capabilities, evolution has furthermore produced particular anticipatory control-oriented mechanisms, which cause the development of particular types of predictive encodings, modularizations, and abstractions. Coupled with an embodied motivational system, versatile, goal-directed, self-motivated behavior, learning becomes possible. These lines of thought are introduced and detailed from interdisciplinary, evolutionary, ontogenetic, reinforcement learning, and anticipatory predictive encoding perspectives in the first part of the book. A short excursus then provides an introduction to neuroscience, including general knowledge about brain anatomy, and basic neural and brain functionality, as well as the main research methodologies. With reference to this knowledge, the subsequent chapters then focus on how the human brain manages to develop abstract thought and language. Sensory systems, motor systems, and their predictive, control-oriented interactions are detailed from a functional and computational perspective. Bayesian information processing is introduced along these lines as are generative models. Moreover, it is shown how particular modularizations can develop. When control and attention come into play, these structures develop also dependent on the available motor capabilities. Vice versa, the development of more versatile motor capabilities depends on structural development. Event-oriented abstractions enable conceptualizations and behavioral compositions, paving the path towards abstract thought and language. Also evolutionary drives towards social interactions play a crucial role. Based on the developing sensorimotor- and socially-grounded structures, the human mind becomes language ready. The development of language in each human child then further facilitates the self-motivated generation of abstract, compositional, highly flexible thought about the present, past, and future, as well as about others. In conclusion, the book gives an overview over how the human mind comes into being – sketching out a developmental pathway towards the mastery of abstract and reflective thought, while detailing the critical body and neural functionalities, and computational mechanisms, which enable this development.
Book chapters on the topic "Brain-based learning theory"
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Zhang, Yuduo, Zhichao Lian, and Chanying Huang. "A Multilayer Sparse Representation of Dynamic Brain Functional Network Based on Hypergraph Theory for ADHD Classification." In Intelligence Science and Big Data Engineering. Big Data and Machine Learning, 325–34. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36204-1_27.
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Alidoust, Mohammadreza. "AGI Brain: A Learning and Decision Making Framework for Artificial General Intelligence Systems Based on Modern Control Theory." In Artificial General Intelligence, 1–10. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27005-6_1.
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Behuet, Sabrina, Sebastian Bludau, Olga Kedo, Christian Schiffer, Timo Dickscheid, Andrea Brandstetter, Philippe Massicotte, Mona Omidyeganeh, Alan Evans, and Katrin Amunts. "A High-Resolution Model of the Human Entorhinal Cortex in the ‘BigBrain’ – Use Case for Machine Learning and 3D Analyses." In Lecture Notes in Computer Science, 3–21. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_1.
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AbstractThe ‘BigBrain’ is a high-resolution data set of the human brain that enables three-dimensional (3D) analyses with a 20 µm spatial resolution at nearly cellular level. We use this data set to explore pre-α (cell) islands of layer 2 in the entorhinal cortex (EC), which are early affected in Alzheimer’s disease and have therefore been the focus of research for many years. They appear mostly in a round and elongated shape as shown in microscopic studies. Some studies suggested that islands may be interconnected based on analyses of their shape and size in two-dimensional (2D) space. Here, we characterized morphological features (shape, size, and distribution) of pre-α islands in the ‘BigBrain’, based on 3D-reconstructions of gapless series of cell-body-stained sections. The EC was annotated manually, and a machine-learning tool was trained to identify and segment islands with subsequent visualization using high-performance computing (HPC). Islands were visualized as 3D surfaces and their geometry was analyzed. Their morphology was complex: they appeared to be composed of interconnected islands of different types found in 2D histological sections of EC, with various shapes in 3D. Differences in the rostral-to-caudal part of EC were identified by specific distribution and size of islands, with implications for connectivity and function of the EC. 3D compactness analysis found more round and complex islands than elongated ones. The present study represents a use case for studying large microscopic data sets. It provides reference data for studies, e.g. investigating neurodegenerative diseases, where specific alterations in layer 2 were previously reported.
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Brandstetter, Andrea, Najoua Bolakhrif, Christian Schiffer, Timo Dickscheid, Hartmut Mohlberg, and Katrin Amunts. "Deep Learning-Supported Cytoarchitectonic Mapping of the Human Lateral Geniculate Body in the BigBrain." In Lecture Notes in Computer Science, 22–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_2.
