Neuroscience Learning Research Articles

NEUROCIÊNCIA E APRENDIZAGEM DE LÍNGUAS ADICIONAIS: UM DIÁLOGO NECESSÁRIO NO CONTEXTO DA EDUCAÇÃO ATUAL

NEUROSCIENCE AND ADDITIONAL LANGUAGE LEARNING: A NECESSARY DIALOGUE IN THE CONTEXT OF CURRENT EDUCATION ABSTRACT Neurolinguistics is a branch of neuroscience that studies the neurophysiological mechanisms responsible for language acquisition and use. So, in this area of ​​knowledge, the individual must be able to automatically correlate the dialectic. The incorporation of neuroscience in education brought a significant transformation to the teaching process. This innovative approach allows educators to create more comprehensive courses that meet the individual needs of each student. However, how can neuroscience and additional language acquisition facilitate the discourse needed in today's educational environment? Learning is a cognitive process that originates in the brain as a response to external stimuli. The synapses created in the brain form circuits capable of processing information and storing it at a molecular level. The thesis that learning is intrinsically linked to brain development is reinforced by research carried out on these technologies. Therefore, the objective of this article is to show the importance of the educational organization through movements and activities that it has already been using in the process of knowledge where it can be proven through multidisciplinary courses. The connection between neuroscience and learning confirmed the main ideas of education theorists. These include the influence of emotions on remembering information, the importance of motivation and focus in learning, and the brain's ability to change depending on experiences and exposure to the environment. There is still a lack of evidence from neuroscience to prove multisensory learning models, such as those that work with visual, auditory and kinesthetic stimulation. They operate on the premise that information gained through one sensory modality is processed in the brain without reference to content learned from another species. Keyword: Neuroscience and Learning. Sensory Modality. Additional Languages. Communication Process

Neuroscience-based Learning Model in Early Childhood Education

Preschool teachers expect to enhance students' intellectual abilities by implementing brain-based learning stimulation in children. However, numerous preschool teachers remain uninformed about the brain's functioning in children. This study aims to establish a neuroscience-based learning model inside early childhood education institution. The study is applied to the 4D development paradigm, which comprises four stages: defining, designing, developing, and disseminating. The study involved 30 kindergarten/RA instructors in Parepare City, selected by purposive sampling, ensuring that the participants had not previously received training related to neuroscience. The data collection procedures employed included observation, interviews, needs analysis, questionnaires, and both pretests and posttests. Qualitative data analysis techniques are descriptive methodologies, whereas quantitative data analysis encompasses expert validation, teacher response surveys, and effectiveness assessments. This project develops a neuroscience-based learning model presented in a guide package as a validated teaching module. After providing content derived from the developed modules, the results indicated an enhancement in participants' scores. The neuroscience learning approach effectively enhances the knowledge of Islamic kindergarten educators. The findings of this study can serve as suggestions for educators and policymakers to improve and elevate the quality of learning in early childhood education.

Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience.

The study of complex behaviors is often challenging when using manual annotation due to the absence of quantifiable behavioral definitions and the subjective nature of behavioral annotation. Integration of supervised machine learning approaches mitigates some of these issues through the inclusion of accessible and explainable model interpretation. To decrease barriers to access, and with an emphasis on accessible model explainability, we developed the open-source Simple Behavioral Analysis (SimBA) platform for behavioral neuroscientists. SimBA introduces several machine learning interpretability tools, including SHapley Additive exPlanation (SHAP) scores, that aid in creating explainable and transparent behavioral classifiers. Here we show how the addition of explainability metrics allows for quantifiable comparisons of aggressive social behavior across research groups and species, reconceptualizing behavior as a sharable reagent and providing an open-source framework. We provide an open-source, graphical user interface (GUI)-driven, well-documented package to facilitate the movement toward improved automation and sharing of behavioral classification tools across laboratories.

Game-Based Learning in Neuroscience

Game-Based Learning in Neuroscience

Decoding the application of deep learning in neuroscience: a bibliometric analysis.

