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The philosophical literature on scientific explanation in neuroscience has been dominated by the idea of mechanisms. The mechanist philosophers often claim that neuroscience is in the business of finding mechanisms. This view has been challenged in numerous ways by showing that there are other successful and widespread explanatory strategies in neuroscience. However, the empirical evidence for all these claims was hitherto lacking. Empirical evidence about the pervasiveness and uses of various explanatory strategies in neuroscience is particularly needed because examples and case studies that are used to illustrate philosophical claims so far tend to be hand-picked. The risk of confirmation bias is therefore considerable: when looking for white swans, all one finds is that swans are white. The more systematic quantitative and qualitative bibliometric study of a large body of relevant literature that we present in this paper can put such claims into perspective. Using text mining tools, we identify the typical linguistic patterns used in the alleged mechanistic, dynamical, and topological explanations in the literature, their preponderance and how they change over time. Our findings show abundant use of mechanistic language, but also the presence of a significant neuroscience literature using topological and dynamical explanatory language, which grows over time and increasingly differentiates from each other and from mechanistic explanations. [ABSTRACT FROM AUTHOR]

This paper presents arguments and evidence from psychology and neuroscience supporting Lipton's 2004 claim that scientists create knowledge through an abductive process that he calls "Inference to the Best Explanation". The paper develops two conclusions. Conclusion 1 is that without conscious effort on our part, our brains use a process very similar to abduction as a powerful way of interpreting sensory information. To support Conclusion 1, evidence from psychology and neuroscience is presented that suggests that what we humans perceive through our senses is not reality, but rather, our 'brain's "best guess" of the causes of its sensory input. The implication of this best guessing is that our brains use a process very similar to abduction throughout our lives to inform us of what is happening in the world around us. In addition, an argument based on Darwinian evolution is presented claiming that our brains do an excellent job of interpreting sensory information from the outside world. (If they did not, we, as a species, could hardly have survived.) Combining these two claims leads to Conclusion 1. Building on Conclusion 1, Conclusion 2 is that Lipton and others are correct in claiming that scientists use abduction when creating scientific theories. Abduction must be strong, because Nature chose abduction for its own sensemaking purposes. This paper's contribution to knowledge is in pointing out that recent psychological and neuroscientific research has major implications for the philosophical world's confidence in the probable validity of abductive inference. The punchline is simple: Nature chose abduction! [ABSTRACT FROM AUTHOR]

In a huge collaborative effort, millions of cells in the mouse brain have been mapped in detail. Two scientists examine the resulting wealth of insights into gene regulation in brain cells, neuronal connections and how our own brains evolved.Transcriptomic and epigenomic data from millions of mouse brain cells. [ABSTRACT FROM AUTHOR]

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document. [ABSTRACT FROM AUTHOR]

In this paper, I examined whether evidence from the neuroscience of morality supports the standard models of phronesis, i.e., Jubilee and Aretai Centre Models. The standard models explain phronesis as a multifaceted construct based on interaction and coordination among functional components. I reviewed recent neuroscience studies focusing on brain networks associated with morality and their connectivity to examine the validity of the models. Simultaneously, I discussed whether the evidence helps the models address challenges, particularly those from the phronesis eliminativism. Neuroscientific evidence supported the importance of brain networks, i.e., the default mode, salience, and central executive functioning networks, in moral functioning in general. The findings favorably supported the multifaceted and integrative nature of phronesis proposed by the standard models. Finally, I considered how the two models could explain the mechanisms of phronesis more integratively based on neuroscientific findings. At the end of this paper, with the evidence, I proposed several practical ideas to promote the cultivation of phronesis, e.g., the consideration of coordination among components for moral functioning and the use of moral exemplars. [ABSTRACT FROM AUTHOR]

Background: This paper discusses a paradox in mental health. It manifests as a relationship between adverse "bad" effects (suffering, clinical costs, loss of productivity) in individuals and populations and advantageous "good" aspects of mental disorders. These beneficial aspects (scientific, artistic and political accomplishments) emanate at the societal level through the frequently unprecedented creativity of people suffering from mental disorders and their relatives. Such gains can contribute to societal innovation and problem-solving. Especially in times of accelerated social-ecological change, approaches are needed that facilitate best-possible mental health care but also recognize creative ideas conducive to beneficial clinical and social-ecological innovations as soon as possible. Discussion: This paper emphasizes the need to account for creativity as a crucial component in evolving mental health systems and societies. It highlights the need for wide-ranging approaches and discusses how research targeting multiple facets (e.g., brain level, cognitive neuroscience, psychiatry, neurology, socio-cultural, economic and other factors) might further our understanding of the creativity-mental disorder link and its importance for innovating mental health systems and societies. Conclusion: Our discussion clarifies that considerable research will be needed to obtain a better understanding of how creativity associated with mental disorders may help to create more sustainable societies on a fast-changing planet through innovative ideas. Given the current-state-of-the-art of research and healthcare management, our discussion is currently speculative. However, it provides a basis for how pros and cons might be studied in the future through transdisciplinary research and collaborations across sectors of society. [ABSTRACT FROM AUTHOR]

