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In this paper I have chosen the topic of psychoanalysis in the age of neuroscience, with the aim of showing why the cultural history of psychoanalysis still matters. To make myself better understood I shall refrain from evaluating the current findings in neuroscience and limit myself to reporting briefly on them. Although I do not regard myself by any means as an expert in that field, I may be permitted to offer a few ideas about it. In this regard, there is presently a significant predominance of biological ideologies and practices regarding the treatment of mental illness, which implies an increase in the interest in etiology, nosology, definitions, and the effectivity of treatments. Even so, those psychoanalytic historians and/or analysts among us who are committed to psychoanalysis and its therapeutic implications, irrespective of what drugs might be prescribed and what the research findings might conclude, believe that patients still want to be listened to in depth and always will. For that reason, it is justified to ask why the cultural history of psychoanalysis still matters in a contemporary mental health environment that is ever more oriented towards the neurosciences. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the psychometric properties of common health-related quality-of-life instruments used post stroke and provide recommendations for research and clinical use with this diagnostic group. Methods: A systematic review of the psychometric properties of the five most commonly used quality-of-life measurement tools (EQ-5D, SF-36, SF-6D, AQoL, SS-QOL) was conducted. Electronic searches were performed in MEDLINE, CINAHL, and EMBASE on November 27th 2019. Two authors screened papers against the inclusion criteria and where consensus was not reached, a third author was consulted. Included papers were appraised using the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) checklist and findings synthesized to make recommendations. Results: A total of n = 50,908 papers were screened and n = 45 papers reporting on 40 separate evaluations of psychometric properties met inclusion criteria (EQ-5D = 19, SF-36 = 16, SF-6D = 4, AQoL = 2, SS-QOL = 4). Studies reported varied psychometric quality of instruments, and results show that psychometric properties of quality-of-life instruments for the stroke population have not been well established. The strongest evidence was identified for the use of the EQ-5D as a quality-of-life assessment for adult stroke survivors. Conclusions: This systematic evaluation of the psychometric properties of self-reported quality-of-life instruments used with adults after stroke suggests that validity across tools should not be assumed. Clinicians and researchers alike may use findings to help identify the most valid and reliable measurement instrument for understanding the impact of stroke on patient-reported quality of life. [ABSTRACT FROM AUTHOR]

This Special Issue of Human Brain Mapping is dedicated to a 10‐year anniversary of the Enhancing NeuroImaging Genetics through Meta‐Analysis (ENIGMA) Consortium. It reports updates from a broad range of international neuroimaging projects that pool data from around the world to answer fundamental questions in neuroscience. Since ENIGMA was formed in December 2009, the initiative grew into a worldwide effort with over 2,000 participating scientists from 45 countries, and over 50 working groups leading large‐scale studies of human brain disorders. Over the last decade, many lessons were learned on how best to pool brain data from diverse sources. Working groups were created to develop methods to analyze worldwide data from anatomical and diffusion magnetic resonance imaging (MRI), resting state and task‐based functional MRI, electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance spectroscopy (MRS). The quest to understand genetic effects on human brain development and disease also led to analyses of brain scans on an unprecedented scale. Genetic roadmaps of the human cortex were created by researchers worldwide who collaborated to perform statistically well‐powered analyses of common and rare genetic variants on brain measures and rates of brain development and aging. Here, we summarize the 31 papers in this Special Issue, covering: (a) technical approaches to harmonize analysis of different types of brain imaging data, (b) reviews of the last decade of work by several of ENIGMA's clinical and technical working groups, and (c) new empirical papers reporting large‐scale international brain mapping analyses in patients with substance use disorders, schizophrenia, bipolar disorders, major depression, posttraumatic stress disorder, obsessive compulsive disorder, epilepsy, and stroke. [ABSTRACT FROM AUTHOR]

The interest to connect results of neuroscientific research to educational contexts has increasingly grown in recent years. Actors from neuroscience and education show the explicit intention to approach each other. Still, issues and debates exist in the relation between them. This paper aims to bring to the fore one such specific issue that is not only relevant to be mindful of, but also raises questions of an organisational and pedagogical nature. The issue concerns the possible occurrences of conceptual confusion on the bridge from neuroscience to education. I present this paper as a thought experiment that hopes to make public not only the respective issue, but also some of its related questions, such as 'neuroprofessionalisation' and 'relevant knowledge for educational actors'. In doing so, I attempt to make the issue and its questions a concern of ours, and invite readers to join in a collective and vivid conversation about it. [ABSTRACT FROM AUTHOR]

This umbrella review aimed to systematically identify the peri-operative risk factors associated with post-operative cognitive dysfunction (POCD) using meta-analyses of observational studies. To date, no review has synthesised nor assessed the strength of the available evidence examining risk factors for POCD. Database searches from journal inception to December 2022 consisted of systematic reviews with meta-analyses that included observational studies examining pre-, intra- and post-operative risk factors for POCD. A total of 330 papers were initially screened. Eleven meta-analyses were included in this umbrella review, which consisted of 73 risk factors in a total population of 67,622 participants. Most pertained to pre-operative risk factors (74%) that were predominantly examined using prospective designs and in cardiac-related surgeries (71%). Overall, 31 of the 73 factors (42%) were associated with a higher risk of POCD. However, there was no convincing (class I) or highly suggestive (class II) evidence for associations between risk factors and POCD, and suggestive evidence (class III) was limited to two risk factors (pre-operative age and pre-operative diabetes). Given that the overall strength of the evidence is limited, further large-scale studies that examine risk factors across various surgery types are recommended. [ABSTRACT FROM AUTHOR]

"Emergence", the phenomenon where a complex system displays properties, behaviours, or dynamics not trivially reducible to its constituent elements, is one of the defining properties of complex systems. Recently, there has been a concerted effort to formally define emergence using the mathematical framework of information theory, which proposes that emergence can be understood in terms of how the states of wholes and parts collectively disclose information about the system's collective future. In this paper, we show how a common, foundational component of information-theoretic approaches to emergence implies an inherent instability to emergent properties, which we call flickering emergence. A system may, on average, display a meaningful emergent property (be it an informative coarse-graining, or higher-order synergy), but for particular configurations, that emergent property falls apart and becomes misinformative. We show existence proofs that flickering emergence occurs in two different frameworks (one based on coarse-graining and another based on multivariate information decomposition) and argue that any approach based on temporal mutual information will display it. Finally, we argue that flickering emergence should not be a disqualifying property of any model of emergence, but that it should be accounted for when attempting to theorize about how emergence relates to practical models of the natural world. [ABSTRACT FROM AUTHOR]