The application of deep learning in neuroscience holds unprecedented potential for unraveling the complex dynamics of the brain. Our bibliometric analysis, spanning from 2012 to 2023, delves into the integration of deep learning in neuroscience, shedding light on the evolutionary trends and identifying pivotal research hotspots. Through the examination of 421 articles, this study unveils a significant growth in interdisciplinary research, marked by the burgeoning application of deep learning techniques in understanding neural mechanisms and addressing neurological disorders. Central to our findings is the critical role of classification algorithms, models, and neural networks in advancing neuroscience, highlighting their efficacy in interpreting complex neural data, simulating brain functions, and translating theoretical insights into practical diagnostics and therapeutic interventions. Additionally, our analysis delineates a thematic evolution, showcasing a shift from foundational methodologies toward more specialized and nuanced approaches, particularly in areas like EEG analysis and convolutional neural networks. This evolution reflects the field's maturation and its adaptation to technological advancements. The study further emphasizes the importance of interdisciplinary collaborations and the adoption of cutting-edge technologies to foster innovation in decoding the cerebral code. The current study provides a strategic roadmap for future explorations, urging the scientific community toward areas ripe for breakthrough discoveries and practical applications. This analysis not only charts the past and present landscape of deep learning in neuroscience but also illuminates pathways for future research, underscoring the transformative impact of deep learning on our understanding of the brain.

Game-Based Learning in Neuroscience: Key Terminology, Literature Survey, and How To Guide to Create a Serious Game.

Game-based learning (GBL) has emerged as a promising approach to engage students and promote deep learning in a variety of educational settings. Neurology and neuroscience are complex fields that require an understanding of intricate neural structures and their functional roles. GBL can support the acquisition and application of such knowledge. In this article, we give an overview of the current state of GBL in neuroscience education. First, we review the language of gaming, establishing conceptual definitions for game elements, gamification, serious games, and GBL. Second, we discuss a literature review of games in the educational literature for adult learners involved in neuroscience. Third, we review available games intended for neuroscience education. Finally, we share tips for educators interested in developing their own educational games. By leveraging the unique features of games, including interactivity, feedback, and immersive experiences, educators and learners can engage with complex neuroscience concepts in a fun, engaging, and effective way.

Machine learning and artificial intelligence in neuroscience: A primer for researchers

Artificial intelligence (AI) is often used to describe the automation of complex tasks that we would attribute intelligence to. Machine learning (ML) is commonly understood as a set of methods used to develop an AI. Both have seen a recent boom in usage, both in scientific and commercial fields. For the scientific community, ML can solve bottle necks created by complex, multi-dimensional data generated, for example, by functional brain imaging or *omics approaches. ML can here identify patterns that could not have been found using traditional statistic approaches. However, ML comes with serious limitations that need to be kept in mind: their tendency to optimise solutions for the input data means it is of crucial importance to externally validate any findings before considering them more than a hypothesis. Their black-box nature implies that their decisions usually cannot be understood, which renders their use in medical decision making problematic and can lead to ethical issues. Here, we present an introduction for the curious to the field of ML/AI. We explain the principles as commonly used methods as well as recent methodological advancements before we discuss risks and what we see as future directions of the field. Finally, we show practical examples of neuroscience to illustrate the use and limitations of ML.

Applications of machine learning in neuroscience and inspiration of reinforcement learning for computational neuroscience

High-performance machine learning algorithms have always been one of the concerns of many researchers. Since its birth, machine learning has been a product of multidisciplinary integration. Especially in the field of neuroscience, models from related fields continue to inspire the development of neural networks and deepen people's understanding of neural networks. The mathematical and quantitative modeling approach to research brought about by machine learning is also feeding into the development of neuroscience. One of the emerging products of this is computational neuroscience. Computational neuroscience has been pushing the boundaries of models of brain function in recent years, and just as early studies of visual hierarchy influenced neural networks, computational neuroscience has great potential to lead to higher performance machine learning algorithms, particularly in the development of deep learning algorithms with strong links to neuroscience. In this paper, it first reviews the help and achievements of machine learning for neuroscience in recent years specially in fMRI image recognition and look at the possibilities for the future development of neural networks due to the recent development of the computational neuroscience in psychiatry of the temporal difference model for dopamine and serotonin.

Enhancing medical student neuroscience training with a team-based learning curriculum (P4-2.005)

