Since the growth cone was first described a century ago by Cajal, considerable effort has been directed towards understanding the mechanisms responsible for its guidance. Traditionally, attention has focussed on the role of adhesive molecules in determining neural development. Recently, it has become apparent that inhibitory interactions may play a crucial part in axonal navigation. A common feature of inhibition seen in three model systems (peripheral nerve segmentation, retinotectal mapping and CNS/PNS segregation) is a collapse of the motile (...) structures of the growth cone. It is increasingly clear that the identification of molecular mechanisms of inhibition, as well as those of adhesion, will be of fundamental importance to understanding neural development. (shrink)

The neuregulin gene encodes a series of polypeptide growth factors that can influence the growth state of target vertebrate cells in culture. Recently, three studies have explored the in vivo function of the neuregulin signaling system in mice by disrupting the genes encoding the neuregulin ligand(1) and two of its receptors, ErbB2(2) and ErbB4(3). Each of the genes is essential for development, and aberrations in cardiac and neural development are particularly prominent in mutant embryos. The observed defects, (...) together with the localization of expression of the neuregulin signaling components within these tissues, highlight a paracrine mechanism for neuregulin‐mediated intercellular communication. (shrink)

Clahsen's theory raises problems that make it seem untenable. As an alternative, a constructivist neural network model is reported that develops a modular architecture and in which a single associative mechanism produces all inflections, displaying an emergent dissociation between regular and irregular verbs. Thus, Clahsen's rejection of associative models of inflection concerns only a subgroup of these models.

On the basis of recent advancements in both neuroscience and archaeology, we propose a plausible biocultural mechanism at the basis of cultural evolution. The proposed mechanism, which relies on the notions of cultural exaptation and cultural neural reuse, may account for the asynchronous, discontinuous, and patchy emergence of innovations around the globe. Cultural exaptation refers to the reuse of previously devised cultural features for new purposes. Cultural neural reuse refers to cases in which exposure to cultural (...) practices induces the formation, activation, and stabilization of new functional and/or structural brain networks during the individual lifespan. The invention of writing is interpreted as a case of cultural exaptation of previous devices to record information, in use since at least the Early Later Stone Age and the beginning of the Upper Paleolithic. The measurable changes in brain structure and functioning caused by learning to read are proposed as an exemplar case of cultural neural reuse. It is argued that repeated cycles of cultural exaptation, development of appropriate strategies of cultural transmission, and ensuing cultural neural reuse represent the fundamental mechanism that has regulated the cultural evolution of our lineage. A general predictive model of when and under which circumstances the proposed mechanism should be expected to occur is proposed, and the relationship of our mechanism with gene-culture coevolutionary models is discussed. (shrink)

During development of the Xenopus embryo, the formation of the nervous system depends on an inductive interaction between mesoderm and ectoderm. The result is a neural tube that is regionally differentiated along the anterior–posterior axis from forebrain to spinal cord (Fig. 1). The discovery of genes whose transcripts can be used as molecular markers for different regions of the nervous system has permitted reassessment of the existing theories of neural tissue formation. Although the neural inducing molecules (...) remain elusive, the mechanism by which cells interact to form a regionally differentiated nervous system is becoming clearer. (shrink)

This chapter explores some of the ways that mechanisms are invoked in neuroscience and looks at a selection of the philosophical problems that arise when trying to understand mechanistic explanations. It introduces a series of historical case studies that illustrate how neuroscientists have depended on mechanistic metaphors in their efforts to understand the mind and brain, and how their mechanistic explanations have developed over time. The chapter highlights what contemporary philosophers have identified as the fundamental features of mechanisms and mechanistic (...) explanation. It considers some of the methodological issues that arise in neuroscience including how to integrate psychological with neural models, how to generalize findings in model organisms like the sea slug Aplysia to human learning and memory, and whether to favor top-down or bottom-up methods. Constructing multi-level mechanistic explanations involves intensive collaboration across different branches of science, and involves many challenges, both pragmatic and methodological. (shrink)

