[This is an edited and improved version of "You Are Not Your Brain: Against 'Teaching to the Brain'" previously published in *Review of Higher Education and Self-Learning* 5(15), Summer 2012.] Since educators are always looking for ways to improve their practice, and since empirical science is now accepted in our worldview as the final arbiter of truth, it is no surprise they have been lured toward cognitive neuroscience in hopes that discovering how the brain learns will (...) provide a nutshell explanation for student learning in general. I argue that identifying the person with the brain is scientism (not science), that the brain is not the person, and that it is the person who learns. In fact the brain only responds to the learning of embodied experience within the extra-neural network of intersubjective communications. Learning is a dynamic, cultural activity, not a neural program. Brain-based learning is unnecessary for educators and may be dangerous in that a culturally narrow ontology is taken for granted, thus restricting our creativity and imagination, and narrowing the human community. (shrink)

This paper discusses the current fashion for brain-based learning, in which value-laden claims about learning are grounded in neurophysiology. It argues that brain science cannot have the ‘authority’ about learning that some seek to give it. It goes on to discuss whether the claim that brain science is relevant to learning involves a category mistake. The heart of the paper tries to show how the contribution of brain science to our grasp (...) of the nature of learning is limited in principle. Finally the paper explores the potential of brain science to illuminate specific learning disabilities. (shrink)

This paper discusses the current fashion for brain-based learning, in which value-laden claims about learning are grounded in neurophysiology. It argues that brain science cannot have the ‘authority’ about learning that some seek to give it. It goes on to discuss whether the claim that brain science is relevant to learning involves a category mistake. The heart of the paper tries to show how the contribution of brain science to our grasp (...) of the nature of learning is limited in principle. Finally the paper explores the potential of brain science to illuminate specific learning disabilities. (shrink)

Teachers of science-based education in Malaysian secondary schools, especially those in the field of physics, often find their students facing huge difficulties in dealing with conceptual ideas in physics, resulting thus in a lack of interest towards the subject. The aim of this study was to assess the effectiveness of the Brain-Based Teaching Approach (henceforth BBTA) in dealing with the issues of the conceptual understanding of Newtonian physics of Form Four students in secondary science schools in the (...) state of Kedah, situated in the northern region of Peninsular Malaysia, and also their learning motivation towards the subject of physics. The BBTA is based on the Brain-Based Learning Principles, where detailed attention is given to seven main steps: (1) activation; (2) clarify the outcome and paint big picture of the lesson; (3) making connection; (4) doing the learning activity; (5) demonstrate student understanding; (6) review for student recall and retention/closure; and (7) preview the new topic. The effectiveness of the BBTA within the targeted context was then assessed in a quasi-experimental research approach involving 100 Form Four students from two secondary science schools. Data collected using the Test of Conceptual Understanding of Newtonian Physics and the Questionnaire of Physics Learning Motivation were then analysed descriptively and inferentially. The results obtained revealed that the BBTA was in fact exceedingly effective in dealing with the problems aforementioned. It was found that students who received physics education taught using the BBTA possessed a better conceptual understanding of Newtonian physics and also a higher learning motivation in the subject of physics, compared to students who received physics education taught using conventional teaching methods. (shrink)

In this response, we provide further clarification of the propositional approach to human associative learning. We explain why the empirical evidence favors the propositional approach over a dual-system approach and how the propositional approach is compatible with evolution and neuroscience. Finally, we point out aspects of the propositional approach that need further development and challenge proponents of dual-system models to specify the systems more clearly so that these models can be tested.

A fundamental problem with the Clark & Thornton definition of a type-1 problem (requirement 2) is identified. An alternative classical statistical formulation is proposed where a type-1 (learnable) problem corresponds to the case where the learning machine is capable of representing its statistical environment.

Transfer learning can improve the robustness of deep learning in the case of small samples. However, when the semantic difference between the source domain data and the target domain data is large, transfer learning easily introduces redundant features and leads to negative transfer. According the mechanism of the human brain focusing on effective features while ignoring redundant features in recognition tasks, a brain-like classification method based on adaptive feature matching dual-source domain heterogeneous transfer (...) class='Hi'>learning is proposed for the preoperative aided diagnosis of lung granuloma and lung adenocarcinoma for patients with solitary pulmonary solid nodule in the case of small samples. The method includes two parts: (1) feature extraction and (2) feature classification. In the feature extraction part, first, By simulating the feature selection mechanism of the human brain in the process of drawing inferences about other cases from one instance, an adaptive selected-based dual-source domain feature matching network is proposed to determine the matching weight of each pair of feature maps and each pair of convolution layers between the two source networks and the target network, respectively. These two weights can, respectively, adaptive select the features in the source network that are conducive to the learning of the target task, and the destination of feature transfer to improve the robustness of the target network. Meanwhile, a target network based on diverse branch block is proposed, which made the target network have different receptive fields and complex paths to further improve the feature expression ability of the target network. Second, the convolution kernel of the target network is used as the feature extractor to extract features. In the feature classification part, an ensemble classifier based on sparse Bayesian extreme learning machine is proposed that can automatically decide how to combine the output of base classifiers to improve the classification performance. Finally, the experimental results (the AUCs were 0.9542 and 0.9356, respectively) on the data of two center data show that this method can provide a better diagnostic reference for doctors. (shrink)