Organizations that embrace innovative business strategies position themselves well for success. In this evolving technological landscape, businesses are increasingly focused on understanding consumer behaviors and preferences to tailor their products and services effectively. Notably, integrating neuroscience has become pivotal in achieving these goals, requiring its incorporation throughout various business processes. This comprehensive research review conducts a meta-analysis, synthesizing findings from numerous studies on applying neuroscience across diverse business domains. The primary objective is to identify innovative neuroscience approaches that can significantly revolutionize businesses. Through a meticulous literature exploration using PRISMA analysis, we discerned broad themes and further refined them into critical sub-themes. This thematic categorization enables a focused examination of each area of business research. The findings in this study uncovered vital intersections between neuroscience and business, offering valuable insights for corporations and researchers. In essence, the integration of neuroscientific techniques and artificial intelligence (AI)-based softwares into business practices is no longer optional, but a necessity for businesses to stay competitive in today's fast-paced world. This study, therefore, plays a crucial role in connecting the state-of-the-art tools and techniques in neuroscience and traditional business practices. It paves the way for a new era of business operations, where science and technology work hand in hand with commerce to create a more prosperous and sustainable future. In conclusion, the paper emphasizes the importance of incorporating neuroscience techniques and newly developed AI-based softwares into business practices. This integration can yield precise and favorable outcomes, offering businesses the tools to thrive in an ever-changing market environment. [ABSTRACT FROM AUTHOR]

Morphometry is fundamental for studying and correlating neuronal morphology with brain functions. With increasing computational power, it is possible to extract morphometric characteristics automatically, including features such as length, volume, and number of neuron branches. However, to the best of our knowledge, there is no mapping of morphometric tools yet. In this context, we conducted a systematic search and review to identify and analyze tools within the scope of neuron analysis. Thus, the work followed a well-defined protocol and sought to answer the following research questions: What open-source tools are available for neuronal morphometric analysis? What morphometric characteristics are extracted by these tools? For this, aiming for greater robustness and coverage, the study was based on the paper analysis as well as the study of documentation and tests with the tools available in repositories. We analyzed 1,586 papers and mapped 23 tools, where NeuroM, L-Measure, and NeuroMorphoVis extract the most features. Furthermore, we contribute to the body of knowledge with the unprecedented presentation of 150 unique morphometric features whose terminologies were categorized and standardized. Overall, the study contributes to advancing the understanding of the complex mechanisms underlying the brain. [ABSTRACT FROM AUTHOR]

Brain tumors are one of the leading causes of death worldwide. Different types of brain tumors are known, so the choice of treatment depends directly on the type of tumor. The classification of brain tumors is very important as a complex and challenging problem in the field of image processing. Today, deep learning methods are used to classify brain tumors. In addition to being able to detect and automatically classify all types of brain tumors, these methods significantly reduce the diagnosis time and increase accuracy. In this paper, a deep learning-based model is proposed to classify brain tumors into three classes: glioma, meningioma, and pituitary tumor. In the first phase, the pre-trained network ResNet50 is used to extract features from MRI images. In the second phase, by proposing two attention mechanisms (depth-separable convolution-based channel attention mechanism and an innovative multi-head-attention mechanism), the most effective spatial and channel features are extracted and integrated. Finally, the classification phase is performed. Evaluations on the Figshare dataset showed an accuracy of 99.32%, which performs better than existing models. Therefore, the proposed model can accurately classify brain tumors and help neurologists and physicians make accurate diagnostic decisions. [ABSTRACT FROM AUTHOR]

Three studies of the human dopamine transporter reveal how it binds to molecules such as dopamine and cocaine. Dopamine imbalances underlie some brain conditions and these data will aid targeted drug design.Insights into how molecules bind to the human dopamine transporter. [ABSTRACT FROM AUTHOR]