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AbstractThe human lateral geniculate body (LGB) with its six sickle shaped layers (lam) represents the principal thalamic relay nucleus for the visual system. Cytoarchitectonic analysis serves as the groundtruth for multimodal approaches and studies exploring its function. This technique, however, requires experienced knowledge about human neuroanatomy and is costly in terms of time. Here we mapped the six layers of the LGB manually in serial, histological sections of the BigBrain, a high-resolution model of the human brain, whereby their extent was manually labeled in every 30th section in both hemispheres. These maps were then used to train a deep learning algorithm in order to predict the borders on sections in-between these sections. These delineations needed to be performed in 1 µm scans of the tissue sections, for which no exact cross-section alignment is available. Due to the size and number of analyzed sections, this requires to employ high-performance computing. Based on the serial section delineations, high-resolution 3D reconstruction was performed at 20 µm isotropic resolution of the BigBrain model. The 3D reconstruction shows the shape of the human LGB and its sublayers for the first time at cellular precision. It represents a use case to study other complex structures, to visualize their shape and relationship to neighboring structures. Finally, our results could provide reference data of the LGB for modeling and simulation to investigate the dynamics of signal transduction in the visual system.
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Lee, Daeyeol. "Brain for Learning." In Birth of Intelligence, 127–54. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190908324.003.0007.
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Long-lasting effects of brief sensory experience must be mediated by long-term changes in the strength of connections between neurons in the brain. This phenomenon is known as synaptic plasticity, and the physical location of such change is referred to as the engram. This chapter illustrates how multiple learning and memory systems might be implemented in different anatomical modules of the brain and what role dopamine plays in learning. Most of these neurobiological and behavioral observations can be accounted for by reinforcement learning theory. The goal of reinforcement is to understand how utilities must be altered by experience so that rational choices based on the utility functions can result in the most desirable outcomes through learning.
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"Deep Learning Theory and Software." In Advances in Computer and Electrical Engineering, 23–61. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1554-9.ch002.
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In the past decade, deep learning has achieved a significant breakthrough in development. In addition to the emergence of convolution, the most important is self-learning of deep neural networks. By self-learning methods, adaptive weights of kernels and built-in parameters or interconnections are automatically modified such that the error rate is reduced along the learning process, and the recognition rate is improved. Emulating mechanism of the brain, it can have accurate recognition ability after learning. One of the most important self-learning methods is back-propagation (BP). The current BP method is indeed a systematic way of calculating the gradient of the loss with respect to adaptive interconnections. The main core of the gradient descent method addresses on modifying the weights negatively proportional to the determined gradient of the loss function, subsequently reducing the error of the network response in comparison with the standard answer. The basic assumption for this type of the gradient-based self-learning is that the loss function is the first-order differential.
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Bastedo, Kathleen, and Jessica Vargas. "Universal Design for Learning in Today’s Diverse Educational Environments." In Assistive Technology Research, Practice, and Theory, 1–10. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5015-2.ch001.
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Learning can be difficult for a myriad of reasons and not just for those with disabilities and for those dedicated to teaching in its many forms. It can be next to impossible to accommodate the variety of students encountered in today’s diverse learning environments. This is where the principle of Universal Design for Learning (UDL) can be successfully applied. This chapter explores the strides made in creating content that brain-based research supports as a way for not only motivating students to learn, but also for allowing those with disabilities a way to learn that meets their specific needs. Although there is no one surefire way to design learning that teaches everyone, UDL is a stepping-stone to that pursuit. If implemented to its fullest potential, it can be a panacea to reducing many barriers to access and learning.
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Aliustaoğlu, Feyza, and Abdulkadir Tuna. "Brain-Based Learning." In Handbook of Research on Innovations in Non-Traditional Educational Practices, 361–78. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4360-3.ch019.