Brain–computer interfaces (BCIs) utilizing machine learning techniques are an emerging technology that enables a communication pathway between a user and an external system, such as a computer. Owing to its practicality, electroencephalography (EEG) is one of the most widely used measurements for BCI. However, EEG has complex patterns and EEG-based BCIs mostly involve a cost/time-consuming calibration phase; thus, acquiring sufficient EEG data is rarely possible. Recently, deep learning (DL) has had a theoretical/practical impact on BCI research (...) because of its use in learning representations of complex patterns inherent in EEG. Moreover, algorithmic advances in DL facilitate short/zero-calibration in BCI, thereby suppressing the data acquisition phase. Those advancements include data augmentation (DA), increasing the number of training samples without acquiring additional data, and transfer learning (TL), taking advantage of representative knowledge obtained from one dataset to address the so-called data insufficiency problem in other datasets. In this study, we review DL-based short/zero-calibration methods for BCI. Further, we elaborate methodological/algorithmic trends, highlight intriguing approaches in the literature, and discuss directions for further research. In particular, we search forgenerative model-based andgeometric manipulation-based DA methods. Additionally, we categorize TL techniques in DL-based BCIs intoexplicitandimplicitmethods. Our systematization reveals advances in the DA and TL methods. Among the studies reviewed herein, ~45% of DA studies used generative model-based techniques, whereas ~45% of TL studies used explicit knowledge transferring strategy. Moreover, based on our literature review, we recommend an appropriate DA strategy for DL-based BCIs and discuss trends of TLs used in DL-based BCIs. (shrink)

Since educators are always looking for ways to improve their practice, and since empirical science is now accepted in our worldview as the final arbiter of truth, it is no surprise they have been lured toward cognitive neuroscience in hopes that discovering how the brain learns will provide a nutshell explanation for student learning in general. I argue that identifying the person with the brain is scientism (not science), that the brain is not the person, and (...) that it is the person who learns. In fact the brain only responds to the learning of embodied experience within the extra-neural network of intersubjective communications. Learning is a dynamic, cultural activity, not a neural program. Brain-based learning is unnecessary for educators and may be dangerous in that a culturally narrow ontology is taken for granted, thus restricting our creativity and imagination, and narrowing the human community. (shrink)

In the case of magnetic resonance imaging (MRI) imaging, image processing is crucial. In the medical industry, MRI images are commonly used to analyze and diagnose tumor growth in the body. A number of successful brain tumor identification and classification procedures have been developed by various experts. Existing approaches face a number of obstacles, including detection time, accuracy, and tumor size. Early detection of brain tumors improves options for treatment and patient survival rates. Manually segmenting brain tumors (...) from a significant number of MRI data for brain tumor diagnosis is a tough and time-consuming task. Automatic image segmentation of brain tumors is required. The objective of this study is to evaluate the degree of accuracy and simplify the medical picture segmentation procedure used to identify the type of brain tumor from MRI results. Additionally, this work suggests a novel method for identifying brain malignancies utilizing the Bagging Ensemble with K-Nearest Neighbor (BKNN) in order to raise the KNN’s accuracy and quality rate. For image segmentation, a U-Net architecture is utilized first, followed by a bagging-based k-NN prediction algorithm for classification. The goal of employing U-Net is to improve the accuracy and uniformity of parameter distribution in the layers. Each decision tree is fitted on a little different training dataset during classification, and the bagged decision trees are effective since each tree has minor differences and generates slightly different skilled predictions. The overall classification accuracy was up to 97.7 percent, confirming the efficiency of the suggested strategy for distinguishing normal and pathological tissues from brain MR images; this is greater than the methods that are already in use. (shrink)

Recent advances in second-person neuroscience have allowed the underlying neural mechanisms involved in teaching-learning interactions to be better understood. Teaching is not merely a one-way transfer of information from teacher to student; it is a complex interaction that requires metacognitive and mentalizing skills to understand others’ intentions and integrate information regarding oneself and others. Physiotherapy involving therapists instructing patients on how to improve their motor skills is a clinical field in which teaching-learning interactions play a central role. Accumulating (...) evidence suggests that non-invasive brain stimulation modulates cognitive functions; however, NIBS approaches to teaching-learning interactions are yet to be utilized in rehabilitation. In this review, I evaluate the present research into NIBS and its role in enhancing metacognitive and mentalizing abilities; I then review hyperscanning studies of teaching-learning interactions and explore the potential clinical applications of NIBS in rehabilitation. Dual-brain stimulation using NIBS has been developed based on findings of brain-to-brain synchrony in hyperscanning studies, and it is delivered simultaneously to two individuals to increase inter-brain synchronized oscillations at the stimulated frequency. Artificial induction of brain-to-brain synchrony has the potential to promote instruction-based learning. The brain-to-brain interface, which induces inter-brain synchronization by adjusting the patient’s brain activity, using NIBS, to the therapist’s brain activity, could have a positive effect on both therapist-patient interactions and rehabilitation outcomes. NIBS based on second-person neuroscience has the potential to serve as a useful addition to the current neuroscientific methods used in complementary interventions for rehabilitation. (shrink)

Preoperative diagnosis of gastric cancer and primary gastric lymphoma is challenging and has important clinical significance. Inspired by the inductive reasoning learning of the human brain, transfer learning can improve diagnosis performance of target task by utilizing the knowledge learned from the other domains (source domain). However, most studies focus on single-source transfer learning and may lead to model performance degradation when a large domain shift exists between the single-source domain and target domain. By simulating the (...) multi-modal information learning and transfer mechanism of human brain, this study designed a multisource transfer learning feature extraction and classification framework, which can enhance the prediction performance of the target model by using multisource medical data (domain). First, this manuscript designs a feature extraction network that takes the maximum mean difference based on the Wasserstein distance as an adaptive measure of probability distribution and extracts the domain-specific invariant representations between source and target domain data. Then, aiming at the random generation of parameters bringing uncertainties to prediction accuracy and generalization ability of extreme learning machine network, the 1-norm regularization is used to implement sparse constraints of the output weight matrix and improve the robustness of the model. Finally, some experiments are carried out on the data of two medical centers. The experimental results show that the area under curves (AUCs) of the method are 0.958 and 0.929 in the two validation cohorts, respectively. The method in this manuscript can provide doctors with a better diagnostic reference, which has certain practical significance. (shrink)

This article provides a much‐needed review of the neural bases of implicit statistical learning. Batterink, Paller and Reber focus on the neural processes that underpin performance in experimental paradigms employed in implicit learning and statistical learning research. An important insight is that learning across all paradigms is supported by interactions between the declarative and nondeclarative memory systems of the brain. They conclude with a helpful discussion of future directions of research.