From the very beginning of their life, human beings are immersed in a social and interactive environment that contributes to shaping their social and cognitive development under typical and at-risk conditions. In order to understand human development in its bidirectional relationship with the social environment, we need to develop a 'complexity-sensitive' approach in neuroscience. Recent advances have started to do so with the application of hyperscanning techniques which involve recording adult and child neural activity simultaneously and highlighting the presence of similar patterns of brain activity in the dyad. Numerous studies focused on typically developing children have been published in recent years with the application of this technique to different fields of developmental research. However, hyperscanning techniques could also be extremely beneficial and effective in studying development in atypical and clinical populations. Such application, namely translational hyperscanning, should foster the transition toward a two-brain translational neuroscience. In this paper, we envision how the application of hyperscanning to atypical and clinical child populations can inform family-centered care for children and their parents. [ABSTRACT FROM AUTHOR]

This paper analyses new fact-finding methods in criminal proceedings, using state-of-the-art innovations in neuroscience and artificial intelligence (AI). It will outline the existing methods and explain their effects. Then it will address the criminal-law aspects of the use of such methods as evidence in criminal proceedings, with an emphasis on the assessment of their admissibility from the perspective of the right to a fair trial and the privilege against self-incrimination. This topic will be observed from the perspective of US and European law, highlighting the existing jurisprudence of the European Court of Human Rights (ECtHR) and the legal standards established by the court in relation to the privilege against self-incrimination. Based on this analysis, the authors will formulate a conclusion suggesting that the use of current AI technologies should be juxtaposed to the relevant benchmark of the privilege against self-incrimination as the requisite standard of the right to a fair trial. [ABSTRACT FROM AUTHOR]

Recent work in several fields of psychology has advanced understanding of how humans imaginatively construct, simulate and (pre‐)feel the future. These advances have not yet been substantively engaged in social and cultural geography. In this paper, we identify, review and begin to draw together scholarship in human geography and several subfields of psychology on the ways in which people imagine and navigate towards the future. The most influential existing work on the future in geography has concerned powerful institutional and discursive depictions of threatening times‐to‐come. In contrast, psychological and neuroscientific work on cognitive processes involved in prospection extends possibilities for a human geographical approach to the future considering how people relate to discursive imaginaries and spatial environments. Reinvigoration of the human geography‐psychology nexus can further critical understanding of the spatialities through which futures are imaginatively formed and felt by individuals, and are thereby brought into the realm of political and social possibility. [ABSTRACT FROM AUTHOR]

Neuroimaging experiments in general, and EEG experiments in particular, must take care to avoid confounds. A recent TPAMI paper uses data that suffers from a serious previously reported confound. We demonstrate that their new model and analysis methods do not remedy this confound, and therefore that their claims of high accuracy and neuroscience relevance are invalid. [ABSTRACT FROM AUTHOR]

This article addresses missional spirituality, that is, what spirituality, individual and corporate, is appropriate and fruitful in churches which seek to participate in the mission of God. The paper engages with neuroscience around how the right and left hemispheres of the brain offer two ways of attending to the world; Iain McGilchrist asserts in The Master and his Emissary that left brain attention has predominated especially in Modernity. Two Christian responses which utilize these truths about the brain are critically examined, one from an American, broadly Evangelical school of thought and the other from the Anglican Solitary, Maggie Ross. Ross’ recovery of the apophatic stream of Christian spirituality via the right brain of “Deep Mind” is preferred without dismissing other approaches. The article delineates the “unintended consequences” of seeking silence before God who is, including the gift of “beholding” and how they connect to what is required of missional churches. Some brief suggestions are made as to what might be done to enhance the practice of silence in local churches. [ABSTRACT FROM AUTHOR]

The experimental method has promoted the popularity of neuroscientific research on the human mind. In this interdisciplinary enterprise, the experimental method, with its roots in natural science and experimental psychology, is often uncritically accepted as the royal road to investigate the human mind not only by neuroscientists, but by many philosophers as well, especially those inclined to some form of naturalism. It is rarely disputed that experiments reveal actual states of nature (here: of mind and/or brain). Experimental results are used to picture the human person or subject as an illusionary construct resulting from neuronal interactions. The present paper sketches some of the limitations of neuroscientific experiments in order to demonstrate that cognitive neuroscience is far from relying on firm methodological grounds. Numerous issues still have to be solved, some of which date back to the early days of modern science. At least, to make experiments work, many theoretical presuppositions have to be accepted and decisions of relevance have to be made in the scientific process. This implies that all scientific endeavor is constituted by persons making free decisions for good reasons, despite all reductionist claims to the contrary. The fact that we as scientists have to distinguish relevant from irrelevant aspects of experimental procedures is also crucial for dealing with the current replicability crisis in the life sciences including neuroscience. [ABSTRACT FROM AUTHOR]

After decades of being ignored, the concept of beauty, as understood by the non-architect, has recently been making a comeback in architecture, not so much in the practice itself, as in appeals for design solutions that are more human-centered and not dictated by abstract principles. Architectural beauty needs to be evaluated from its effects on human health. This study discusses two diagnostic tools for measuring the degree of architectural "beauty" and presents the results of the pilot application of one of them. The goal is to use diagnostic imaging for evaluations. Analytical elements are introduced from disciplines with which practitioners are normally not familiar, such as artificial intelligence, medicine, neuroscience, visual attention and image-processing software, etc. In addition to the diagnostic tools, this paper ties related ideas on objective beauty into a novel synthesis. These results support the idea of a feasible, "objective" way to evaluate what the users will consider as beautiful, and set the stage for an upcoming larger study that will quantitatively correlate the two methods. [ABSTRACT FROM AUTHOR]