While neuroscientists often characterize brain activity as representational, many philosophers have construed these accounts as just theorists’ glosses on the mechanism. Moreover, philosophical discussions commonly focus on finished accounts of explanation, not research in progress. I adopt a different perspective, considering how characterizations of neural activity as representational contributes to the development of mechanistic accounts, guiding the investigations neuroscientists pursue as they work from an initial proposal to a more detailed understanding of a mechanism. I develop (...) one illustrative example involving research on the information-processing mechanisms mammals employ in navigating their environments. This research was galvanized by the discovery in the 1970s of place cells in the hippocampus. This discovery prompted research in what the activity of these cells represents and how place representations figure in navigation. It also led to the discovery of a host of other types of neurons—grid cells, head-direction cells, boundary cells—that carry other types of spatial information and interact with place cells in the mechanism underlying spatial navigation. As I will try to make clear, the research is explicitly devoted to identifying representations and determining how they are constructed and used in an information processing mechanism. Construals of neural activity as representations are not mere glosses but are characterizations to which neuroscientists are committed in the development of their explanatory accounts. (shrink)

In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given (...) that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description—four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity, to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene–environment interactions. We argue that gene–behavior associations can inform cognitive theory with respect to effect size, specificity, and timing. The model demonstrates a means by which researchers can undertake multiscale modeling with respect to cognition and develop highly specific and complex hypotheses across multiple levels of description. (shrink)

This volume brings together new papers advancing contemporary debates in foundational, conceptual, and methodological issues in cognitive neuroscience. The different perspectives presented in each chapter have previously been discussed between the authors, as the volume builds on the experience of Neural Mechanisms Online – webinar series on the philosophy of neuroscience organized by the editors of this volume. The contributed chapters pertain to five core areas in current philosophy of neuroscience. It surveys the novel forms of explanation developed in (...) cognitive neuroscience, and looks at new concepts, methods and techniques used in the field. The book also highlights the metaphysical challenges raised by recent neuroscience and demonstrates the relation between neuroscience and mechanistic philosophy. Finally, the book dives into the issue of neural computations and representations. Assembling contributions from leading philosophers of neuroscience, this work draws upon the expertise of both established scholars and promising early career researchers. (shrink)

A central project for the neuroscience of consciousness is to reveal the neural basis of consciousness. For the past 20-odd years, this project has been conceptualized in terms of minimal sufficiency. Recently, a number of authors have suggested that the project is better conceived in mechanistic terms as the search for difference-makers. In this paper, I (i) motivate this mechanistic alternative to minimal sufficiency, (ii) develop it further by clarifying debates about the prospects of leveraging mutual manipulability to distinguish (...) constitutive difference-makers from those that are merely causal, and (iii) explore the implications this has for recent debates concerning the status of the prefrontal cortex. I argue that adopting a mechanistic approach to the neuroscience of consciousness suggests that the prefrontal cortex is part of the neural mechanisms underlying consciousness even if it is not strictly speaking a necessary part. (shrink)

Two widely accepted assumptions within cognitive science are that (1) the goal is to understand the mechanisms responsible for cognitive performances and (2) computational modeling is a major tool for understanding these mechanisms. The particular approaches to computational modeling adopted in cognitive science, moreover, have significantly affected the way in which cognitive mechanisms are understood. Unable to employ some of the more common methods for conducting research on mechanisms, cognitive scientists’ guiding ideas about mechanism have developed in conjunction with (...) their styles of modeling. In particular, mental operations often are conceptualized as comparable to the processes employed in classical symbolic AI or neural network models. These models, in turn, have been interpreted by some as themselves intelligent systems since they employ the same type of operations as does the mind. For this paper, what is significant about these approaches to modeling is that they are constructed specifically to account for behavior and are evaluated by how well they do so—not by independent evidence that they describe actual operations in mental mechanisms. (shrink)