<h3>Objective:</h3> To assess the effects of a transition to clinician-educators and active learning in a pre-clinical neuropathology curriculum. <h3>Background:</h3> Neurophobia, the fear of and dislike of clinical neurology, is frequently due to poor experiences in pre-clinical neuroscience education. Methods to mitigate neurophobia include increasing active learning, engaging clinicians, and demonstrating clinical relevance. <h3>Design/Methods:</h3> We identified an under-performing neuropathology curriculum within a second-year medical student neuroscience course and implemented a traditional team-based learning curriculum to deliver this content. In addition, we transitioned to primarily clinician-led lectures. We assessed student opinions of the curricular changes through end-of-course feedback, a novel survey, and semi-structured interviews with students. We assessed outcomes on course examinations and overall course performance, comparing performance in the pre-implementation phase (2020–2021) to the post-implementation phase (2021–2022) using a two-sample t-test. <h3>Results:</h3> Student opinions of the curricular changes were positive on the end-of-course evaluation (79.4% rated team-based learning as good or excellent) and novel survey (89–96% of students rated the portions of the curriculum positively). Themes identified in qualitative analysis included an appreciation of streamlined course content and a sense that the sessions within the curriculum reinforced learning. Student performance on the final examination was similar in the pre-implementation vs post-implementation phases (81.2% correct vs 80.3% correct; p=0.37). Performance on the neuropathology subsection of the final examination was also similar (82.6% correct vs 83.9% correct; p=0.36). <h3>Conclusions:</h3> We demonstrate the feasibility and utility of a transition to neurologist and neurosurgeon-led lectures and the implementation of a team-based learning curriculum within a neuroscience course. While we report data from implementation at a single center, these results have relevance to other courses given our demonstration that team-based learning is a useful method to deliver neuroscience learning, non-pathologist lecturers can effectively provide neuropathology education, and a small number of educational faculty can be engaged to deliver this material. <b>Disclosure:</b> Dr. Tarolli has received personal compensation for serving as an employee of University of Rochester. The institution of Dr. Tarolli has received research support from Biosensics. The institution of Dr. Tarolli has received research support from National Institutes of Health. The institution of Dr. Tarolli has received research support from Biogen. Dr. Tarolli has received personal compensation in the range of $500-$4,999 for serving as a Speaker with Davis Phinney Foundation. Dr. Tarolli has received personal compensation in the range of $500-$4,999 for serving as a Speaker, Committee Member with American Academy of Neurology. Dr. Tarolli has a non-compensated relationship as a Committee Member with American Academy of Neurology that is relevant to AAN interests or activities. Dr. Jozefowicz has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for American Academy of Neurology.

Curriculum Innovations: Enhancing Medical Student Neuroscience Training With a Team-Based Learning Curriculum

Background and Problem Statement Neurophobia, the fear of, discomfort with, and dislike of clinical neurology, is frequently due to poor experiences in preclinical neuroscience education among medical providers. We developed, implemented, and assessed a curricular innovation using clinician-educators and team-based learning (TBL) with the goals to demonstrate clinical relevance in neuropathology, enhance student engagement in neuropathology education, and promote direct application of knowledge. Methods and Curriculum Description We identified an underperforming neuropathology curriculum within the second-year medical student neuroscience course at the University of Rochester School of Medicine and Dentistry and implemented a traditional TBL curriculum to deliver this content. In addition, we transitioned to primarily clinician-led lectures in the neuropathology curriculum. We assessed student opinions of the curricular changes though end-of-course feedback, the implementation of a novel survey, and semistructured interviews with students. We assessed outcomes on the course final examination and overall course performance, comparing student performance in the preimplementation phase (year 2020–2021) with that in the postimplementation phase (year 2021–2022) using a 2-sample t test. Results and Assessment Student opinions of the curricular changes were positive on the end-of-course evaluation (79.4% rated TBL as good or excellent) and novel survey (89%–96% of students rated the portions of the curriculum positively). Themes identified in free text responses and through qualitative interviews included an appreciation of the streamlined course content and a sense that the various sessions within the neuropathology curriculum effectively reinforced learning. Student performance on the final examination was similar in the preimplementation vs postimplementation phases (81.2% correct vs 80.3% correct; p = 0.37). Performance on the neuropathology subsection of the final examination was also similar among the 2 cohorts (82.6% correct vs 83.9% correct; p = 0.36). Discussion and Lessons Learned We demonstrate the feasibility and utility of a transition to primarily neurologist and neurosurgeon-led lectures and the implementation of a TBL curriculum within a neuroscience course. While we report data from implementation at a single center, these results have potential relevance to other courses, given our demonstration that TBL is a useful method to deliver neuroscience learning, nonpathologist lecturers can effectively provide neuropathology education, and a small number of educational faculty can be engaged to deliver this material.