Deep learning is a popular topic among machine learning researchers nowadays, with great strides being made in recent years to develop robust artificial neural networks for faster convergence to a reasonable accuracy. Network architecture and hyperparameters of the model are fundamental aspects of model convergence. One such important parameter is the initial values of weights, also known as weight initialization. In this paper, we perform two research tasks concerned with the weights of neural networks. First, we develop three novel weight initialization algorithms inspired by the neuroscientific construction of the mammalian brains and then test them on benchmark datasets against other algorithms to compare and assess their performance. We call these algorithms the lognormal weight initialization, modified lognormal weight initialization, and skewed weight initialization. We observe from our results that these initialization algorithms provide state-of-the-art results on all of the benchmark datasets. Second, we analyze the influence of training an artificial neural network on its weight distribution by measuring the correlation between the quantitative metrics of skewness and kurtosis against the model accuracy using linear regression for different weight initializations. Results indicate a positive correlation between network accuracy and skewness of the weight distribution but no affirmative relation between accuracy and kurtosis. This analysis provides further insight into understanding the inner mechanism of neural network training using the shape of weight distribution. Overall, the works in this paper are the first of their kind in incorporating neuroscientific knowledge into the domain of artificial neural network weights. [ABSTRACT FROM AUTHOR]

In recent years, neuroscience and brain-based approaches to education have started to feature prominently in the rationale for radical educational reform, both in terms of policy and practice. Revelations about what way the brain works, it seems, is a common point of interest for neuroscience and education alike. Out of these common interests there are now entire disciplines in their own right, such as 'mind, brain and education' and 'neuroeducation', which are collaborative disciplines formed at the boundaries of common points of interest between the brain sciences in general and education. This paper seeks to examine the credibility of the science inherent in this collaboration, with a particular example found in the guise of the Revised Northern Ireland Curriculum, devised and implemented from 2003 onwards by the Council for the Curriculum, Examinations and Assessment (CCEA). The paper will also invoke a philosophical notion known as the mereological fallacy, to offer a conceptual critique of the curricular changes in Northern Ireland. [ABSTRACT FROM AUTHOR]

Research has shown that the Flash Technique (FT) appears to reduce memory-related disturbance and may reduce symptoms of posttraumatic stress disorder. This paper discusses the connections between FT and eye movement desensitization and reprocessing (EMDR) therapy. In FT, clients remind themselves of a traumatic memory without dwelling on it and focus instead on a positive engaging focus and then blink their eyes when prompted. This paper summarizes numerous models describing how the brain processes traumatic material and presents a model for how FT may work in the brain. It proposes that during the blinking, the patient's periaqueductal gray (PAG) may take over, sensing the reminder of the traumatic memory and reflexively triggering the amygdala. In Porges's neuroception model, the PAG assesses danger without going through the conscious brain. Recent fMRI data show that for patients with posttraumatic stress disorder, there is enhanced connectivity from the amygdala to the left hippocampus. Thus, triggering the amygdala may, in turn, activate the left hippocampus, which may then provide a brief access to the traumatic memory. Given the brief access, there is insufficient time for the amygdala to go into overactivation. The client remains calm while accessing the traumatic memory, thus setting up the prediction error necessary for possible memory reconsolidation. This process is repeated during blinking in FT allowing memory reconsolidation to proceed. This model requires experimental confirmation. [ABSTRACT FROM AUTHOR]

Over the past few years, the computer vision domain has evolved and made a revolutionary transition from human-engineered features to automated features to address challenging tasks. Computer vision is an ever-evolving domain with its roots deeply correlated with neuroscience; any new findings that trigger a more intuitive understanding and working of the human visual system generally impact the design strategy of computer vision algorithms. The convolutional neural network is one such algorithm that is currently the de facto standard for most computer vision tasks such as image classification, object detection, image segmentation, etc. As convolutional neural networks are associated with inherent constraints such as the requirement for an immense amount of labeled data and an inefficient data routing policy, capsule networks could be a viable alternative. Upheld by the backpropagation and the dynamic routing algorithm, the capsule network has set the new paradigm for developing reliable computer vision algorithms. Despite the phenomenal theoretical backing from neuroscience and the groundbreaking performance on benchmark datasets, the lack of information concerning the conception and working of capsule networks becomes the major impediment to adopting them for computer vision algorithms. This paper presents a concise overview of capsule network-based classification architectures, routing algorithms, performance analysis, limitations, and future scope, helping the research community to adopt capsule networks at the forefront of modern computer vision research. [ABSTRACT FROM AUTHOR]

Scientists often respond to failures to replicate by citing differences between the experimental components of an original study and those of its attempted replication. In this paper, we investigate these purported mismatch explanations. We assess a body of failures to replicate in neuroscience studies on spinal cord injury. We argue that a defensible mismatch explanation is one where (1) a mismatch of components is a difference maker for a mismatch of outcomes, and (2) the components are relevantly different in the follow-up study, given the scope of the original study. With this account, we argue that not all differences between studies are meaningful, even if they are difference makers. As our examples show, focusing only on these differences results in disregarding the representativeness of the original experiment’s components and the scope of its outcomes, undercutting other epistemic aims, such as translation, in the process. [ABSTRACT FROM AUTHOR]

This paper is extracted from the research "Relationship between executive functions and social cognition with job performance in the Corporación Universitaria Minuto de Dios, seccional Bello" carried out in 2019. Social cognition as a fundamental part of higher cognitive processes and Person - Environment interaction is presented as a leading actor at work and in its manifestations, that is, the level of work performance is closely linked to social interaction skills. The article works with a population made up of university professors from the city of Medellín (Colombia) whose performance is variable but within the criteria accepted by that educational institution and who, in addition, do not present emotional alterations or mental illnesses categorized in the DSM -V. A series of tests are applied to this population that are detailed throughout the writing and a direct relationship between social cognition and performance is evidenced. This investigative work promises a great contribution to the improvement, not only of the performance of the teachers, but also of the quality of life of them and their families and the positioning and continuous improvement of the IES (Institutions of Higher Education). [ABSTRACT FROM AUTHOR]