In this paper, findings from research in neuroscience of morality will be reviewed to consider the purposes of moral education. Particularly, I will focus on two main themes in neuroscience, novel neuroimaging and experimental investigations, and Bayesian learning mechanism. First, I will examine how neuroimaging and experimental studies contributed to our understanding of psychological mechanisms associated with moral functioning while addressing methodological concerns. Second, Bayesian learning mechanism will be introduced to acquire insights about how moral learning occurs in human brains. Based on the reviewed neuroscientific research on morality, I will examine how evidence can support the model of moral education proposed by virtue ethics, Neo-Aristotelian moral philosophy in particular. Particularly, two main aims of virtue ethics-based moral education, the habituation of virtues and the cultivation of phronesis, will be discussed as the important purposes of moral education based on neuroscientific evidence. [ABSTRACT FROM AUTHOR]

Naturalistic understandings that frame human experiences and differences as biological dysfunctions have been identified as a key source of epistemic injustice. Critics argue that those understandings are epistemically harmful because they obscure social factors that might be involved in people’s suffering; therefore, naturalistic understandings should be undermined. But those critics have overlooked the epistemic benefits such understandings can offer marginalised individuals. In this paper, I argue that the capacity of naturalistic understandings to obscure social factors does not necessarily cause epistemic injustice and can even help people to avoid some epistemic injustice. I do this by considering how some individuals with bipolar disorder deploy the neurobiological understanding of their disorder, highlighting three functions it fills for them: explanation, disclamation, and decontestation. In performing these functions, the neurobiological understanding does marginalise alternative, social perspectives on bipolar disorder. However, this can be understood as a feature rather than a bug. By marginalising alternative explanations, the neurobiological understanding can help individuals with bipolar disorder resist epistemic injustice, including, for example, the trivialisation of their experiences. Given this, critics seeking to undermine naturalistic understandings of mental disorder and other experiences in the pursuit of epistemic justice themselves risk exacerbating epistemic injustice. [ABSTRACT FROM AUTHOR]

This paper analyses recent calls for so called “neurorights”, suggested novel human rights whose adoption is allegedly required because of advances in neuroscience, exemplified by a proposal of the Neurorights Initiative. Advances in neuroscience and technology are indeed impressive and pose a range of challenges for the law, and some novel applications give grounds for human rights concerns. But whether addressing these concerns requires adopting novel human rights, and whether the proposed neurorights are suitable candidates, are a different matter. This paper argues that the proposed rights, as individuals and a class, should not be adopted and lobbying on their behalf should stop. The proposal tends to promote rights inflationism, is tainted by neuroexceptionalism and neuroessentialism, and lacks grounding in relevant scholarship. None of the proposed individual rights passes quality criteria debated in the field. While understandable from a moral perspective, the proposal is fundamentally flawed from a legal perspective. Rather than conjuring up novel human rights, existing rights should be further developed in face of changing societal circumstances and technological possibilities. [ABSTRACT FROM AUTHOR]

This article explores the wide-ranging impact of inviting architecture students to increase conscious somatic awareness of their body and function, and the effects on their design process. This paper analyses the results of a Movement Awareness Intervention conducted prior to students undertaking their usual architectural design studio at the Queensland University of Technology, Brisbane, Australia. The impact of a global trend in extensive use of digital design tools has resulted in impacts on components in the architecture design process that appear to be slipping under the radar in architecture education and practice. This article addresses issues raised by Juhani Pallasmaa and Paolo Belardi, bolstered by neuroscience research by Harry Mallgrave and colleagues, to explore the impact of heightened conscious bodily awareness upon the cognitive design thinking processes required in the architecture. The case study research using mixed data collection methods on 39 participants draws upon surveys, hand notations, and audio interviews to track participant perceptions of the impact on their design process in action. Analysis of the data produced in this study addresses the concerns raised by theses seminal writers. This paper argues for new approaches to reconfiguring and recalibrating of emotional, cognitive, and physical stimulants that were once invisibly physically embedded in the traditional architectural design process to work with digital design technology. [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]