This article describes a number of human cohort studies based on the concept of brain‐science and education. These studies assess the potential effects of new technologies on babies, children and adolescents, and test hypotheses drawn from animal and genetic case studies to see if they apply to people. A flood of information, virtual media, individualism and the pursuit of efficiency might be transforming our brain and its functions. An environmental assessment from the metaphysical aspect could be essential (...) to providing an appropriate environment for future generations. (shrink)

Electroencephalography and functional near-infrared spectroscopy have potentially complementary characteristics that reflect the electrical and hemodynamic characteristics of neural responses, so EEG-fNIRS-based hybrid brain-computer interface is the research hotspots in recent years. However, current studies lack a comprehensive systematic approach to properly fuse EEG and fNIRS data and exploit their complementary potential, which is critical for improving BCI performance. To address this issue, this study proposes a novel multimodal fusion framework based on multi-level progressive learning with multi-domain (...) features. The framework consists of a multi-domain feature extraction process for EEG and fNIRS, a feature selection process based on atomic search optimization, and a multi-domain feature fusion process based on multi-level progressive machine learning. The proposed method was validated on EEG-fNIRS-based motor imagery and mental arithmetic tasks involving 29 subjects, and the experimental results show that multi-domain features provide better classification performance than single-domain features, and multi-modality provides better classification performance than single-modality. Furthermore, the experimental results and comparison with other methods demonstrated the effectiveness and superiority of the proposed method in EEG and fNIRS information fusion, it can achieve an average classification accuracy of 96.74% in the MI task and 98.42% in the MA task. Our proposed method may provide a general framework for future fusion processing of multimodal brain signals based on EEG-fNIRS. (shrink)

A growing body of evidence in cognitive psychology and neuroscience suggests a deep interconnection between sensory-motor and language systems in the brain. Based on recent neurophysiological findings on the anatomo-functional organization of the fronto-parietal network, we present a computational model showing that language processing may have reused or co-developed organizing principles, functionality, and learning mechanisms typical of premotor circuit. The proposed model combines principles of Hebbian topological self-organization and prediction learning. Trained on sequences of either motor (...) or linguistic units, the network develops independent neuronal chains, formed by dedicated nodes encoding only context-specific stimuli. Moreover, neurons responding to the same stimulus or class of stimuli tend to cluster together to form topologically connected areas similar to those observed in the brain cortex. Simulations support a unitary explanatory framework reconciling neurophysiological motor data with established behavioral evidence on lexical acquisition, access, and recall. (shrink)

The brain-computer interface based on functional near-infrared spectroscopy has received more and more attention due to its vast application potential in emotion recognition. However, the relatively insufficient investigation of the feature extraction algorithms limits its use in practice. In this article, to improve the performance of fNIRS-based BCI, we proposed a method named R-CSP-E, which introduces EEG signals when computing fNIRS signals’ features based on transfer learning and ensemble learning theory. In detail, we used (...) the Independent Component Analysis algorithm for the correspondence between the sources of the two signals. We then introduced the EEG signals when computing the spatial filter based on a modified Common Spatial Pattern algorithm. Experimental results on public datasets show that the proposed method in this paper outperforms traditional methods without transfer. In general, the mean classification accuracy can be increased by up to 5%. To our knowledge, it is an innovation that we tried to apply transfer learning between EEG and fNIRS. Our study’s findings not only prove the potential of the transfer learning algorithm in cross-model brain-computer interface, but also offer a new and innovative perspective to research the hybrid brain-computer interface. (shrink)

A brain tumor is an abnormal mass or growth of a cell that leads to certain death, and this is still a challenging task in clinical practice. Early and correct diagnosis of this type of cancer is very important for the treatment process. For this reason, this study aimed to develop computer-aided systems for the diagnosis of brain tumors. In this research, we proposed three different end-to-end deep learning approaches for analyzing effects of local and deep features (...) for brain MRI images anomaly detection. The first proposed system is Directional Bit-Planes Deep Autoencoder which extracts and learns local and direction features. The DBP-DAE by decomposition of a local binary pattern into eight bit-planes extracts are directional and inherent local-structure features from the input image and learns robust feature for classification purposes. The second one is a Dilated Separable Residual Convolutional Network which extracts high and low-level features. The main advantage of this approach is that it is robust and shows stable results regardless to size of image database and to solve overfitting problems. To explore the effects of mixture of local and deep extracted feature on accuracy of classification of brain anomaly, a multibranch convolutional neural network approach is proposed. This approach is designed according to combination of DBP-DAE and DSRCN in an end-to-end manner. Extensive experiments conducted based on brain tumor in MRI image public access databases and achieves significant results compared to state-of-the-art algorithms. In addition, we discussed the effectiveness and applicability of CNNs with a variety of different features and architectures for brain abnormalities such as Alzheimer’s. (shrink)