Time series prediction is one of the most challenging topics in real-world applications. Most studies undertook advanced mathematics and computer techniques above benchmark methods but often unable to guarantee forecasting models' generalization and reliability. This paper proposed a biologically motivated recurrent unit based on neuronal synaptic activity mechanism and chaotic behaviors in deep learning domain called Excitatory and Inhibitory Neural Synapse unit (EINS). The major contribution of this research is to create a flexible and robust recurrent unit based on neuroscience theory. It conducted experiments on three real-world time series forecast examples including finance, household power, weather for generalization and robustness evaluation using eight state-of-the-art recurrent architecture units as baseline models to compare convergence rate and forecasting accuracy. Experimental results showed that EINS achieved satisfactory performances in time series prediction reliability and effectiveness. [ABSTRACT FROM AUTHOR]

This paper develops a challenge to standard libertarian views that is based on an imagined neuroscientificdiscovery that is incompatible with satisfaction of a standard libertarian requirement for mainstream free decision making, and it explores potential libertarian responses to this discovery. The requirement at issue may beformulated as follows: In mainstream cases, an agent freely decided at t to A only if, given the past and the laws of nature, the agent was able right up to t to do something else intentionally at t than decide to A. The imagined discovery is about a point of no return for the making of any particular decision in a mainstream scenario. [ABSTRACT FROM AUTHOR]

This is an opinion paper about the strengths and weaknesses of Deep Nets for vision. They are at the heart of the enormous recent progress in artificial intelligence and are of growing importance in cognitive science and neuroscience. They have had many successes but also have several limitations and there is limited understanding of their inner workings. At present Deep Nets perform very well on specific visual tasks with benchmark datasets but they are much less general purpose, flexible, and adaptive than the human visual system. We argue that Deep Nets in their current form are unlikely to be able to overcome the fundamental problem of computer vision, namely how to deal with the combinatorial explosion, caused by the enormous complexity of natural images, and obtain the rich understanding of visual scenes that the human visual achieves. We argue that this combinatorial explosion takes us into a regime where "big data is not enough" and where we need to rethink our methods for benchmarking performance and evaluating vision algorithms. We stress that, as vision algorithms are increasingly used in real world applications, that performance evaluation is not merely an academic exercise but has important consequences in the real world. It is impractical to review the entire Deep Net literature so we restrict ourselves to a limited range of topics and references which are intended as entry points into the literature. The views expressed in this paper are our own and do not necessarily represent those of anybody else in the computer vision community. [ABSTRACT FROM AUTHOR]

The neuroscience of volition is an emerging subfield of the brain sciences, with hundreds of papers on the role of consciousness in action formation published each year. This makes the state-of-the-art in the discipline poorly accessible to newcomers and difficult to follow even for experts in the field. Here we provide a comprehensive summary of research in this field since its inception that will be useful to both groups. We also discuss important ideas that have received little coverage in the literature so far. We systematically reviewed a set of 2220 publications, with detailed consideration of almost 500 of the most relevant papers. We provide a thorough introduction to the seminal work of Benjamin Libet from the 1960s to 1980s. We also discuss common criticisms of Libet's method, including temporal introspection, the interpretation of the assumed physiological correlates of volition, and various conceptual issues. We conclude with recent advances and potential future directions in the field, highlighting modern methodological approaches to volition, as well as important recent findings. • We systematically reviewed 2220 papers related to the neuroscience of volition. • We detail Libet's work including the seminal "free will" experiments. • We discuss and categorize the extensive criticisms of Libet's experiments. • We present key philosophical issues relevant to the neuroscience of volition. • We examine contemporary techniques, methods, and findings related to volition. [ABSTRACT FROM AUTHOR]

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications. • The most groundbreaking advances in theoretical and applied Artificial Intelligence. • Deep Learning in real-world tasks, such as clinical diagnostics or robotics. • Several applications are presented, reviewed and discussed. • State-of-the-art in AI methods, models and applications. • New scientific discoveries successfully transferred to real-life applications. [ABSTRACT FROM AUTHOR]

There is a noticeable gap between results of cognitive neuroscientific research into basic mathematical abilities and philosophical and empirical investigations of mathematics as a distinct intellectual activity. The paper explores the relevance of a Wittgensteinian framework for dealing with this discrepancy. [ABSTRACT FROM AUTHOR]

Many scientific disciplines give rise to research published under the moniker of aesthetics. For instance, both psychology and neuroscience have highly active subfields focused on aesthetics research, known as empirical aesthetics and neuroaesthetics. However, it remains unclear what aesthetics is about, and, consequently, if aesthetics research pursued by different scientific disciplines addresses common problems. It is, therefore, difficult to assess how well aesthetics is doing as a scientific enterprise, identify and compare its main subfields, and quantify its productivity. To give an unbiased account of what counts as aesthetics across scientific disciplines, we conducted a bibliometric analysis of every publication found in Web of Science tagged as aesthetics. Spanning, 1970 to 2018, the retrieved literature comprised a total of 27,159 papers, 45,832 authors, and 123 countries. Visualization and bibliometric techniques were used to investigate the main research trends and subfields, growth of publications, citation analysis, and country productivity and collaborations. From 1970 to 2018, there was a clear increase in aesthetics research over time, with a stronger growth in recent years. The retrieved documents received a total of 217,931 citations, with a mean of 8.02 citations per document (SD = 25.7). Both a cluster analysis of the data, and a comparative analysis a posteriori, revealed that the aesthetics literature clusters into distinct research areas that differ significantly in their object of interest, research productivity and impact. This finding suggests that aesthetics is better thought of as a confederate of research traditions than a whole unified by common problems and research strategies. [ABSTRACT FROM AUTHOR]

This paper proposes the integration of the Neuroscience field in psychoanalytic psychotherapy training. This broadens the conceptual framework of psychoanalysis, enriching and contrasting theories, as well as psychopathology, clinic and the type of interventions, in light of current knowledge of the brain/mind relationship. Examples of such integration in the clinic are proposed. [ABSTRACT FROM AUTHOR]