Rationalization of Character Education in Islamic Religious Education Through Anti-Corruption Education Strategies

It is a new phenomenon in Indonesia regarding cases involving several Muslim officials and politicians. In order to anticipate and eradicate corruption, there is an alternative that can be developed in Islamic Religious Education that is a neuroscience approach using the Anti-Corruption Education strategy at the Early Childhood Education level. The research used is a literature study by reviewing journals that are relevant to the author's research, using secondary data, namely data obtained indirectly. The data needed in this study were taken from documents related to research. The data collection technique was carried out by examining and analyzing books and journals with rules related to the implementation of Anti-Corruption Education through fun learning activities for children carried out by participating in the National Movement for Learning I'm an Honest Child while the implementation of Islamic Religious Education is carried out scientifically and holistically integratively but both not yet integrated. Integration is carried out between Anti-Corruption Education and Islamic Religious Education by using the expansion of various forms of corruption related to the themes conveyed during learning activities. Islamic Religious Education is based on neuroscience learning theories. The data analysis technique used is qualitative analysis. The results of the study show that an educator can apply a neuroscience approach by integrating Anti-Corruption Education and Islamic Religious Education through learning activities that are used as habituation programs, carried out repeatedly and continuously with various learning methods so that honest character recognition can be implemented properly. The educator also stimulates and facilitates introducing this honest character according to the stages of early childhood development. The author's argument states that there is a neuroscience correlation, Islamic Religious Education and Anti-Corruption Education in implementing honesty and the development of Moral and Religious Values in early childhood can develop more optimally. Keywords: Islamic education, brains, goals, character, anti-corruption, and early childhood

Learning neuroscience: Investigating influences of notetaking materials and individual differences

Learning neuroscience: Investigating influences of notetaking materials and individual differences

Optimization of Brain Function Learning (Neocortex) Through Neuroscience Based on Islamic Literature to Improve Behavior of Primary Students

Deep networks in human brain can develop representations that are a better match to recordings in the neocortex of humans as existing models in neuroscience. This suggests that deep learning captures something important about how our own brains works. Islamic education curriculum neuroscience such as Islamic manners lesson, apply learning methods for the development of intellect learners so in the formation of character can maximize the performance of sense and neuroscience of learning students are given the opportunity to actively construct knowledge through learning, thus neuroscience learning combined with a variety of disciplines so integrated into the mainstream of the formation of character especially character of polite and disciplined through Islamic literacy lesson. This research uses mixed methods these following approaches; Literature study and also Class Action Research in three cycles. The results of these research Educational Neuroscience as one of the fields of study of neuroscience specializes in its studies in the field of education from a neuroscience perspective.In learning Islamic fairy tale literacy which was carried out on 3rd grade students of SDIT Aulady, with three students as the object of this research, 3 models of Islamic fairy tale literacy learning were carried out.

Repetition-Based Approach for Task Adaptation in Imitation Learning.

Transfer learning is an effective approach for adapting an autonomous agent to a new target task by transferring knowledge learned from the previously learned source task. The major problem with traditional transfer learning is that it only focuses on optimizing learning performance on the target task. Thus, the performance on the target task may be improved in exchange for the deterioration of the source task’s performance, resulting in an agent that is not able to revisit the earlier task. Therefore, transfer learning methods are still far from being comparable with the learning capability of humans, as humans can perform well on both source and new target tasks. In order to address this limitation, a task adaptation method for imitation learning is proposed in this paper. Being inspired by the idea of repetition learning in neuroscience, the proposed adaptation method enables the agent to repeatedly review the learned knowledge of the source task, while learning the new knowledge of the target task. This ensures that the learning performance on the target task is high, while the deterioration of the learning performance on the source task is small. A comprehensive evaluation over several simulated tasks with varying difficulty levels shows that the proposed method can provide high and consistent performance on both source and target tasks, outperforming existing transfer learning methods.

Student Evaluation of a Learning Community Model Adapted to Student and Curriculum Needs.

The Neuroscience Learning Community (LC) that Stonehill introduced to its curriculum grew out of the Great Recession of 2008 and the need for our students to gain hands-on, high-impact learning experiences, despite limited resources. This learning model was first reported in 2013, and since then it has undergone changes that were necessary due to the number of credits and amount of time required for that model. Curriculum changes are common, and Stonehill College changed its credit requirements for LCs to meet students' needs. As a result, the new Neuroscience LC model that we describe here reduced credit hours while leveraging new faculty expertise, collaborations, and new community partnerships. This paper reports student evaluations of an LC model adapted to demand fewer credits and less time, but to retain the community-based learning aspect and to increase faculty collaboration, while maintaining a high standard of learning fundamental neuroscience topics. Evaluations suggest that students valued the updated Neuroscience LC because it helped them understand neuroscience concepts and the impact of neuroscience in our world.