Education theorists have emphasized that transformative learning is not simply a matter of students gaining access to new knowledge and information, but instead centers upon personal transformation: it alters students’ perspectives, interpretations, and responses. How should learning that brings about this sort of self-transformation be understood from the perspectives of philosophy of mind and cognitive science? Jack Mezirow has described transformative learning primarily in terms of critical reflection, meta-cognitive reasoning, and the questioning of assumptions and beliefs. And (...) within mainstream philosophy of mind, there has been a long-standing assumption that cognition and thought are brain-based, computational, disembodied processes that occur separately from emotion and affect. According to this view, selftransformation might be construed as the forging of new neural connections and the development of new cognitive “programs.” However, I will argue that the literature on embodiment and enactivism that has emerged in recent years offers us a different and more productive way to conceptualize the intended effects of transformative learning. From the standpoint of enactivism, the experience of transformative learning is thoroughly bound up with the cognitive shifts that it involves, and it also involves significant changes to the neurobiological dynamics of the living body. Moreover, personal transformation is not simply something that happens to subjects, but rather a process in which they are actively and dynamically engaged. In addition, this enactivist approach emphasizes that the learning process is fully embodied and fundamentally affective. From a phenomenological perspective, personal transformation can be understood as a pronounced alteration in cognitive-affective orientation; and from a neurobiological perspective, the development of new habits of mind can be understood as the formation of highly integrated patterns of bodily engagement and response. The upshot is that it is not just subjects’ brains that are altered over the course of transformative learning, but also their overall bodily and affective attunement to their surroundings. (shrink)

If the cortex is an associative memory, strongly connected cell assemblies will form when neurons in different cortical areas are frequently active at the same time. The cortical distributions of these assemblies must be a consequence of where in the cortex correlated neuronal activity occurred during learning. An assembly can be considered a functional unit exhibiting activity states such as full activation (“ignition”) after appropriate sensory stimulation (possibly related to perception) and continuous reverberation of excitation within the assembly (a (...) putative memory process). This has implications for cortical topographies and activity dynamics of cell assemblies forming during language acquisition, in particular for those representing words. Cortical topographies of assemblies should be related to aspects of the meaning of the words they represent, and physiological signs of cell assembly ignition should be followed by possible indicators of reverberation. The following postulates are discussed in detail: (1) assemblies representing phonological word forms are strongly lateralized and distributed over perisylvian cortices; (2) assemblies representing highly abstract words such as grammatical function words are also strongly lateralized and restricted to these perisylvian regions; (3) assemblies representing concrete content words include additional neurons in both hemispheres; (4) assemblies representing words referring to visual stimuli include neurons in visual cortices; and (5) assemblies representing words referring to actions include neurons in motor cortices. Two main sources of evidence are used to evaluate these proposals: (a) imaging studies focusing on localizing word processing in the brain, based on stimulus-triggered event-related potentials (ERPs), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI), and (b) studies of the temporal dynamics of fast activity changes in the brain, as revealed by high-frequency responses recorded in the electroencephalogram (EEG) and magnetoencephalogram (MEG). These data provide evidence for processing differences between words and matched meaningless pseudowords, and between word classes, such as concrete content and abstract function words, and words evoking visual or motor associations. There is evidence for early word class-specific spreading of neuronal activity and for equally specific high-frequency responses occurring later. These results support a neurobiological model of language in the Hebbian tradition. Competing large-scale neuronal theories of language are discussed in light of the data summarized. Neurobiological perspectives on the problem of serial order of words in syntactic strings are considered in closing. Key Words: associative learning; cell assembly; cognition; cortex; ERP; EEG; fMRI; language; lexicon; MEG; PET; word category. (shrink)

Neuroscience, brain based learning and education -- Collaborative reports in neuroscience and education -- A local paradigmatic example, founded on an international research phenomenon -- The mereological fallacy -- First-person/third-person asymmetry -- Neuroscience and irreducible uncertainty -- Inner and outer : the epistemology of the mind -- Inner and outer : the challenges of crypto-cartesianism, materialism and reductionism -- Intrinsic and relational models of education -- Education, psychology and physics -- Bohr's philosophy of physics and its application (...) to psychology and education -- A new educational philosophy based on Bohr's interpretation of quantum physics -- Conclusions : wittgenstein-bohr model of education. (shrink)

People with post-stroke motor aphasia know what they would like to say but cannot express it through motor pathways due to disruption of cortical circuits. We present a theoretical background for our hypothesized connection between attention and aphasia rehabilitation and suggest why in this context, Brain-Computer Interfaces (BCI) use might be beneficial for patients diagnosed with aphasia. Not only could BCI technology provide a communication tool, it might support neuronal plasticity by activating language circuits and thereby boost aphasia recovery. (...) However, stroke may lead to heterogeneous symptoms that might hinder BCI use which is why the feasibility of this approach needed to be investigated first. In this pilot study, we included five participants diagnosed with post-stroke aphasia. Four participants were initially unable to use the visual P300 speller paradigm. By adjusting the paradigm to their needs, all participants could successfully learn to use the speller for communication with accuracies up to 100%. We describe necessary adjustments to the paradigm and present future steps to further investigate the here presented approach. (shrink)