This paper explores a series of thoughts about the meaning of intelligence in neuroscience and computer science. This work aims to present an understandable definition that fits our contemporary artificial intelligence background. The research methodology of this essay lies in existing theories of artificial intelligence, focused on computer science and neuroscience. I analyze the relationship between intelligence and neuroscience and Hawkin's Thousand Brains Theory, an approach to show what it is an intelligent agent according to neuroscience. Here, the main result relies on the verification that intelligence is only possible in the neocortex. According to this result, the study performs a second critical analysis aiming to demonstrate why there is no artificial intelligence today. [ABSTRACT FROM AUTHOR]

Background Neuromyths are prevalent in all spheres of life and can be found in all professions. The teaching profession is especially susceptible to neuromyths as teachers want to provide the most effective, science-based instruction to their students. Sometimes these instructions are not based on scientific studies but on a misinterpretation of scientific findings or neuromyths. The goal of the present paper was to examine the prevalence of seven popular neuromyths in teachers in Bosnia and Herzegovina (BIH). Material and methods The sample for this study comprised 300 teachers from all parts of BIH. The research material comprised 300 teachers (232 females and 62 males) from all parts of BIH. Participants were either personally invited to the study or recruited through an online survey sent to schools throughout BIH and teacher organizations. As a method we used the Questionnaire consisting of basic demographic information on the participants (gender, working experience, type of teacher) and 7 neuromyths statements on which participants were asked to answer whether they think the statement is true, not true, or they do not know. Results The results of this study indicate a wide prevalence of neuromyths in BIH teachers. The prevalence ranged from 17% to 82%. The most prevalent myth is about learning styles, while the least prevalent was that drinking less than 8 glasses of water causes a brain to shrink. There were statistically significant differences in the prevalence of neuromyths in relation to the teachers’ gender for every statement. However, the trend is not uniform. Although, overall the neuromyths were more prevalent in female teachers (for 5 items), for two neuromyth statements, male teachers had a higher prevalence. Additionally, the prevalence of neuromyths was more frequent in early-grade teachers. It should be noted that there are also differences in the results obtained for the different types of neuromyths. Conclusion Neuroscience is important for education and for the teachers. Thus, more attention should be given to the process of translating neuroscientific findings into useful facts for teachers. One way to improve this process is through the continual professional development of teachers in the field of neuroscience. [ABSTRACT FROM AUTHOR]

Recent advances in neuro-imaging enable scientists to create brain network data that can lead to novel insights into neurocircuitry, and a better understanding of the brain's organization. These networks inherently involve a spatial component, depicting which brain regions are structurally, functionally or genetically related. Their visualization in 3D suffers from occlusion and clutter, especially with increasing number of nodes and connections, while 2D representations such as connectograms, connectivity matrices, and node-link diagrams neglect the spatio-anatomical context. Approaches to arrange 2D-graphs manually are tedious, species-dependent, and require the knowledge of domain experts. In this paper, we present a spatial-data-driven approach for layouting 3D brain networks in 2D node-link diagrams, while maintaining their spatial organization. The produced graphs do not need manual positioning of nodes, are consistent (even for sub-graphs), and provide a perspective-dependent arrangement for orientation. Furthermore, we provide a visual design for highlighting anatomical context, including the shape of the brain, and the size of brain regions. We present in several case-studies the applicability of our approach for different neuroscience-relevant species, including the mouse, human, and Drosophila larvae. In a user study conducted with several domain experts, we demonstrate its relevance and validity, as well as its potential for neuroscientific publications, presentations, and education. [Display omitted] • A novel method for generating Spatial-Data-Driven Layouts for neural networks of multiple species and perspectives. • No need to manually define brain region related constraints to generate anatomically feasible layouts. • Visual designs providing a consistent spatial context to the user to ease orientation and visual comparison of different brain networks. [ABSTRACT FROM AUTHOR]

• Neurophysiological correlates of positive emotions contribute to wellbeing. • Brain networks that implement positive emotions are flexible and modifiable. • Developmental, social, and environmental factors impact positive emotions. • Meditation, contemplative practices, and flow cultivate positive emotions. • Linguistic dimensions contribute to advancing the neuroscience of positive emotions. This review paper provides an integrative account regarding neurophysiological correlates of positive emotions and affect that cumulatively contribute to the scaffolding for happiness and wellbeing in humans and other animals. This paper reviews the associations among neurotransmitters, hormones, brain networks, and cognitive functions in the context of positive emotions and affect. Consideration of lifespan developmental perspectives are incorporated, and we also examine the impact of healthy social relationships and environmental contexts on the modulation of positive emotions and affect. The neurophysiological processes that implement positive emotions are dynamic and modifiable, and meditative practices as well as flow states that change patterns of brain function and ultimately support wellbeing are also discussed. This review is part of "The Human Affectome Project" (http://neuroqualia.org/background.php), and in order to advance a primary aim of the Human Affectome Project, we also reviewed relevant linguistic dimensions and terminology that characterizes positive emotions and wellbeing. These linguistic dimensions are discussed within the context of the neuroscience literature with the overarching goal of generating novel recommendations for advancing neuroscience research on positive emotions and wellbeing. [ABSTRACT FROM AUTHOR]