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Many models that take into account the learning styles have been developed with the formation of modern educational approaches. 4MAT model is a natural learning process moving sequentially through the learning cycle. According to the 4MAT model there are four learning styles and each student can learn more comfortable with their own learning styles. In consideration of neurological studies, also, the dominant hemisphere used by individuals in the information processing process is important in the 4MAT model. This chapter presents a lesson plan based on the 4MAT model as well as the results regarding the application of this lesson plan in a middle school located in the northern part of Turkey. The lesson plan was prepared by examining the books titled “4MAT 4 algebra: The system of mathematics” and “4 MAT 4 geometry teacher book” and receiving expert opinions.
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Pitoglou, Stavros. "Machine Learning in Healthcare." In Quality Assurance in the Era of Individualized Medicine, 92–109. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2390-2.ch004.
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Machine learning, closely related to artificial intelligence and standing at the intersection of computer science and mathematical statistical theory, comes in handy when the truth is hiding in a place that the human brain has no access to. Given any prediction or assessment problem, the more complicated this issue is, based on the difficulty of the human mind to understand the inherent causalities/patterns and apply conventional methods towards an acceptable solution, machine learning can find a fertile field of application. This chapter's purpose is to give a general non-technical definition of machine learning, provide a review of its latest implementations in the healthcare domain and add to the ongoing discussion on this subject. It suggests the active involvement of entities beyond the already active academic community in the quest for solutions that “exploit” existing datasets and can be applied in the daily practice, embedded inside the software processes that are already in use.
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Curry, Marjorie. "Culturally Responsive Math." In Theory and Practice: An Interface or A Great Divide?, 115–17. WTM-Verlag Münster, 2019. http://dx.doi.org/10.37626/ga9783959871129.0.23.
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Using the Ready for Rigor framework, Zaretta Hammond’s book Culturally Responsive Teaching and the Brain: Promoting Authentic Engagement and Rigor Among Culturally and Linguistically Diverse Students gives educators a neuroscience-based approach to closing the achievement gap. The Ready for Rigor framework consists of four strands: awareness, learning partnerships, information processing, and community building. Acknowledging that all four strands are paramount to culturally responsive teaching but restricting focus to information processing, this session will give participants examples of and strategies for making their mathematics lessons more culturally responsive. More specifically, participants will learn to game-ify it, story-ify it, and make it social.
Conference papers on the topic "Brain-based learning theory"
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Rusák, Zoltán, Niels van de Water, Bram de Smit, Imre Horváth, and Wilhelm Frederik Van Der Vegte. "Smart Reading Aid for Detecting Problems With Reading Fluency and Comprehension." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59130.
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Brain signal and eye tracking technology have been intensively applied in cognitive science in order to study reading, listening and learning processes. Though promising results have been found in laboratory experiments, there are no smart reading aids that are capable to estimate difficulty during normal reading. This paper presents a new concept that aims to tackle this challenge. Based on a literature study and an experiment, we have identified several indicators for characterizing word processing difficulty by interpreting electroencelography (EEG) and electrooculography (EOG) signals. We have defined a computational model based on fuzzy set theory, which estimates the probability of word processing and comprehension difficulty during normal reading. The paper also presents a concept and functional prototype of a smart reading aid, which is used to demonstrate the feasibility of our solution. The results of our research proves that it is possible to implement a smart reading aid that is capable to detect reading difficulty in real time. We show that the most reliable indicators are related to eye movement (i.e. fixation and regression), while brain signals are less dependable sources for indicating word processing difficulty during continuous reading.
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Nayef, B. H., S. Sahran, R. I. Hussain, and S. N. H. S. Abdullah. "Brain imaging classification based On Learning Vector Quantization." In 2013 1st International Conference on Communications, Signal Processing, and Their Applications (ICCSPA). IEEE, 2013. http://dx.doi.org/10.1109/iccspa.2013.6487253.
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Matsuno, Kevin, and Vidya K. Nandikolla. "Machine Learning Using Brain Computer Interface System." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23394.