The vital data about the electrical activities of the brain are carried by the electroencephalography signals. The recordings of the electrical activity of brain neurons in a rhythmic and spontaneous manner from the scalp surface are measured by EEG. One of the most important aspects in the field of neuroscience and neural engineering is EEG signal analysis, as it aids significantly in dealing with the commercial applications as well. To uncover the highly useful information for neural classification activities, (...) EEG studies incorporated with machine learning provide good results. In this study, a Fusion Hybrid Model with Singular Value Decomposition Based Estimation of Robust Parameters is proposed for efficient feature extraction of the biosignals and to understand the essential information it has for analyzing the brain functionality. The essential features in terms of parameter components are extracted using the developed hybrid model, and a specialized hybrid swarm technique called Hybrid Differential Particle Artificial Bee algorithm is proposed for feature selection. To make the EEG more practical and to be used in a plethora of applications, the robust classification of these signals is necessary thereby relying less on the trained professionals. Therefore, the classification is done initially using the proposed Zero Inflated Poisson Mixture Regression Model and then it is also classified with a deep learning methodology, and the results are compared with other standard machine learning techniques. This proposed flow of methodology is validated on a few standard Biosignal datasets, and finally, a good classification accuracy of 98.79% is obtained for epileptic dataset and 98.35% is obtained for schizophrenia dataset. (shrink)

In this paper we analyse the conditions for attributing to AI autonomous systems the ontological status of “artificial moral agents”, in the context of the “distributed responsibility” between humans and machines in Machine Ethics (ME). In order to address the fundamental issue in ME of the unavoidable “opacity” of their decisions with ethical/legal relevance, we start from the neuroethical evidence in cognitive science. In humans, the “transparency” and then the “ethical accountability” of their actions as responsible moral agents is not (...) in contradiction with the unavoidable “opacity” (unawareness) of the brain process by which they perform their moral judgements on the right action to execute. In fact, the moral accountability of our actions depends on what is immediately before and after our “moral judgements” on the right action to execute (formally, deontic first order logic (FOL) decisions). I.e., our moral accountability depends on the “ethical constraints” we imposed to our judgement before performing it in an opaque way. Anyway, our moral accountability depends overall on the “ethical assessment” or explicit “moral reasoning” after and over the moral judgement before executing our actions (deontic higher order logic (HOL) assessment). In this way, in the light of the AI “imitation game”, the consistent attribution of the status of ethically accountable artificial moral agents to autonomous AI systems depends on two similar conditions. Firstly, it depends on the presence of “ethical constraints” to be satisfied in their Machine Learning (ML) supervised optimization algorithm during its training phase, to give the system ethical skills (“competences”) in its decisions. Secondly – and definitely—, it depends on the presence in an AI autonomous system of a deontic HOL “ethical reasoner” to perform an automatic, and fully transparent assessment (metalogical deontic valuation) about the decisions taken by the ethically skilled ML algorithm about the right action to execute, before executing it. Finally, we show that the proper deontic FOL and HOL for this class of artificial moral agents is Kripke’s modal relational logic, in its algebraic topological formalization. This is naturally implemented in the dissipative QFT unsupervised deep learning of our brains, based on the “doubling of the degrees of freedom” (DDF), and then in the so-called “deep-belief” artificial neural networks for the statistical data pre-processing. This unsupervised learning procedure is also compliant with the usage of the “maximin fairness principle”, used as a balancing aggregation principle of the statistical variables in Sen’s formal theory of fairness. (shrink)

Neuroscience, brain based learning and education -- Collaborative reports in neuroscience and education -- A local paradigmatic example, founded on an international research phenomenon -- The mereological fallacy -- First-person/third-person asymmetry -- Neuroscience and irreducible uncertainty -- Inner and outer : the epistemology of the mind -- Inner and outer : the challenges of crypto-cartesianism, materialism and reductionism -- Intrinsic and relational models of education -- Education, psychology and physics -- Bohr's philosophy of physics and its application (...) to psychology and education -- A new educational philosophy based on Bohr's interpretation of quantum physics -- Conclusions : wittgenstein-bohr model of education. (shrink)

This paper proposed a novel tactile-stimuli P300 paradigm for Brain-Computer Interface, which potentially targeted at people with less learning ability or difficulty in maintaining attention. The new paradigm using only two types of stimuli was designed, and different targets were distinguished by frequency and spatial information. The classification algorithm was developed by introducing filters for frequency bands selection and conducting optimization with common spatial pattern on the tactile evoked EEG signals. It features a combination of spatial and frequency (...) information, with the spatial information distinguishing the sites of stimuli and frequency information identifying target stimuli and disturbances. We investigated both electrical stimuli and vibration stimuli, in which only one target site was stimulated in each block. The results demonstrated an average accuracy of 94.88% for electrical stimuli and 95.21% for vibration stimuli, respectively. (shrink)

ObjectiveThis systematic review aims to analyze existing literature of the effects of transcranial direct current stimulation (tDCS) on motor skills learning of healthy adults and discuss the underlying neurophysiological mechanism that influences motor skills learning.MethodsThis systematic review has followed the recommendations of the Preferred Reporting Items for Systematic reviews and Meta-Analyses. The PubMed, EBSCO, and Web of Science databases were systematically searched for relevant studies that were published from database inception to May 2022. Studies were included based (...) on the Participants, Intervention, Comparison, Outcomes, and Setting inclusion strategy. The risk of bias was evaluated by using the Review manager 5.4 tool. The quality of each study was assessed with the Physiotherapy Evidence Database (PEDro) scale.ResultsThe electronic search produced 142 studies. Only 11 studies were included after filtering. These studies performed well in terms of distribution, blinding availability and selective reporting. They reported that tDCS significantly improved motor skills learning. The main outcomes measure were the improvement of the motor sequence tasks and specific motor skills. Nine studies showed that tDCS interventions reduced reaction time to complete motor sequence tasks in healthy adults and two studies showed that tDCS interventions improved golf putting task performance.ConclusionThe included studies showed that tDCS can help healthy adults to improve the motor skills learning by activating different brain regions, such as the primary motor cortex, left dorsolateral prefrontal cortex and right cerebellum. However, the number of included studies was limited, and the sample sizes were small. Therefore, more studies are urgently needed to validate the results of current studies and further explore the underlying neurophysiological mechanisms of tDCS in the future. (shrink)