Aim. The primary aim of this paper is to present and discuss the Apple-Tree Model of Emotion-Involved Processing in the context of foreign language learning with regard to didactic, neuroscientific, and psychological viewpoints. The proposed model mirrors theories which emphasize the role of emotional experiencing in the process of learning and relate it to the enhancement of cognitive processes. The secondary aim of this paper is to propose examples of application of these theories and recent research findings - with the use of videos in foreign language learning both inside and outside of the classes. Methods. Along with several others, two studies (an action research and experiment), dealing with the role of emotional engagement and positive emotional stimuli, are discussed in more detail. Concerning the action research (Kamenicka & Kovacikova, 2019), the subjects of this study were 31 non-native teenage secondary grammar school EFL learners (i.e., 17-18 years old). In this study, the role of emotional engagement in foreign language learning was observed - with regard to learners of various learning styles. Concerning the experiment (KraTova, Kamenicka, & Tirpakova, submitted for publication), it provided both quantitative and qualitative data. Its subjects were 82 non-native first-year university EFL students (i.e., 18-19 years old). The effect of application of positive emotional stimuli during the foreign language classes was observed - with regard to research participants' vocabulary retention, emotional experiencing, and their autonomic stress response. The qualitative data gathered dealt with research participants' subjective perception of emotionally competent stimuli applied, vocabulary recall, and experimental classes as such. Results. The analysis of the qualitative data suggests that emotional engagement leads to better motivation to learn among learners, including the reluctant ones. Moreover, the research outcomes suggest that emotionally competent stimuli might be considered as a universal key, when it comes to engagement of learners of various learning styles. Furthermore, the qualitative data analysis also indicates that learners especially appreciate learning the foreign language through topics which are not usually part of school curriculum. The statistical data analysis indicates that positive emotional experiences lead to the increase of both foreign language enjoyment and learners' foreign language vocabulary retention. Conclusions. The research findings suggest that positive emotional stimulation of FL learners might lead to their increased motivation for learning as well as to better retention of new language items. Furthermore, they also confirm the significant role of the teacher in terms of regulation and responsibility for the learning atmosphere. As nowadays, the FL learning does not take place in classes only, the teachers' managerial guidance and direction of learners becomes essential more than ever. With regard to these findings, several ideas how to guide learners' FL learning both inside and outside of class, are proposed - with the use of videos, which are staggeringly omnipresent in everyday lives of nowadays' society. [ABSTRACT FROM AUTHOR]

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general. [ABSTRACT FROM AUTHOR]

Calcium imaging data promises to transform the field of neuroscience by making it possible to record from large populations of neurons simultaneously. However, determining the exact moment in time at which a neuron spikes, from a calcium imaging data set, amounts to a non-trivial deconvolution problem which is of critical importance for downstream analyses. While a number of formulations have been proposed for this task in the recent literature, in this article, we focus on a formulation recently proposed in Jewell and Witten (2018. Exact spike train inference via $\ell_{0} $ optimization. The Annals of Applied Statistics12(4), 2457-2482) that can accurately estimate not just the spike rate, but also the specific times at which the neuron spikes. We develop a much faster algorithm that can be used to deconvolve a fluorescence trace of 100 000 timesteps in less than a second. Furthermore, we present a modification to this algorithm that precludes the possibility of a "negative spike". We demonstrate the performance of this algorithm for spike deconvolution on calcium imaging datasets that were recently released as part of the $\texttt{spikefinder}$ challenge (http://spikefinder.codeneuro.org/). The algorithm presented in this article was used in the Allen Institute for Brain Science's "platform paper" to decode neural activity from the Allen Brain Observatory; this is the main scientific paper in which their data resource is presented. Our $\texttt{C++}$ implementation, along with $\texttt{R}$ and $\texttt{python}$ wrappers, is publicly available. $\texttt{R}$ code is available on $\texttt{CRAN}$ and $\texttt{Github}$, and $\texttt{python}$ wrappers are available on $\texttt{Github}$; see https://github.com/jewellsean/FastLZeroSpikeInference. [ABSTRACT FROM AUTHOR]

In this paper we begin to explore how knowledges being generated in bioscience might be brought into productive articulation with the Sociology of Education, considering the potential for emerging transdisciplinary, 'biosocial' approaches to enable new ways of researching and understanding pressing educational issues. In this paper, as in our current research, we take learning as our focus. Our work brings together collaborators from across fields: sociology of education; molecular biology and biochemistry; cognitive neuroscience; fMRI imaging; and EEG. Through the paper we explore the generative potential of an encounter between life sciences and sociology of education. Through consideration of the conceptual and methodological elements of our 'Synchrony in Learning' research and engagement with our pilot experimental approach, our research is suggesting that our central concept, learning, is undergoing metamorphosis, challenging us to understand learning as a phenomenon produced through the intra-action of a multiplicity of forces and processes. [ABSTRACT FROM AUTHOR]

Despite rapid advances in sensor development and technological miniaturization, it remains challenging to non-invasively record small-amplitude electrophysiological signals from an animal in its natural environment. Many advances in ecophysiology and biologging have arisen through sleep studies, which rely on detecting small signals over multiple days and minimal disruption of natural animal behavior. This paper describes the development of a surface-mounted system that has allowed novel electrophysiological recordings of sleep in wild marine mammals. We discuss our iterative design process by providing sensor-comparison data, detailed technical illustrations, and material recommendations. We describe the system's performance over multiple days in 12 freely moving northern elephant seals (Mirounga angustirostris) sleeping on land and in water in captivity and the wild. We leverage advances in signal processing by applying independent components analysis and inertial motion sensor calibrations to maximize signal quality across large (> 10 gigabyte), multi-day datasets. Our study adds to the suite of biologging tools available to scientists seeking to understand the physiology and behavior of wild animals in the context in which they evolved. [ABSTRACT FROM AUTHOR]

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work. [ABSTRACT FROM AUTHOR]

Motor learning generates synaptic neural connections that favour the motor environment and, also, various processes where our cognitive and executive functions intervene. Therefore, it is essential to know the different contributions that come from neuroscience linked to motor learning in a child. This study aimed to determine the fundamental contributions of neuroscience to motor learning in children. The methodology included a qualitative systematic review in the PubMed, Medline and Scopus databases. Of 479 related documents, 24 papers achieved the inclusion criteria (the learning mechanisms of motor skills and the different approaches to achieving meaningful learning). They were selected using the data collection methodology indicated by PRISMA®. The main results indicated that learning occurs based on experiences (cognitive, perceptual, motor, linguistic, neuronal, organic and cultural) and requires processes of adaptation, stabilization, and maturation of brain synchronization of vestibular, perceptual and visual processes. Children who receive motor intervention improve sustained attention, working memory, problem-solving and planning capacity. Motor and cognitive development are favoured by instructions as an essential tool. The implicit instructions present a higher benefit for children with lower motor skills. [ABSTRACT FROM AUTHOR]