Differences in socioeconomic status (SES) correlate both with differences in cognitive development and in brain structure. Associations between SES and brain measures such as cortical surface area and cortical thickness mediate differences in cognitive skills such as executive function and language. However, causal accounts that link SES, brain, and behavior are challenging because SES is a multidimensional construct: correlated environmental factors, such as family income and parental education, are only distal markers for proximal causal pathways. Moreover, the causal accounts (...) themselves must span multiple levels of description, employ a developmental perspective, and integrate genetic effects on individual differences. Nevertheless, causal accounts have the potential to inform policy and guide interventions to reduce gaps in developmental outcomes. In this article, we review the range of empirical data to be integrated in causal accounts of developmental effects on the brain and cognition associated with variation in SES. We take the specific example of language development and evaluate the potential of a multiscale computational model of development, based on an artificial neural network, to support the construction of causal accounts. We show how, with bridging assumptions that link properties of network structure to magnetic resonance imaging (MRI) measures of brain structure, different sets of empirical data on SES effects can be connected. We use the model to contrast two possible causal pathways for environmental influences that are associated with SES: differences in prenatal brain development and differences in postnatal cognitive stimulation. We then use the model to explore the implications of each pathway for the potential to intervene to reduce gaps in developmental outcomes. The model points to the cumulative effects of social disadvantage on multiple pathways as the source of the poorest response to interventions. Overall, we highlight the importance of implemented models to test competing accounts of environmental influences on individual differences. (shrink)

Under the background that the development of low-carbon circular economy is the objective requirement for the in-depth implementation of scientific development and the inevitable choice for promoting the sustainable development of economy and society, it is not only the requirement of corporate social responsibility but also the path to realize corporate social responsibility. Enterprises should become the representative and model of social responsibility practice in the development of low-carbon circular economy, in order to promote the fulfilment (...) and development of corporate social responsibility in the whole society. Therefore, it is of great theoretical and practical significance to study the realization of corporate social responsibility in the context of low-carbon circular economy. This paper introduces the connotation of low-carbon circular economy and corporate social responsibility, analyses the reality and theoretical basis of realizing corporate social responsibility in low-carbon circular economy, analyses the interactive relationship between the development of low-carbon circular economy and the realization of corporate social responsibility, and puts forward the construction of enterprise low-carbon operation mechanism. This paper uses the research of corporate social responsibility based on radial basis function neural network to build a low-carbon circular economy. The evaluation model of environment economy coupling development is verified by an example, which provides useful guidance for the evaluation and development of corporate social responsibility. (shrink)

Long‐term modification of synaptic strength is thought to be the basic mechanism underlying the activity‐dependent refinement of neural circuits and the formation of memories engrammed on them. Studies ranging from cell culture preparations to humans subjects indicate that the decision of whether a synapse will undergo strengthening or weakening critically depends on the temporal order of presynaptic and postsynaptic activity. At many synapses, potentiation will be induced only when the presynaptic neuron fires an action potential within milliseconds before (...) the postsynaptic neuron fires, whereas weakening will occur when it is the postsynaptic neuron that fires first. Such processes might be important for the remodeling of neural circuits by activity during development and for network functions such as sequence learning and prediction. Ultimately, this synaptic property might also be fundamental for the cognitive process by which we structure our experience through cause and effect relations. BioEssays 24:212–222, 2002. © 2002 Wiley Periodicals, Inc.; DOI 10.1002/bies.10060. (shrink)

Inner speech emerges in early childhood, in parallel with the maturation of the dorsal language stream. To date, the developmental relations between these two processes have not been examined. We review evidence that the dorsal language stream has a role in supporting the psychological phenomenon of inner speech, before considering paediatric studies of the dorsal stream’s anatomical development and evidence for its emerging functional roles. We examine possible causal accounts of the relations between these two developmental processes, and consider (...) their implications for phylogenetic theories about the evolution of inner speech and the accounts of the ontogenetic relations between language and cognition. (shrink)

Excessive alcohol use has adverse effects on the central nervous system (CNS) and can lead to alcohol use disorders (AUDs). Recent studies have suggested that myelin reductions may directly contribute to CNS dysfunctions associated with AUDs. Myelin consists of compact lipid membranes wrapped around axons to provide electrical insulation and trophic support. Regulation of myelin is considered as a new form of neural plasticity due to its profound impacts on the computation of neural networks. In this review, the (...) authors first discuss experimental evidence showing how alcohol exposure causes demyelination in different brain regions, often accompanied by deficits in cognition and emotion. Next, they discuss postulated molecular and cellular mechanisms underlying alcohol's impact on myelin. It is clear that more extensive investigations are needed in this important but underexplored research field in order to gain a better understanding of the myelin‐behavior relationship and to develop new treatment strategies for AUDs. (shrink)