Based on the newly discovered, extensive manuscript notes of a virtually unknown German medical student by the name of Johann Konrad Zinn, who studied in Padua from 1593 to 1595, this paper offers a detailed account of what medical students could expect to learn about anatomy in late sixteenth-century Padua. It highlights the large number and wide range of anatomical demonstrations, most of which were private anatomies for a small circle of students and do not figure in Acta of (...) the German Nation, the principal source historians have so far relied upon. While the large audience in the big, celebrated public anatomies made it difficult if not impossible for the students to see the details of the anatomical structures, the much more numerous private anatomies offered a view from close up. As Zinn’s notes show, the two leading Paduan anatomists, Hieronymus Fabricius Aquapendente and Giulio Casseri often focused on a specific part of the body, like the brain or the pregnant uterus, and, following the Galenic model, consistently linked the demonstration of the fabric of that part to a discussion of its action and uses. In this sense, the different kinds of valves in the body, including those in the veins, were shown and discussed, as a subsection on William Harvey underlines, and the vivisection of animals for a group of students even allowed them to see the beating heart and other organs in action. In retrospect, much of the anatomical knowledge that students acquired in late sixteenth-century Padua was of limited relevance for medical practice but the anatomists did their best to point out such clinical uses and even used anatomical demonstrations to show different kinds surgical interventions on the corpse. (shrink)

Extended and distributed cognition theories argue that human cognitive systems sometimes include non-biological objects. On these views, the physical supervenience base of cognitive systems is thus not the biological brain or even the embodied organism, but an organism-plus-artifacts. In this paper, we provide a novel account of the implications of these views for learning, education, and assessment. We start by conceptualising how we learn to assemble extended cognitive systems by internalising cultural norms and practices. Having a better grip (...) on how extended cognitive systems are assembled, we focus on the question: If our cognition extends, how should we educate and assess such extended cognitive systems? We suggest various ways to minimize possible negative effects of extending one’s cognition and to efficiently find and organise (online) information by adopting a virtue epistemology approach. Educational and assessment implications are foregrounded, particularly in the case of Danish students’ use of the Internet during exams. (shrink)

Decision making in any brain is imperfect and costly in terms of time and energy. Operating under such constraints, an organism could be in a position to improve performance if an opportunity arose to exploit informative patterns in the environment being searched. Such an improvement of performance could entail both faster and more accurate (i.e., reward-maximizing) decisions. The present study investigated the extent to which human participants could learn to take advantage of immediate patterns in the spatial arrangement of (...) serially presented foods such that a region of space would consistently be associated with greater subjective value. Eye movements leading up to choices demonstrated rapidly induced biases in the selective allocation of visual fixation and attention that were accompanied by both faster and more accurate choices of desired goods as implicit learning occurred. However, for the control condition with its spatially balanced reward environment, these subjects exhibited preexisting lateralized biases for eye and hand movements (i.e., leftward and rightward, respectively) that could act in opposition not only to each other but also to the orienting biases elicited by the experimental manipulation, producing an asymmetry between the left and right hemifields with respect to performance. Potentially owing at least in part to learned cultural conventions (e.g., reading from left to right), the findings herein particularly revealed an intrinsic leftward bias underlying initial saccades in the midst of more immediate feedback-directed processes for which spatial biases can be learned flexibly to optimize oculomotor and manual control in value-based decision making. The present study thus replicates general findings of learned attentional biases in a novel context with inherently rewarding stimuli and goes on to further elucidate the interactions between endogenous and exogenous biases. (shrink)

The distinction between students diagnosed with a learning disability and those considered merely slow learners is based on conceptually flawed assumptions that: 1) LD represents a brain dysfunction while SL does not; 2) LD is a well-defined disorder; 3) valid measurement instruments distinguish LD and SL; 4) special education for students with LD is fundamentally different from that for SL students. These erroneous beliefs are maintained because governmental legislation transformed a diagnosis of LD into an admission ticket (...) to a variety of government entitlements thereby subjecting it to political, legal, social and ideological forces. (shrink)

AcknowledgmentsIntroduction: Social Theory After Cognitive Science1. Throwing Out the Tacit Rule Book: Learning and Practices2. Searle's Social Reality3. Imitation or the Internalization of Norms: Is Twentieth-Century Social Theory Based on the Wrong Choice?4. Relativism as Explanation5. The Limits of Social Constructionism6. Making Normative Soup Out of Nonnormative Bones7. Teaching Subtlety of Thought: The Lessons of "Contextualism"8. Practice in Real Time9. The Significance of ShilsReferences Index Copyright © Libri GmbH. All rights reserved.

Background and ObjectivesFocal cortical dysplasia is a type of malformations of cortical development and one of the leading causes of drug-resistant epilepsy. Postoperative results improve the diagnosis of lesions on structural MRIs. Advances in quantitative algorithms have increased the identification of FCD lesions. However, due to significant differences in size, shape, and location of the lesion in different patients and a big deal of time for the objective diagnosis of lesion as well as the dependence of individual interpretation, sensitive approaches (...) are required to address the challenge of lesion diagnosis. In this research, a FCD computer-aided diagnostic system to improve existing methods is presented.MethodsMagnetic resonance imaging data were collected from 58 participants. Morphological and intensity-based features were calculated for each cortical surface and inserted into an artificial neural network. Statistical examinations evaluated classifier efficiency.ResultsNeural network evaluation metrics—sensitivity, specificity, and accuracy—were 96.7, 100, and 98.6%, respectively. Furthermore, the accuracy of the classifier for the detection of the lobe and hemisphere of the brain, where the FCD lesion is located, was 84.2 and 77.3%, respectively.ConclusionAnalyzing surface-based features by automated machine learning can give a quantitative and objective diagnosis of FCD lesions in presurgical assessment and improve postsurgical outcomes. (shrink)

Rips et al. appear to discuss, and then dismiss with counterexamples, the brain-based theory of mathematical cognition given in Lakoff and Nez (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.