Twin studies yield valuable insights into the sources of variation, covariation and causation in human traits. The ABCD Study® (abcdstudy.org) was designed to take advantage of four universities known for their twin research, neuroimaging, population-based sampling, and expertise in genetic epidemiology so that representative twin studies could be performed. In this paper we use the twin data to: (i) provide initial estimates of heritability for the wide range of phenotypes assessed in the ABCD Study using a consistent direct variance estimation approach, assuring that both data and methodology are sound; and (ii) provide an online resource for researchers that can serve as a reference point for future behavior genetic studies of this publicly available dataset. Data were analyzed from 772 pairs of twins aged 9–10 years at study inception, with zygosity determined using genotypic data, recruited and assessed at four twin hub sites. The online tool provides twin correlations and both standardized and unstandardized estimates of additive genetic, and environmental variation for 14,500 continuously distributed phenotypic features, including: structural and functional neuroimaging, neurocognition, personality, psychopathology, substance use propensity, physical, and environmental trait variables. The estimates were obtained using an unconstrained variance approach, so they can be incorporated directly into meta-analyses without upwardly biasing aggregate estimates. The results indicated broad consistency with prior literature where available and provided novel estimates for phenotypes without prior twin studies or those assessed at different ages. Effects of site, self-identified race/ethnicity, age and sex were statistically controlled. Results from genetic modeling of all 53,172 continuous variables, including 38,672 functional MRI variables, will be accessible via the user-friendly open-access web interface we have established, and will be updated as new data are released from the ABCD Study. This paper provides an overview of the initial results from the twin study embedded within the ABCD Study, an introduction to the primary research domains in the ABCD study and twin methodology, and an evaluation of the initial findings with a focus on data quality and suitability for future behavior genetic studies using the ABCD dataset. The broad introductory material is provided in recognition of the multidisciplinary appeal of the ABCD Study. While this paper focuses on univariate analyses, we emphasize the opportunities for multivariate, developmental and causal analyses, as well as those evaluating heterogeneity by key moderators such as sex, demographic factors and genetic background. [ABSTRACT FROM AUTHOR]

In a 2004 paper, Greene and Cohen predicted that neuroscience would revolutionise criminal justice by presenting a mechanistic view of human agency that would change people's intuitions about retributive punishment. According to their theory, this change in intuitions would in turn lead to the demise of retributivism within criminal justice systems. Their influential paper has been challenged, most notably by Morse, who has argued that it is unlikely that there will be major changes to criminal justice systems in response to neuroscience. In this paper we commence a tentative empirical enquiry into the claims of these theorists, focusing on Australian criminal justice. Our analysis of Australian cases is not supportive of claims about the demise of retributive justice, and instead suggests the possibility that neuroscience may be used by the courts to calibrate retributive desert. It is thus more consistent with the predictive claims of Morse than of Greene and Cohen. We also consider evidence derived from interviews with judges, and this leads us to consider the possibility of a backlash against evidence of brain impairment. Finally we note that change in penal aims may be occurring that is unrelated to developments in neuroscience. [ABSTRACT FROM AUTHOR]

Understanding the complex connectivity structure of the brain is a major challenge in neuroscience. Vast and ever-expanding literature about neuronal connectivity between brain regions already exists in published research articles and databases. However, with the ever-expanding increase in published articles and repositories, it becomes difficult for a neuroscientist to engage with the breadth and depth of any given field within neuroscience. Natural Language Processing (NLP) techniques can be used to mine 'Brain Region Connectivity' information from published articles to build a centralized connectivity resource helping neuroscience researchers to gain quick access to research findings. Manually curating and continuously updating such a resource involves significant time and effort. This paper presents an application of supervised machine learning algorithms that perform shallow and deep linguistic analysis of text to automatically extract connectivity between brain region mentions. Our proposed algorithms are evaluated using benchmark datasets collated from PubMed and our own dataset of full text articles annotated by a domain expert. We also present a comparison with state-of-the-art methods including BioBERT. Proposed methods achieve best recall and F 2 scores negating the need for any domain-specific predefined linguistic patterns. Our paper presents a novel effort towards automatically generating interpretable patterns of connectivity for extracting connected brain region mentions from text and can be expanded to include any other domain-specific information. [ABSTRACT FROM AUTHOR]