Distractibility is one of the key features of attention deficit hyperactivity disorder and has been associated with alterations in the neural orienting and alerting networks. Task-irrelevant stimuli are thus expected to have detrimental effects on the performance of patients with ADHD. However, task-irrelevant presentation of novel sounds seems to have the opposite effect and improve subsequent attentional performance particularly in patients with ADHD. Here, we aimed to understand the neural modulations of the attention networks underlying these improvements. Fifty (...) boys participated in a functional magnetic resonance imaging study in which unique or repeatedly presented sounds were placed before a visual flanker task in 2/3 of the trials. We found that presenting any sound improved task performance in all participants, but the underlying neural mechanisms differed for the type of sound. Familiar sounds led to a stronger increase in activity in the left posterior insula in patients with ADHD compared to typically developing peers. Novel sounds led to activations of the fronto-temporoparietal ventral attention network, likewise in ADHD and TD. These changes in signaling by novelty in the right inferior frontal gyrus were directly related to improved response speed showing that neural orienting network activity following novel sounds facilitated subsequent attentional performance. This mechanism of behavioral enhancement by short distractions could potentially be useful for cognitive trainings or homework situations. (shrink)

In previous research Libet discovered that a critical time period for neural activation is necessary in order for a stimulus to become conscious. This necessary time period varies from subject to subject. In this current study, six subjects for whom the time for neural activation of consciousness had been previously determined were administered a battery of psychological tests on the basis of which ratings were made of degree of repressiveness. As hypothesized, repressive subjects had a longer critical time (...) period for neural activation of consciousness, suggesting the possibility that this neurophysiological time factor is a necessary condition for the development of repression. (shrink)

Cultural development is often reflected in the emotional expression of various cultural carriers, such as literary works, movies, etc. Therefore, the cultural development can be analyzed through emotion analysis of the text, so as to sort out its context and obtain its development dynamics. This paper proposes a text emotion analysis method based on deep learning. The traditional neural network model mainly deals with the classification task of short texts in the form of word vectors, which (...) causes the model to rely too much on the accuracy of word segmentation. In addition, the short texts have the characteristics of short corpus and divergent features. A text emotion classification model combing the Bidirectional Encoder Representations from Transformers and Bi-directional Long Short-Term Memory is developed in this work. First, the BERT model is used to convert the trained text into a word-based vector representation. Then, the generated word vector is employed as the input of the BiLSTM to obtain the semantic representation of the context of the relevant word. By adding random dropout, the mechanism prevents the model from overfitting. Finally, the extracted feature vector is input to the fully connected layer, and the emotion category to which the text belongs is calculated through the Softmax function. Experiments show that in processing short texts, the proposed model based on BERT-BiLSTM is more accurate and reliable than the traditional neural network model using word vectors. The proposed method has a better analysis effect on the development of western culture. (shrink)

Retinoic acid (RA), a derivative of vitamin A, is essential for normal mammalian development. Developmental abnormalities induced by RA excess and vitamin A deficiency are different even though they affect the same organ systems, and it is clear that there are intraembryonic tissue differences in the requirement for RA. The developmental functions of RA are mediated by its effects on gene expression. In the nucleus, two different forms of RA bind to and activate two families of nuclear receptors, which (...) themselves co‐operate in initiating the transcription of target genes. In this article I propose that the amount of RA reaching the nucleus in different embryonic tissues is modulated by a mechanism involving three cytoplasmic binding proteins for retinol (CRBP I) and retinoic acid (CRABP I and II). Abnormalities of craniofacial development resulting from exposure of early neural plate stage embryos to RA excess have been studied in some detail; their initial stages involve alteration of both morphological development and the segment‐specific pattern of gene expression in the early hindbrain and its derived neural crest. This system is ideal for studying the relationships between retinoic acid receptors, retinoid binding proteins, and the development of genetic and morphological pattern. (shrink)

I hypothesize that re-occurring prior experience of complex systems mobilizes a fast response, whose attractor is encoded by their strongly connected network core. In contrast, responses to novel stimuli are often slow and require the weakly connected network periphery. Upon repeated stimulus, peripheral network nodes remodel the network core that encodes the attractor of the new response. This “core-periphery learning” theory reviews and generalizes the heretofore fragmented knowledge on attractor formation by neural networks, periphery-driven innovation, and a number of (...) recent reports on the adaptation of protein, neuronal, and social networks. The core-periphery learning theory may increase our understanding of signaling, memory formation, information encoding and decision-making processes. Moreover, the power of network periphery-related “wisdom of crowds” inventing creative, novel responses indicates that deliberative democracy is a slow yet efficient learning strategy developed as the success of a billion-year evolution. Also see the video abstract here: https://youtu.be/IIjP7zWGjVE. The network core triggers fast responses to previously experienced stimuli. Innovations require the network periphery that encodes a novel attractor by core-remodeling. This core-periphery learning theory is introduced as a general adaptation, learning, decision-making, and forgetting mechanism of all complex systems including protein, metabolic, signaling, neuronal, social networks, and ecosystems. (shrink)