What type of artificial systems will claim to be conscious and will claim to experience qualia? The ability to comment upon physical states of a brain-like dynamical system coupled with its environment seems to be sufficient to make claims. The flow of internal states in such system, guided and limited by associative memory, is similar to the stream of consciousness. Minimal requirements for an artificial system that will claim to be conscious were given in form of specific architecture named (...) articon. Nonverbal discrimination of the working memory states of the articon gives it the ability to experience different qualities of internal states. Analysis of the inner state flows of such a system during typical behavioral process shows that qualia are inseparable from perception and action. The role of consciousness in learning of skills, when conscious information processing is replaced by subconscious, is elucidated. Arguments confirming that phenomenal experience is a result of cognitive processes are presented. Possible philosophical objections based on the Chinese room and other arguments are discussed, but they are insufficient to refute claims articon’s claims. Conditions for genuine understanding that go beyond the Turing test are presented. Articons may fulfill such conditions and in principle the structure of their experiences may be arbitrarily close to human. (shrink)

This book explores the ways in which humor can enhance the learning environment. Drawing upon empirical research and brain-based concepts, Jonas presents a theoretical model of humor, along with practical examples for enhancing learning in schools and classrooms.

The landmark book Boys and Girls Learn Differently! outlines the brain-based educational theories and techniques that can be used to transform classrooms and help children learn better. Now The Boys and Girls Learn Differently Action Guide for Teachers presents experiential learning techniques that teachers can use to create an environment and enriched curriculum that take into account the needs of the developing child's brain and allows both boys and girls to gain maximum learning opportunities. This (...) important and easy-to-use guide is based on the latest scientific scholarship on the differences between boy's and girl's brains, neurological development, hormonal effects, behavior, and learning needs and offers information on what all children need to be able to learn effectively. Michael Gurian and his colleagues applied these recent discoveries in the field during a two-year Gurian Institute pilot program in Missouri that led to measurably better academic performance and improved behavior. (shrink)

Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about$750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) (...) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences. (shrink)

Despite technological innovations, clinical expertise remains the cornerstone of psychiatry. A clinical expert does not only have general textbook knowledge, but is sensitive to what is demanded for the individual patient in a particular situation. A method that can do justice to the subjective and situation-specific nature of clinical expertise is ethnography. Effective deep brain stimulation (DBS) for obsessive-compulsive disorder (OCD) involves an interpretive, evaluative process of optimizing stimulation parameters, which makes it an interesting case to study clinical expertise. (...) The aim of this study is to explore the role of clinical expertise through an ethnography of the particular case of DBS optimization in OCD. In line with the topic of the special issue this article is a part of, we will also use our findings to reflect on ethnography as a method to study complex phenomena like clinical expertise. This ethnography of DBS optimization is based on 18 months of participant observation and nine in-depth interviews with a team of expert clinicians who have been treating over 80 OCD patients since 2005. By repeatedly observing particular situations for an extended period of time, we found that there are recurrent patterns in the ways clinicians interact with patients. These patterns of clinical practice shape the possibilities clinicians have for making sense of DBS-induced changes in patients’ lived experience and behavior. Collective established patterns of clinical practice are dynamic and change under the influence of individual learning experiences in particular situations, opening up new possibilities and challenges. We conclude that patterns of clinical practice and particular situations are mutually constitutive. Ethnography is ideally suited to bring this relation into view thanks to its broad temporal scope and focus on the life-world. Based on our findings, we argue that clinical expertise not only implies skillful engagement with a concrete situation but also with the patterns of clinical practice that shape what is possible in this specific situation. Given this constraining and enabling role of practices, it is important to investigate them in order to find ways to improve diagnostic and therapeutic possibilities. (shrink)

Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder, definitive neuroimaging markers remain obscured due to inconsistent or incompatible findings, especially for structural imaging. Furthermore, brain differences defined by statistical analysis are difficult to implement individual prediction. The present study has employed the machine learning techniques under the unified framework in neuroimaging to identify the neuroimaging markers of patients with ASD and distinguish them from typically developing controls. To enhance the interpretability of the machine (...) learning model, the study has processed three levels of assessments including model-level assessment, feature-level assessment, and biology-level assessment. According to these three levels assessment, the study has identified neuroimaging markers of ASD including the opercular part of bilateral inferior frontal gyrus, the orbital part of right inferior frontal gyrus, right rolandic operculum, right olfactory cortex, right gyrus rectus, right insula, left inferior parietal gyrus, bilateral supramarginal gyrus, bilateral angular gyrus, bilateral superior temporal gyrus, bilateral middle temporal gyrus, and left inferior temporal gyrus. In addition, negative correlations between the communication skill score in the Autism Diagnostic Observation Schedule and regional gray matter volume in the gyrus rectus, left middle temporal gyrus, and inferior temporal gyrus have been detected. A significant negative correlation has been found between the communication skill score in ADOS_G and the orbital part of the left inferior frontal gyrus. A negative correlation between verbal skill score and right angular gyrus and a significant negative correlation between non-verbal communication skill and right angular gyrus have been found. These findings in the study have suggested the GM alteration of ASD and correlated with the clinical severity of ASD disease symptoms. The interpretable machine learning framework gives sight to the pathophysiological mechanism of ASD but can also be extended to other diseases. (shrink)