Integrative psychotherapeutic practice is the process of creating a broad, overarching framework as a guide to selecting and combining concepts and interventions. Attracting attention over the past 20 years, it has been identified as the dominant trend in family therapy moving into the 21st century. It is no surprise that clinicians necessarily combine several theories, techniques, and factors in their work given the multidimensional nature of work with clients and families. Integrative practice has evolved over time with different models and frameworks proposed, and yet in the field, there are concerns that it is not widely taught despite many identifying as eclectic or integrative. The purpose of this paper is twofold. Firstly, it reviews the topic of integrative practice, its history, evolution, strengths and pitfalls, and models. Secondly, a systemic meta‐framework is proposed as a guide to clinical practice and teaching. The review found a move from eclecticism to integrative practice, with the evolution of integration leading to five primary methods. The proposed QIFT Systemic Meta‐Framework for Integrative Practice both fits within and extends these methods, with the following key elements: way of being, therapeutic alliance with a focus on neurobiology, systemic assessment and formulation, and decision‐making highlighting theory of change. [ABSTRACT FROM AUTHOR]

This paper builds upon previous cybernetics scholarship to explore the neuroscience in The Crying of Lot 49, focusing on the neuronal action potential. In 1952, the cybernetics-influenced action potential was elucidated, a process which decreases entropy through feedback loops to transmit information. Lot 49 exemplifies what happens when the action potential stops properly communicating. This communication failure occurs because the clues that the protagonist Oedipa associates with the Tristero organization increase information entropy, which breaks her neuronal feedback loops and subsequently increases the thermodynamic and informatic entropy of her nervous system. Oedipa drinks alcohol in an attempt to combat the Tristero's entropic effects on herself, but the attempt backfires as the entropy of her nervous system continues to increase and culminates in seizures. Oedipa believes her seizures are divine interventions that provide energy despite the fact that they derive from all the Tristero information saturating her. This misguided belief overtakes Oedipa because the type of epilepsy she suffers from is temporal lobe epilepsy, whose seizures symptomatically impart consciousness loss, amnesia, and religious hallucinations to mask their entropy. Consequently, Lot 49 captures the blossoming of modern neuroscience from cybernetics in the mid-20th century by intertwining neuroscience with its themes of entropy. [ABSTRACT FROM AUTHOR]

Although we are accustomed to thinking about technology as involving things—objects and processes—derived from scientific discoveries, science also creates a technology of ideas, ways of thinking both about the world and about human beings. And unlike "thing technology," "idea technology" can have powerful effects even when the ideas are false. This paper discusses false idea technology, or ideology, and suggests mechanisms by which it can have effects on both individuals and societies. It discusses neuroscience as the "next frontier" of ideology that may change our conceptions of human nature. [ABSTRACT FROM AUTHOR]

Prof B Ramamurthi was a pioneer of Indian neurosurgery and a major force in the development of Indian neuroscience. Founding the Madras Institute of Neurology and later the A Lakshmipathi Neurosurgical Centre (ALNC), both at Madras (or Chennai as it is now called), he developed centres of excellence in his career that spanned over five decades. During this period of time he made Madras, a destination for neurosurgery and neuroscience. Along with his colleagues a large number of publications were produced which influenced the world. Notable among his contributions were those in Stereotaxy for movement disorders, epilepsy, pain and psychiatric illness. He also had notable contributions in brain tumours especially acoustic neurinomas and pituitary tumours. His papers on the low incidence of aneurysms is still quoted widely. Head injuries formed a major part of the neurosurgical work and major contributions were made in that field too. As a developing country with socio-economic issues, infections of the nervous system were seen commonly. His publications on tuberculomas of the brain are noteworthy. He was intrigued by the neurophysiological basis of consciousness. He writings on the subject reflect his attempt to bring together ancient eastern thoughts and concepts of consciousness and life and western science. In the later part of his career he spoke on ethics in and the changing milieu of neurosurgery. While contributions to spinal surgery were not seen in the first half of his career, he along with his colleagues from ALNC published original articles on spinal surgery especially tumours and OPLL. Prof B Ramamurthi, has not only influenced, taught and mentored, during his lifetime, a great many neuroscientists, but he also continues to do so through his publications which continue to be relevant in todays world. A glimpse into his contributions show us how without the technology of today a lot was achieved - and we need to see that, to inspire us to achieve more and to strive for greater heights. [ABSTRACT FROM AUTHOR]

Despite the recent success of neural network models in mimicking animal performance on various tasks, critics worry that these models fail to illuminate brain function. We take it that a central approach to explanation in systems neuroscience is that of mechanistic modeling, where understanding the system requires us to characterize its parts, organization, and activities, and how those give rise to behaviors of interest. However, it remains controversial what it takes for a model to be mechanistic, and whether computational models such as neural networks qualify as explanatory on this approach. We argue that certain kinds of neural network models are actually good examples of mechanistic models, when an appropriate notion of mechanistic mapping is deployed. Building on existing work on model-to-mechanism mapping (3M), we describe criteria delineating such a notion, which we call 3M++. These criteria require us, first, to identify an abstract level of description that is still detailed enough to be "runnable", and then, to construct model-to-brain mappings using the same principles as those employed for brain-to-brain mapping across individuals. Perhaps surprisingly, the abstractions required are just those already in use in experimental neuroscience and deployed in the construction of more familiar computational models — just as the principles of inter-brain mappings are very much in the spirit of those already employed in the collection and analysis of data across animals. In a companion paper, we address the relationship between optimization and intelligibility, in the context of functional evolutionary explanations. Taken together, mechanistic interpretations of computational models and the dependencies between form and function illuminated by optimization processes can help us to understand why brain systems are built they way they are. [ABSTRACT FROM AUTHOR]

In the last twenty years or so neuroscience and linguistics, language and culture have become increasingly interconnected, as we seek to understand the influence of language on behaviour and the way we think and express ourselves. Neuroscience is the study of how the brain and mind work and cognitive science applies that study to how we use language to express our thoughts and feelings and also how our mental faculties work. One of these faculties is memory. How do we recall and express what has happened in the past and even assuming we can remember it at all? The purpose of this paper is to examine how cognitive science has clarified the relationship between language and culture in particular in the way we remember things and express our recollections, using experiments based on facial recognition. In the process the paper demonstrates how language and culture influence our cultural processes and the importance of cognitive science as a part of language and cultural study. [ABSTRACT FROM AUTHOR]