Optimization of Gobak Sodor Based Neuroscience Learning Game as Character Education in Intellectual Disabilities

The purpose of writing this article is to find out the optimization of traditional gobak sodor neuroscience learning-based game activities as character education in intellectual disability with grade medium (ATG). Referring to the problems, most schools do not have policies and administrations regarding character education, most schools that have an environment that supports the implementation of character education, most teachers do not have good knowledge and attitude in character education, most teachers do not have good competence, most schools have used the curriculum and most teachers have not used assessments that are suitable for children. Character education and most of the community has not supported the course of character education. The purpose of writing this article is to find out the optimization of traditional "gobak sodor" neuroscience learning-based game activities as character education in children ATG. The method used in writing this article is the study of non-systematic literature from national &amp; international database. Based on the concept of a previous relevant frame of mind, schema, and research, it can be concluded that there will be optimization of traditional "gobak sodor" game activities based on neuroscience learning as character education in children. Optimization of adaptive physical education learning can occur through synergistic contributions through neuroscience learning in traditional games and character education of lightly impaired children. The results of adaptation of these mechanisms are able to reduce the barriers of cognitive domains, practice domains, and social domains of lightly impaired children. Keywords: character, neuroscience learning, traditional games, ATG

Foundations of Bayesian Learning in Clinical Neuroscience.

There is an increasing interest in using prediction models to forecast clinical outcomes within the fields of neurosurgery and clinical neuroscience. The present chapter outlines the foundations of Bayesian learning and introduces Bayes theorem and its use in machine learning methodology. The use of Bayesian networks to structure and define associations between outcome predictors and final outcomes is highlighted and Naïve Bayes classifiers are outlined for use in predicting neurosurgical outcomes, where the understanding of underlying causes is less important. The present work aims to orient researchers in Bayesian machine learning methods and when and how to use them. When used correctly, these tools have the potential to improve the understanding of factors influencing neurosurgical outcomes, aid in structuring the relationships between them, and provide reliable machine learning classification models for predicting neurosurgical outcomes.

Advances in modeling learning and decision-making in neuroscience.

An organism's survival depends on its ability to learn about its environment and to make adaptive decisions in the service of achieving the best possible outcomes in that environment. To study the neural circuits that support these functions, researchers have increasingly relied on models that formalize the computations required to carry them out. Here, we review the recent history of computational modeling of learning and decision-making, and how these models have been used to advance understanding of prefrontal cortex function. We discuss how such models have advanced from their origins in basic algorithms of updating and action selection to increasingly account for complexities in the cognitive processes required for learning and decision-making, and the representations over which they operate. We further discuss how a deeper understanding of the real-world complexities in these computations has shed light on the fundamental constraints on optimal behavior, and on the complex interactions between corticostriatal pathways to determine such behavior. The continuing and rapid development of these models holds great promise for understanding the mechanisms by which animals adapt to their environments, and what leads to maladaptive forms of learning and decision-making within clinical populations.

Legal Governance of Brain Data Derived from Artificial Intelligence

Legal Governance of Brain Data Derived from Artificial Intelligence

Stochasticity, Nonlinear Value Functions, and Update Rules in Learning Aesthetic Biases.

A theoretical framework for the reinforcement learning of aesthetic biases was recently proposed based on brain circuitries revealed by neuroimaging. A model grounded on that framework accounted for interesting features of human aesthetic biases. These features included individuality, cultural predispositions, stochastic dynamics of learning and aesthetic biases, and the peak-shift effect. However, despite the success in explaining these features, a potential weakness was the linearity of the value function used to predict reward. This linearity meant that the learning process employed a value function that assumed a linear relationship between reward and sensory stimuli. Linearity is common in reinforcement learning in neuroscience. However, linearity can be problematic because neural mechanisms and the dependence of reward on sensory stimuli were typically nonlinear. Here, we analyze the learning performance with models including optimal nonlinear value functions. We also compare updating the free parameters of the value functions with the delta rule, which neuroscience models use frequently, vs. updating with a new Phi rule that considers the structure of the nonlinearities. Our computer simulations showed that optimal nonlinear value functions resulted in improvements of learning errors when the reward models were nonlinear. Similarly, the new Phi rule led to improvements in these errors. These improvements were accompanied by the straightening of the trajectories of the vector of free parameters in its phase space. This straightening meant that the process became more efficient in learning the prediction of reward. Surprisingly, however, this improved efficiency had a complex relationship with the rate of learning. Finally, the stochasticity arising from the probabilistic sampling of sensory stimuli, rewards, and motivations helped the learning process narrow the range of free parameters to nearly optimal outcomes. Therefore, we suggest that value functions and update rules optimized for social and ecological constraints are ideal for learning aesthetic biases.