This paper seeks to explain learning by examining five theories of learning—conceptual analysis, behavioural, constructivist, computational and connectionist. The first two are found wanting and rejected. Piaget's constructivist theory offers a general explanatory framework but fails to provide an adequate account of the empirical mechanisms of learning. Two theories from cognitive science offering rival explanations of learning are finally considered; it is argued that the brain is not like a computer so the computational model is (...) rejected in favour of a neurally‐based connectionist model of learning. (shrink)

Emotion has a substantial influence on the cognitive processes in humans, including perception, attention, learning, memory, reasoning, and problem solving. Emotion has a particularly strong influence on attention, especially modulating the selectivity of attention as well as motivating action and behavior. This attentional and executive control is intimately linked to learning processes, as intrinsically limited attentional capacities are better focused on relevant information. Emotion also facilitates encoding and helps retrieval of information efficiently. However, the effects of emotion on (...) learning and memory are not always univalent, as studies have reported that emotion either enhances or impairs learning and long-term memory retention, depending on a range of factors. Recent neuroimaging findings have indicated that the amygdala and prefrontal cortex cooperate with the medial temporal lobe in an integrated manner that affords (i) the amygdala modulating memory consolidation; (ii) the prefrontal cortex mediating memory encoding and formation; and (iii) the hippocampus for successful learning and long-term memory retention. We also review the nested hierarchies of circular emotional control and cognitive regulation (bottom-up and top-down influences) within the brain to achieve optimal integration of emotional and cognitive processing. This review highlights a basic evolutionary approach to emotion to understand the effects of emotion on learning and memory and the functional roles played by various brain regions and their mutual interactions in relation to emotional processing. We also summarize the current state of knowledge on the impact of emotion on memory and map implications for educational settings. In addition to elucidating the memory-enhancing effects of emotion, neuroimaging findings extend our understanding of emotional influences on learning and memory processes; this knowledge may be useful for the design of effective educational curricula to provide a conducive learning environment for both traditional “live” learning in classrooms and “virtual” learning through online-based educational technologies. (shrink)

In recent years, there have been many attempts at defining learning as a social phenomenon as opposed to an individual and primarily psychological matter. The move towards understanding learning as social processes has also altered the concept of knowledge as a well-defined element stored in books, brains, CD-Roms, disks, videos or on the Internet. Instead, knowledge has been perceived as a social and context related construction. The roots of the social angle within theories on learning and knowledge (...) are much older than the current literature suggests. This paper illustrates how these theories can be traced back to pragmatism as a philosophy and foundation for an educational approach introduced by the American, John Dewey, more than one hundred years ago. The paper also suggests that Dewey avoids some pitfalls that have come with the new theories, particularly the strong division between individual vs. social and school vs. everyday life learning. (shrink)

Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surprisingly elegant and powerful models (...) of memory, perception, motor control, categorization, and reasoning. What makes the connectionist approach unique is not its variety of representational possibilities or its departure from explicit rule-based models, or even its preoccupation with the brain metaphor. Rather, it is that connectionist models can be used to explore systematically the complex interaction between learning and representation, as we try to demonstrate through the analysis of several large networks. (shrink)

What type of artificial systems will claim to be conscious and will claim to experience qualia? The ability to comment upon physical states of a brain-like dynamical system coupled with its environment seems to be sufficient to make claims. The flow of internal states in such systems, guided and limited by associative memory, is similar to the stream of consciousness. A specific architecture of an artificial system, termed articon, is introduced that by its very design has to claim being (...) conscious. Non-verbal discrimination of the working memory states of the articon gives it the ability to experience different qualities of internal states. Analysis of the flow of inner states of such a system during typical behavioral process shows that qualia are inseparable from perception and action. The role of consciousness in learning of skills — when conscious information processing is replaced by subconscious — is elucidated. Arguments confirming that phenomenal experience is a result of cognitive processes are presented. Possible philosophical objections based on the Chinese room and other arguments are discussed, but they are insufficient to refute articon’s claims that it is conscious. Conditions for genuine understanding that go beyond the Turing test are presented. Articons may fulfill such conditions and in principle the structure of their experiences may be arbitrarily close to human. (shrink)

In our view, a central issue in relating brain development to education is whether classroom interventions can alter neural networks related to cognition in ways that generalize beyond the specific domain of instruction. This issue depends upon understanding how neural networks develop under the influence of genes and experience. Imaging studies have revealed common networks underlying many important tasks undertaken at school, such as reading and number skills, and we are beginning to learn how genes and experience work together (...) to shape the development of these networks. The results obtained appear sufficient to propose research-based interventions that could prove useful in improving the ability of children to adjust to the school setting and to acquire skills like literacy and numeracy. (shrink)

The high prevalence of brain injury incidents in adolescence and adulthood demands effective models for re-learning lost cognitive abilities. Impairment in brain injury survivors’ higher-level cognitive functions is common and a negative predictor for long-term outcome. We conducted two small-scale interventions (N = 12; 33.33% female) with persons with acquired brain injuries in two municipalities in Sweden. Age ranged from 17 to 65 years (M = 51.17, SD = 14.53). The interventions were dialogic, inquiry-based, and (...) inspired by the Philosophy for Children Programme, a participatory thinking skills approach with documented higher-order cognitive outcomes, such as developed argumentation skills, in other target groups. Philosophical dialogues were conducted once a week in the two groups, totalling 12 dialogues per group. Group argumentation development was measured through compared scores from structured observations of filmed dialogues early and late in the intervention. Large positive changes in mean scores from early to late in the intervention, together with constantly high facilitator quality, suggest argumentation development in the sample due to the intervention. (shrink)