In this work, we detailed the improvement of a hand-made screen-printed electrode (SPE), which has easy production with conductive ink. The preparation of the hand-made SPE based on conductive ink to be transferred to cardboard substrates as electrochemical sensors. Thanks to its advantages such as being cheap and easy to obtain, cardboard was preferred as a substrate. As a new approach, we report for the first time in the literature the alkaline pretreatment applied for the cardboard substrate. The conductive ink was used as a working electrode, a counter electrode, and a reference electrode. The components of the developed conductive ink are carboxymethyl cellulose (CMC) as binder, water:ethanol:ethylene glycol as solvent and graphite as conductive material. The SPE was characterized by Fourier-transformed infrared spectroscopy, scanning electron microscopy energy-dispersive X-ray spectroscopy, cyclic voltammetry, and electrochemical impedance spectroscopy. The developed electrode was used in the voltammetric determination of dopamine, an important neurotransmitter for human health. The device achieved a linear response between 5.0 and 1000 μM and a limit of detection (LOD) of 1.25 μM. Thus, these developed hand-made SPE was used in the determination of dopamine in a selective, fast, and cost-effective way. It provides a new device approach for electrochemical biosensors and wearable technologies with hand-made screen-printed electrodes developed as easy to prepare, inexpensive and disposable. [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 paper presents a scoping review of empirical studies on the incorporation of neuroscience into interventions for survivors of intimate partner violence. Using Arksey and O'Malley's framework for scoping reviews, 13 articles were collected for this review. The results showed an overall paucity of research incorporating neuroscience into intimate partner violence interventions. The majority of the studies were conducted in North America, split between various fields of medicine, nursing, psychology, and social work, and were all published between 2013 and 2020, showing the timeliness of this topic. A majority of the studies used quantitative design, with fewer using qualitative or mixed methods design. While some articles described fully integrating neuroscience into study design, most articles highlighted its significance and called for a further examination of the role of neuroscience in this field. This paper offers suggestions for the inclusion of neuroscience into social work practice with those impacted by intimate partner violence. [ABSTRACT FROM AUTHOR]

The field is taking a leap forward thanks to innovative technologies, such as artificial intelligence. Researchers must improve consent procedures and public involvement. [ABSTRACT FROM AUTHOR]

The good regulator theorem and the internal model principle are sometimes cited as mathematical proofs that an agent needs an internal model of the world in order to have an optimal policy. However, these principles rely on a definition of “internal model” that is far too permissive, applying even to cases of systems that do not use an internal model. As a result, these principles do not provide evidence (let alone a proof) that internal models are necessary. The paper also diagnoses what is missing in the GRT and IMP definitions of internal model, which is that models need to make predictions that represent variables in the target system (and these representations need to be usable by an agent so as to guide behavior). [ABSTRACT FROM AUTHOR]

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes. [ABSTRACT FROM AUTHOR]

While research in specific academic disciplines has individually advanced knowledge and practice for promoting multiple aspects of health and well-being in children and adolescents, still missing is an understanding of the interconnectedness of many critical aspects of development and how to intentionally weave these factors to advance a more holistic approach. The need for a more holistic and inclusive approach to child and adolescent development is increasingly evident to promote long-term health and well-being as the overall percentage of children, adolescents, and adults who suffer from mental health disorders is increasing. To address this issue, our authorship team consists of researchers in the areas of developmental psychology, neuroscience, motor development, exercise science, and mental health. The collective ideas outlined in this paper are aligned to address the need to remove disciplinary-specific boundaries and elucidate synergistic linkages across multiple research domains that support holistic development and lifespan health and wellness. We propose a conceptual framework that comprehensively addresses the integration of physical, cognitive, psychological, social, and emotional domains of child and adolescent development. In addition, we also provide a holistic preventative approach that is aligned with a contemporary intervention structure (i.e., Multi-tiered Systems of Support) to promote, from a developmental perspective, positive trajectories of health and well-being across childhood and adolescence. [ABSTRACT FROM AUTHOR]

The aim of this mini-review paper is to present an overview of work on consciousness from a philosophical perspective, and to argue for the continuing relevance of philosophy in consciousness research. After introducing some major philosophical positions about the relationship between consciousness and matter, we argue that the problem of consciousness that many authors have focused on—the mind–body or mind-brain problem—is only one among several problems of consciousness. We illustrate the idea that the perplexities about consciousness go beyond its relationship with matter by discussing the relationship between consciousness, self-consciousness, and selfhood. This discussion also indicates ways in which philosophy and neuroscience can learn from each other. [ABSTRACT FROM AUTHOR]

Simulating the human natural visual system is beneficial for understanding brain intelligence and exploiting new aspects of computer vision. Previous studies have proposed many progressive models and experiments for visual tracking; however, only a few consider all factors involved in visual tracking. Improvements in cross-modal sensory fusion, online physical environment training, and leveraging machine learning are required. In this paper, we present a balanced visual tracking study between neuroscience models and deep-learning methods. In our visual tracking framework, we modify the original region proposal network and interconnect binocular R-CNNs with a new region of interest (RoI) model. Ground frame prediction can be implemented by localization fusion from binocular R-CNNs, as well as external sensory information, such as a dense disparity map. In the behavior stage, visual-motor transformation is implemented through the online training of saccades, pursuit, and vergence networks in the real environment. As demonstrated on a robot, our framework can learn tracking skills through online parameter updates using physical data collected from the robot. The framework achieves performance highly similar to human behaviors and better accuracy than recent models. Moreover, using prediction from our ground vision model to guide binocular, RoI pooling can improve the efficiency of object recognition and localization and reduce visual tracking errors by 27% compared with the original network. In conclusion, this study proposed an effective binocular tracking framework that draws inspiration from brain structures. The performance showed improved accuracy and robustness in tracking random moving targets. [ABSTRACT FROM AUTHOR]