Fodor’s radical concept nativism flowed from his view that hypothesis testing is the only route to concept acquisition. Many have successfully objected to the overly-narrow restriction to learning by hypothesis testing. Existing representations can be connected to a new representational vehicle so as to constitute a sustaining mechanism for a new representation, without the new representation thereby being constituted by or structured out of the old. This paper argues that there is also a deeper objection. Connectionism shows that a (...) more fundamental assumption underpinning the debate can also be rejected: the assumption that the development of a new representation must be explained in content-involving terms if innateness is to be avoided. -/- Fodor has argued that connectionism offers no new resources to explain concept acquisition: unless it is merely an uninteresting claim about neural implementation, connectionism’s defining commitment to distributed representations reduces to the claim that some representations are structured out of others (which is the old, problematic research programme). Examination of examples of representational development in connectionist networks shows, however, that some such models explain the development of new representational capacities in non-representational terms. They illustrate the possibility of representational development that is not explicable-by-content. Connectionist representations can be distributed in an important sense, which is incompatible with the assumption of explanation-by-content: they can be distributed over non-representational resources that account for their development. Rejecting the assumption of explanation-by-content thereby opens up a more radical way of rejecting Fodor’s argument for radical concept nativism. (shrink)

Since its development about 15 years ago, functional magnetic resonance imaging (fMRI) has become the leading research tool for mapping brain activity. The technique works by detecting the levels of oxygen in the blood, point by point, throughout the brain. In other words, it relies on a surrogate signal, resulting from changes in oxygenation, blood volume and flow, and does not directly measure neural activity. Although a relationship between changes in brain activity and blood flow has long been (...) speculated, indirectly examined and suggested and surely anticipated and expected, the neural basis of the fMRI signal was only recently demonstrated directly in experiments using combined imaging and intracortical recordings. In the present paper, we discuss the results obtained from such combined experiments. We also discuss our current knowledge of the extracellularly measured signals of the neural processes that they represent and of the structural and functional neurovascular coupling, which links such processes with the hemodynamic changes that offer the surrogate signal that we use to map brain activity. We conclude by considering applications of invasive MRI, including injections of paramagnetic tracers for the study of connectivity in the living animal and simultaneous imaging and electrical microstimulation. D 2004 Elsevier Inc. All rights reserved. (shrink)

Can we properly define and explain the human mind an aesthetic mind? The purpose of the paper is to answer this and the related questions that it implies. How do we understand the conceptual field of the aesthetic? What do we mean when we speak about an aesthetic experience or when we express an aesthetic judgement? The first move consists in shaping the outlines of the «aesthetic» as a cluster-concept. Having identified the conceptual core of aesthetic as an expressive synthesis (...) between the emotional and cognitive layers of the experience, I try to develop a theoretical paradigm consistent with it and, consequentially, a mental mechanism of the aesthetic. The next step consists, therefore, in replacing the causal monism involved in the adaptationist paradigm by a plurality of factors. Consequently, I claim that at the origin of the aesthetic mechanism there are four factors. These factors are: 1) the mimetic assimilation of the real; 2) the "seeking" or the pleasure of exploration; 3) the pleasure of exercising preferences as a degree of freedom and an advantage in the conduct of life; 4) the impulse to play. All these factors are dispositions rooted in the system of primary emotions and represent the preconditions for the epigenetic emergence of the aesthetic mechanism. Conclusively, this mechanism is considered a dynamic activity of the brain that integrates into a single space of mutual resonance and harmonization neocortical and subcortical neural circuits: aspects of mental life emotionally attuned and aspects that are specific of cognitive processing of information. (shrink)