In this paper, we reexamined the typical analysis methods of a visuomotor sequence learning task, namely the ASRT task (J. H. Howard & Howard, 1997). We pointed out that the current analysis of data could be improved by paying more attention to pre-existing biases (i.e. by eliminating artifacts by using new filters) and by introducing a new data grouping that is more in line with the task’s inherent statistical structure. These suggestions result in more types of learning scores that can be quantified and also in purer measures. Importantly, the filtering method proposed in this paper also results in higher individual variability, possibly indicating that it had been masked previously with the usual methods. The implications of our findings relate to other sequence learning tasks as well, and opens up opportunities to study different types of implicit learning phenomena. [ABSTRACT FROM AUTHOR]

Personal motivation is a key factor in the service of foster care, impending both on the welfare of the child and on the satisfaction of the carer. This paper explores the benefits, challenges and dilemmas involved in the job of professional (i.e. state-supported) foster carer in Romania–a country where the issue of child protection has drawn a great deal of international attention over the last thirty years. The principal hypothesis concerns whether the benefits, challenges and dilemmas identified by foster carers are influenced by the factors that led to their taking up this kind of work. Quantitative research was conducted using a questionnaire as the main tool. The paper takes a descriptive, cross-sectional and multifactorial approach. Sampling was carried out by self-selecting method, and the study involved 51 participants. The research project identified a statistically significant variation in the challenges and dilemmas reported by foster carers. Thus, the results of the study show that the majority of the carers indicate a primarily intrinsic motivation for their work. By way of conclusion, it is argued that social workers, operating in collaboration with multidisciplinary teams, can offer carers support in managing more difficult periods in the child–carer relationship, thus enhancing the sustainability of the foster care service. [ABSTRACT FROM AUTHOR]

The interaction between phenotypic plasticity, e.g. learning, and evolution is an important topic both in Evolutionary Biology and Machine Learning. The evolution of learning is commonly studied in Evolutionary Biology, while the use of an evolutionary process to improve learning is of interest to the field of Machine Learning. This paper takes a different point of view by studying the effect of learning on the evolutionary process, the so-called Baldwin effect. A well-studied result in the literature about the Baldwin effect is that learning affects the speed of convergence of the evolutionary process towards some genetic configuration, which corresponds to the environment-induced plastic response. This paper demonstrates that learning can change the outcome of evolution, i.e., lead to a genetic configuration that does not correspond to the plastic response. Results are obtained both analytically and experimentally by means of an agent-based model of a foraging task, in an environment where the distribution of resources follows seasonal cycles and the foraging success on different resource types is conditioned by trade-offs that can be evolved and learned. This paper attempts to answer a question that has been overlooked: whether learning has an effect on what genotypic traits are evolved, i.e. the selection of a trait that enables a plastic response changes the selection pressure on a different trait, in what could be described as co-evolution between different traits in the same genome. [ABSTRACT FROM AUTHOR]

Past research on the process of extinguishing a fire typically used a traditional linear water jet falling point model and the results ignored external factors, such as environmental conditions and the status of the fire engine, even though the water jet falling point location prediction was often associated with these parameters and showed a nonlinear relationship. This paper constructed a BP (Back Propagation) neural network model. The fire gun nozzle characteristics were included as model inputs, and the water discharge point coordinates were the model outputs; thus, the model could precisely predict the water discharge point with small error and high precision to determine an accurate firing position and allow for the timely adjustment of the spray gun. To improve the slow convergence and local optimality problems of the BP neural network (BPNN), this paper further used a genetic algorithm to optimize the BPNN (GA-BPNN). The BPNN can be used to optimize the weights in the network to train them for global optimization. A genetic algorithm was introduced into the neural network approach, and the water jet landing prediction model was further improved. The simulation results showed that the prediction accuracy of the GA-BP model was better than that of the BPNN alone. The established model can accurately predict the location of the water jet, making the prediction results more useful for firefighters. [ABSTRACT FROM AUTHOR]

• The present paper hypothesizes how research in personality psychology will change with the recent emergence of the Internet of Things (IoT). In a society, where humankind is digitally interconnected and all machines in one's own household or at work are linked to the Internet, manifold data from human-machine-interaction can be used to predict psychological traits and states. • In the present theoretical paper, we first explain why it is (still) important to study personality and we also give a short (historical) overview on different theoretical and methodological approaches to study personality. Second, this work will focus on a recent addition to the toolbox of a personality psychologist, namely digital phenotyping via methods from Psychoinformatics , which falls into the realm of the aforementioned IoT. Aside from an introduction into these new concepts, we discuss strengths and limitations of this new data layer of interest for personality psychologists. • Finally, it is argued that in these modern times personality psychology research is conducted in many scattered scientific areas with researchers sometimes unaware of each other. Therefore, tremendous collaborative efforts need to be invested in order to reunify the psychological/neuroscientific oriented personality science to ultimately understand why we are the creatures we are. [ABSTRACT FROM AUTHOR]

This paper presents a data-driven model identification strategy that characterizes the behavior of a general dynamical system relative to a set of limit cycles that emerge in response to periodic forcing. Using time series data to infer the phase–amplitude dynamics associated with the underlying forced limit cycles, a low-order model can be obtained that accurately captures the dynamical behavior in response to arbitrary external inputs. The proposed strategy can be readily implemented in situations where full state measurements are unavailable and does not require any prior knowledge of the underlying model equations. This technique is applied to a model of coupled planar oscillators and to a model that considers the spike rate in a population of coupled conductance-based neurons where it outperforms two other commonly used data-driven model identification techniques. • Forcing-induced limit cycles are considered in the context of data-driven model identification for dynamical systems. • A general procedure is developed for the proposed model identification strategy. • The proposed approach does not require a priori knowledge about the underlying dynamical system. • Direct comparisons are provided between other well-established model-identification strategies. [ABSTRACT FROM AUTHOR]