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Abstract With commercially available hardware and supporting software, different electrical potential brain waves are measured via a headset with a collection of electrodes. Out of the different types of brain signals, the proposed brain-computer interface (BCI) controller utilizes non-task related signals, i.e. squeezing left/right hand or tapping left/right foot, due to their responsive behavior and general signal feature similarity among patients. In addition, motor imagery related signals, such as imagining left/right foot or hand movement are also examined. The main goal of the paper is to demonstrate the performance of machine learning algorithms based on classification accuracy. The performances are evaluated on BCI dataset of three male subjects to extract the most significant features. Each subject undergoes a 30-minute session composed of four experiments: two non-task related signals and two motor imagery signals. Each experiment records fifteen trials of two classes (i.e. left/right hand movement). The raw data is then pre-processed using a MatLab plugin, EEGLAB, where standard processes of cleaning and epoching the signals is performed. The paper discusses machine learning for robotic application and the common flaws when validating machine learning methods in the context of BCI to provide a brief overview on biologically (using brain waves) controlled devices.
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Vilas-Boas, Vitor Mendes, Vitor Da Silva Jorge, and Cleison Daniel Silva. "Towards ideal time window for classifying motor imagery in brain-computer interfaces." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/kdmile.2020.11961.
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Brain-Computer Interfaces (ICM) allow the control of devices by modulating brain activity. Commonly, when based on motor imagery (IM) these systems use the energy (de)synchronization in the electroencephalogram signal (EEG), voluntarily caused by the individual, to identify and classify their motor intention. Therefore, the EEG segment used in the training of the learning algorithms plays a fundamental role in the description of the characteristics and, consequently, in the recognition of patterns in the signal. In this context, the objective of this work is to demonstrate the correlation between the temporal properties of the input EEG segment and the classification performance of a ICM-IM system. An auxiliary sliding window was used in order to obtain the variation of performance in function of the variation in the time and to support the decision making about the appropriate window. Simulations based on public EEG data point to significant variability in the location and width of the ideal window and suggest the need for individualized selection according to the cognitive patterns of each subject.
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Malhotra, Radhika, Jasleen Saini, Barjinder Singh Saini, and Savita Gupta. "Improving Brain Tumor Segmentation with Data Augmentation Strategies." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.2.
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In the past decade, there has been a remarkable evolution of convolutional neural networks (CNN) for biomedical image processing. These improvements are inculcated in the basic deep learning-based models for computer-aided detection and prognosis of various ailments. But implementation of these CNN based networks is highly dependent on large data in case of supervised learning processes. This is needed to tackle overfitting issues which is a major concern in supervised techniques. Overfitting refers to the phenomenon when a network starts learning specific patterns of the input such that it fits well on the training data but leads to poor generalization abilities on unseen data. The accessibility of enormous quantity of data limits the field of medical domain research. This paper focuses on utility of data augmentation (DA) techniques, which is a well-recognized solution to the problem of limited data. The experiments were performed on the Brain Tumor Segmentation (BraTS) dataset which is available online. The results signify that different DA approaches have upgraded the accuracies for segmenting brain tumor boundaries using CNN based model.
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Gomez, Connie, and Sheema Nasir. "Problem Based Learning: Generating a 3D Educational Brain Model to Engage Undergraduate Engineering Honors Students." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-87197.
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Engineering courses offered through the Honors Program allow students to conduct both research and design during their time at a community college, which is extremely valuable due to the limited number of research opportunities when compared to a four-year institution. Additionally, community college engineering courses normally serve students seeking to enter a wide range of engineering disciplines. Therefore, any research or design experience with lasting impact must also encompass a wide range of topics while also fostering communication, teamwork, creativity and life-long learning. This paper describes an engineering graphics honors course that engaged students in the development of a CAD model and prototype of a 3D brain model for use by Anatomy and Physiology students. This project allowed students to engage in the areas of personalized learning, reverse engineering the brain, manufacturing as well a computer-aided design. This paper discusses the development of technical and soft skill competencies through student performance and student perception via questionnaires. Finally, this paper sets forth recommendations for other community colleges interested in developing problem-based learning opportunities throughout their engineering curriculum.
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Dinparastdjadid, Azadeh, and Ehsan T. Esfahani. "Spike Sorting via Multi Cluster Feature Selection." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60456.