This paper asks when a natural disease kind is truly ‘reactive’ and when it is merely associated with a corresponding social kind. I begin with a permissive account of real kinds and their structure, distinguishing natural kinds, indifferent kinds and reactive kinds as varieties of real kind characterised by super-explanatory properties. I then situate disease kinds within this framework, arguing that many disease kinds prima facie are both natural and reactive. I proceed to distinguish ‘simple dependence’, ‘secondary dependence’ and ‘essential dependence’ between a natural kind and its classification, and argue that a natural kind is only really reactive, in an important sense, under conditions of essential dependence. On this basis, I offer a principled hypothesis for why psychiatric kinds may be more metaphysically unstable than paradigm somatic disease kinds. [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]

Researchers shouldn’t fear papers that test and find flaws in methods. Such work contributes to better experimental designs and better science. [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]

Active neurons can stimulate the clearance of their own metabolic waste by driving changes to ion gradients in the surrounding fluid and by promoting the pulsation of nearby blood vessels.Neurons stimulate the brain's waste disposal system. [ABSTRACT FROM AUTHOR]

Background: Chronic pain is a pervasive and invisible condition which affects people in a myriad of ways including but not limited to their quality of life, autonomy, mental and physical health, social mobility, and productivity. There are many ethical implications of neuroscience research on chronic pain, given its potential to reduce suffering and improve the lived experience of people in pain. While a growing body of research studies the etiology, neurophysiology, and management of chronic pain, it is unknown to what degree neuroscience research in this area engages with relevant ethics concepts. Aim: To explore the presence of ethics concepts in empirical chronic pain neuroscience literature to advance knowledge regarding the ethics of chronic pain management. Methods: We conducted a hybrid bibliometric analysis and scoping review of chronic pain neuroscience articles published between 1999 and 2021 to identify the presence of ethics concepts. We selected articles from the top, middle, and bottom 20 neuroscience journals ranked by Impact Factor. We conducted a database search of Web of Science and a hand-search using PubMed, Google Scholar, and the reference lists of included articles. Findings: Our database search yielded 2779 results from which 46 articles met inclusion criteria. An additional 13 articles were hand-retrieved using PubMed and Google Scholar in accordance with the inclusion criteria, totaling 59 articles. We identified four main ethics themes in our analysis: 1) Quality of Life (n = 46), 2) Autonomy (n = 5), 3) Transparency (n = 4), and 4) Beneficence and Non-Maleficence (n = 4). Conclusion: Most neuroscience papers do not include a discussion of ethics related to chronic pain conditions. Those that do tend to merely state rather than define or contextualize a particular ethics concept. Given the potential ethical implications of neuroscience research for people living with chronic pain, we argue that to maximize its public health benefit, neuroscience researchers should consider the ethical relevance of their work within their scientific publications. This may generate further ethical reflection within the field, to improve pain management. [ABSTRACT FROM AUTHOR]

The paper studies the rise of neuroscience in initial teacher education, paying attention to the relatively recent Australian Government (2023) report titled ‘Strong Beginnings’. In taking up a critical policy sociology lens, we focus on the first priority within the reforms, which is mandating brain science and the ‘brain and learning’ as core curriculum within initial teacher education. The reforms will embed standardised curriculum into initial teacher education and tie this curriculum to graduate teacher standards, bracketed within prescribed texts, ideologies and agendas. The reforms are positioned within the ‘what works’ movement, increasing accreditation and certain types of evidence, and the role and authority of intermediary organisations, including the Australian Institute for Teaching and School Leadership (AITSL). It promotes research that has been paid for by the think tank Centre for Independent Studies and aligned with the Australian Education Research Organisation and Education Endowment Foundation. In this paper, we endeavour to highlight how the singular and narrow focus on brain-based approaches is not only reductionist, but also potentially generative of oppressive technologies. The mandating of standardised curriculum and brain science undermines educators, including initial teacher educators, and bolsters private interests in education. The standardisation of core curriculum, which will be tied to accreditation processes and graduate teacher standards, is underpinned by a punitive-accountability based approach. Furthermore, whilst it is less visible, these reforms contain brain science tropes redolent of eugenics and deficit framings of low socio-economic status students. [ABSTRACT FROM AUTHOR]