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Having the ability to study the activity of single neurons will facilitate studies in many areas including cognitive sciences and brain computer interface applications. Due to the fact that every neuron has it’s own unique spike waveform, by applying spike-sorting methods, one can separate neurons based on their associated spike. Spike sorting is an unsupervised learning problem in the realm of data mining and machine learning. In this study, a new method that will improve the accuracy of spike sorting in comparison to existing methods has been introduced. This method, which is named Multi Cluster Feature Selection (MCFS), will designate a reduced number of features from the original data set that will best differentiate the existing clusters through solving a Lasso optimization problem. MCFS, was also applied to data obtained from multi-channel recordings on a rat’s brain. With MCFS, each channel was studied and neurons in each channel were sorted with an improved rate in comparison to conventional methods such as PCA.
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Li, Chao, Baolin Liu, and Jianguo Wei. "Visual Encoding and Decoding of the Human Brain Based on Shared Features." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/103.
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Using a convolutional neural network to build visual encoding and decoding models of the human brain is a good starting point for the study on relationship between deep learning and human visual cognitive mechanism. However, related studies have not fully considered their differences. In this paper, we assume that only a portion of neural network features is directly related to human brain signals, which we call shared features. In the encoding process, we extract shared features from the lower and higher layers of the neural network, and then build a non-negative sparse map to predict brain activities. In the decoding process, we use back-propagation to reconstruct visual stimuli, and use dictionary learning and a deep image prior to improve the robustness and accuracy of the algorithm. Experiments on a public fMRI dataset confirm the rationality of the encoding models, and comparing with a recently proposed method, our reconstruction results obtain significantly higher accuracy.
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Xu, Mingkun, Yujie Wu, Lei Deng, Faqiang Liu, Guoqi Li, and Jing Pei. "Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/441.
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Biological spiking neurons with intrinsic dynamics underlie the powerful representation and learning capabilities of the brain for processing multimodal information in complex environments. Despite recent tremendous progress in spiking neural networks (SNNs) for handling Euclidean-space tasks, it still remains challenging to exploit SNNs in processing non-Euclidean-space data represented by graph data, mainly due to the lack of effective modeling framework and useful training techniques. Here we present a general spike-based modeling framework that enables the direct training of SNNs for graph learning. Through spatial-temporal unfolding for spiking data flows of node features, we incorporate graph convolution filters into spiking dynamics and formalize a synergistic learning paradigm. Considering the unique features of spike representation and spiking dynamics, we propose a spatial-temporal feature normalization (STFN) technique suitable for SNN to accelerate convergence. We instantiate our methods into two spiking graph models, including graph convolution SNNs and graph attention SNNs, and validate their performance on three node-classification benchmarks, including Cora, Citeseer, and Pubmed. Our model can achieve comparable performance with the state-of-the-art graph neural network (GNN) models with much lower computation costs, demonstrating great benefits for the execution on neuromorphic hardware and prompting neuromorphic applications in graphical scenarios.
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Enikov, Eniko T., Juan-Antonio Escareno, and Micky Rakotondrabe. "Image Schema Based Landing and Navigation for Rotorcraft MAV-s." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51450.
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To date, most autonomous micro air vehicles (MAV-s) operate in a controlled environment, where the location of and attitude of the aircraft are measured with an infrared (IR) tracking systems. If MAV-s are to ever exit the lab, their flight control needs to become autonomous and based on on-board image and attitude sensors. To address this need, several groups are developing monocular and binocular image based navigation systems. One of the challenges of these systems is the need for exact calibration in order to determine the vehicle’s position and attitude through the solution of an inverse problem. Body schemas are a biologically-inspired approach, emulating the plasticity of the animal brain, which allows it to learn non-linear mappings between the body configurations, i.e. its generalized coordinates and the resulting sensory outputs. The advantages of body schemas has long been recognized in the cognitive robotic literature and resulting studies on human-robot interactions based on artificial neural networks, however little effort has been made so far to develop avian-inspired flight control strategies utilizing body and image schemas. This paper presents a numerical experiment of controlling the trajectory of a miniature rotorcraft during landing maneuvers suing the notion of body and image schemas. More specifically, we demonstrate how trajectory planning can be executed in the image space using gradient-based maximum seeking algorithm of a pseudo-potential. It is demonstrated that a neural-gas type artificial neural network (ANN), trained through Hebbian-type learning algorithm, can be effective in learning a mapping between the rotorcraft’s position/attitude and the output of its vision sensors. Numerical simulation of the landing performance, including resulting landing errors are presented using an experimentally validated rotorcraft model.