Multiscale modeling techniques have attracted increasing attention by philosophers of science, but the resulting discussions have almost exclusively focused on issues surrounding explanation (e.g., reduction and emergence). In this paper, I argue that besides explanation, multiscale techniques can serve important exploratory functions when scientists model systems whose organization at different scales is ill-understood. My account distinguishes explanatory and descriptive multiscale modeling based on which epistemic goal scientists aim to achieve when using multiscale techniques. In explanatory multiscale modeling, scientists use multiscale techniques to select information that is relevant to explain a particular type of behavior of the target system. In descriptive multiscale modeling scientists use multiscale techniques to explore lower-scale features which could be explanatorily relevant to many different types of behavior, and to determine which features of a target system an upper-scale data pattern could refer to. Using multiscale models from data-driven neuroscience as a case study, I argue that descriptive multiscale models have an exploratory function because they are a sources of potential explanations and serve as tools to reassess our conception of the target system. [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]

The gut-brain axis is believed to constitute a bidirectional communication mechanism that affects both mental and digestive processes. Recently, the role of the gut microbiota in cognitive performance has been the focus of much research. In this paper, we discuss the effects of gut microbiota and nutrition on spatial memory and learning. Studies have shown the influence of diet on cognitive capabilities such as spatial learning and memory. It has been reported that a high-fat diet can alter gut microbiota which subsequently leads to changes in spatial learning and memory. Some microorganisms in the gut that can significantly affect spatial learning and memory are Akkermansia muciniphila, Bifidobacterium, Lactobacillus, Firmicutes, Bacteroidetes, and Helicobacter pylori. For example, a reduction in the amount of A. muciniphila in the gut leads to increased intestinal permeability and induces immune response in the brain which then negatively affects cognitive performances. We suggest that more studies should be carried out regarding the indirect effects of nutrition on cognitive activities via alteration in gut microbiota. [ABSTRACT FROM AUTHOR]

Studies reveal how neuronal populations in the fruit fly brain work together to compare the direction of a goal with the direction that the fly is facing, and convert this into a signal that steers the fly towards its target.Fly brains compare directional information while steering to a goal. [ABSTRACT FROM AUTHOR]

A new wave of research is unpicking the relationship between cancer and neurons — and looking for ways to stop the crosstalk. [ABSTRACT FROM AUTHOR]

Highlights from News & Views published this year.Snapshots of research from 2021. [ABSTRACT FROM AUTHOR]

Multivariate pattern analysis, or MVPA, has become one of the most popular analytic methods in cognitive neuroscience. Since its inception, MVPA has been heralded as offering much more than regular univariate analyses, for—we are told—it not only can tell us which brain regions are engaged while processing particular stimuli, but also which patterns of neural activity represent the categories the stimuli are selected from. We disagree, and in the current paper we offer four conceptual challenges to the use of MVPA to make claims about neural representation. Our view is that the use of MVPA to make claims about neural representation is problematic. [ABSTRACT FROM AUTHOR]

The discovery of the engram, the physical substrate of memory, is a central challenge for the sciences of memory. Following the application of optogenetics to the neurobiological study of memory, scientists and philosophers claim that the engram has been found. In this paper, I evaluate the implications of applying optogenetic tools to the localization of the engram. I argue that conceptions of engram localization need to be revised to be made consistent with optogenetic studies of the engram. I distinguish between challenges to vehicle and content localization. First, I consider the silent engram hypothesis. According to this hypothesis, optogenetic studies indicate that synaptic efficacy, the traditional engram-bearing vehicle, is important merely for retrieval. I argue that this interpretation rests upon a misunderstanding of accessibility. Second, I argue that optogenetic-based strategies and findings conflict with preservationist and constructivist views on memory storage. There is an enduring trace, but stored content may change over time and experience, resulting in doubt about what constitutes a single engram. [ABSTRACT FROM AUTHOR]

Neuroscientists have located brain activity that prepares or encodes action plans before agents are aware of intending to act. On the basis of these findings and broader agency research, activity in these regions has been proposed as the neural realizers of practical intention. My aim in this paper is to evaluate the case for taking these neural states to be neural representations of intention. I draw on work in philosophy of action on the role and nature of practical intentions to construct a framework of the functional profile of intentions fit for empirical investigation. With this framework, I turn to the broader empirical neuroscience literature on agency to assess these proposed neural representations of intention. I argue that while these neural states in some respects satisfy the functions of intention in planning agency prospective of action, their fit with the role of intention in action execution is not well supported. I close by offering a sketch of which experimental task features could aid in the search for the neural realizer of intention in action. [ABSTRACT FROM AUTHOR]

In this paper we explore the notion of rehearsal as a way to develop an embodied and enactive account of imagining. After reviewing the neuroscience of motor imagery, we argue, in the context of performance studies, that rehearsal includes forms of imagining that involve motor processes. We draw on Sartre's phenomenology of imagining which also suggests that imagining involves motor processes. This research in neuroscience and phenomenology, supports the idea of an embodied and enactive account of imagination. [ABSTRACT FROM AUTHOR]

Absence of evidence arguments are indispensable to comparative neurobiology. The absence in a given species of a homologous neural architecture strongly correlated with a type of conscious experience in humans should be able to be taken as a prima facie reason for concluding that the species in question does not have the capacity for that conscious experience. Absence of evidence reasoning is, however, widely disparaged for being both logically illicit and unscientific. This paper argues that these concerns are unwarranted. There is no logical barrier to formulating cogent absence of evidence arguments; indeed, accepting such arguments is part of what it is to be committed to falsifiability as a critical aspect of the scientific method. Absence of evidence arguments can always be blocked, however, by assuming that psychological properties are 'multiply realizable'. While we take multiple realizability to be highly likely at some level of analysis, we argue that it is question-begging to assume that it exists at every level of analysis, and thus it should not automatically be thought to undermine absence of evidence reasoning in the animal consciousness debate. Using the example of pain and focusing on homologies at the level of information processing tasks, we show how, in the science of consciousness, an absence of evidence might well serve as evidence of absence. [ABSTRACT FROM AUTHOR]

Although there is a substantial philosophical literature on dynamical systems theory in the cognitive sciences, the same is not the case for neuroscience. This paper attempts to motivate increased discussion via a set of overlapping issues. The first aim is primarily historical and is to demonstrate that dynamical systems theory is currently experiencing a renaissance in neuroscience. Although dynamical concepts and methods are becoming increasingly popular in contemporary neuroscience, the general approach should not be viewed as something entirely new to neuroscience. Instead, it is more appropriate to view the current developments as making central again approaches that facilitated some of neuroscience's most significant early achievements, namely, the Hodgkin–Huxley and FitzHugh–Nagumo models. The second aim is primarily critical and defends a version of the "dynamical hypothesis" in neuroscience. Whereas the original version centered on defending a noncomputational and nonrepresentational account of cognition, the version I have in mind is broader and includes both cognition and the neural systems that realize it as well. In view of that, I discuss research on motor control as a paradigmatic example demonstrating that the concepts and methods of dynamical systems theory are increasingly and successfully being applied to neural systems in contemporary neuroscience. More significantly, such applications are motivating a stronger metaphysical claim, that is, understanding neural systems as being dynamical systems, which includes not requiring appeal to representations to explain or understand those phenomena. Taken together, the historical claim and the critical claim demonstrate that the dynamical hypothesis is undergoing a renaissance in contemporary neuroscience. [ABSTRACT FROM AUTHOR]

In their attempt to connect the workings of the human mind with their neural realizers, cognitive neuroscientists often bracket out individual differences to build a single, abstract model that purportedly represents (almost) every human being's brain. In this paper I first examine the rationale behind this model, which I call 'Platonic Brain Model'. Then I argue that it is to be surpassed in favor of multiple models allowing for patterned inter-individual differences. I introduce the debate on legitimate (and illegitimate) ways of mapping neural structures and cognitive functions, endorsing a view according to which function-structure mapping is context-sensitive. Building on the discussion of the ongoing debate on the function(s) of the so-called Fusiform "Face" Area, I show the necessity of indexing function-structure mappings to some populations of subjects, clustered on the basis of factors such as their expertise in a given domain. [ABSTRACT FROM AUTHOR]

In 1987, two independent groups cloned a gene that encoded the potassium-ion-channel protein Shaker, enabling functional and structural studies that have transformed ion-channel research. Cloning of the Shaker protein had far-reaching effects in neuroscience. [ABSTRACT FROM AUTHOR]

The accumulation of a network of proteins and sugars called the extracellular matrix stops insulin from reaching hunger-regulating neurons, disrupting normal food intake and metabolism, and contributing to obesity. Neuronal insulin resistance caused by excess extracellular matrix. [ABSTRACT FROM AUTHOR]

Neural activity in human brains rapidly restructures to reflect hidden relationships needed to adapt to a changing environment. Surprisingly, trial-and-error learning and verbal instruction induce similar changes.Mental maps of associated items are rapidly assembled by the human brain. [ABSTRACT FROM AUTHOR]