An influential view of the nature of the language system is that of an evolved biological system in which a set of rules is combined with a lexicon that contains the words of the language together with a representation of their context. Alternative views, usually based on connectionist modeling, attempt to explain the structure of language on the basis of complex associative processes. Here, I put forward a third view that stresses experience-dependent structural development of the brain circuits supporting (...) language as a core principle of the organization of the language system. In this view, embodied in a recent neuroconstructivist neural network of past tense development and processing, initial domain-general predispositions enable the development of functionally specialized brain structures through interactions between experience-dependent brain development and statistical learning in a structured environment. Together, these processes shape a biological adult language system that appears to separate into distinct mechanism for processing rules and exceptions, whereas in reality those subsystems co-develop and interact closely. This view puts experience-dependent brain development in response to a specific language environment at the heart of understanding not only language development but adult language processing as well. (shrink)

Early and accurate diagnosis of glaucoma is critical for avoiding human vision deterioration and preventing blindness. A deep-neural-network model has been developed for the diagnosis of glaucoma based on Heidelberg retina tomography, called “Seeking Common Features and Reserving Differences Net” to make full use of the HRT data. In this work, the proposed SCRD-Net model achieved an area under the curve of 94.0%. For the two HRT image modalities, the model sensitivities were 91.2% and 78.3% at specificities of 0.85 (...) and 0.95, respectively. These results demonstrate a significant improvement over earlier results. In addition, we visualized the network outputs to develop an interpretation of the learned mechanism for discriminating glaucoma and normal images. Thus, the SCRD-Net can be an effective diagnostic indicator of glaucoma during clinical screening. To facilitate SCRD-Net utilization by the scientific community, the code implementation will be made publicly available. (shrink)

Emotion regulation is an umbrella term to describe interactive, goal-dependent explicit and implicit processes that are intended to help an individual manage and shift an emotional experience. The primary window for appropriate emotion regulation development occurs during the infant, toddler, and preschool years. Atypical emotion regulation development is considered a risk factor for mental health problems and has been implicated as a primary mechanism underlying childhood pathologies. Current treatments are predominantly verbal- and behavioral-based and lack the opportunity (...) to practice in-the-moment management of emotionally charged situations. There is also an absence of caregiver-child interaction in these treatment strategies. Based on behavioral and neural support for music as a therapeutic mechanism, the incorporation of intentional music experiences, facilitated by a music therapist, may be one way to address these limitations. Musical Contour Regulation Facilitation is an interactive therapist-child music-based intervention for emotion regulation development practice in preschoolers. The Musical Contour Regulation Facilitation intervention uses the deliberate contour and temporal structure of a music therapy session to mirror the changing flow of the caregiver-child interaction through the alternation of high arousal and low arousal music experiences. The purpose of this paper is to describe the Therapeutic Function of Music, a theory-based description of the structural characteristics for a music-based stimulus to musically facilitate developmentally appropriate high arousal and low arousal in-the-moment emotion regulation experiences. The Therapeutic Function of Music analysis is based on a review of the music theory, music neuroscience, and music development literature and provides a preliminary model of the structural characteristics of the music as a core component of the Musical Contour Regulation Facilitation intervention. (shrink)

In the third Whitehead Psychology Nexus Studies, we have discussed (i) the dual-aspect-dual-mode proto-experience (PE)-subjective experience (SE)framework of consciousness based on neuroscience, (ii) its implication in war, suffering, peace, and happiness, (iii) the process of sublimation for optimizingthem and converting the negative aspects of seven groups of self-protective energy system (desire, anger, ego, greed, attachment, jealousy, and selfishlove)into their positive aspects from both western and eastern perspectives (Vimal, 2009b). In this article, we summarize the recent development since then as (...) follows. (1) In (Vimal, 2010), we rigorously investigated the classical and quantum matching and selection processes for precisely experiencing a specific SE in a specific neural-network. (2) In (Vimal, 2009i), we unpacked the quantum view of superposition related to the superposition-based hypothesis H1 of our framework in terms of subquantum dual-aspect primal entities (bhutatmas) and addressed the related explanatory gaps. (3) We developed alternative hypotheses of our framework, namely, the superposition-then-integration-emergence based H2, the integration-emergence based H3, the intelligent mechanism based H4, and the vacuum/Aether based H5. We concluded that our framework with H1 is the most optimal one because it has the least number of problems (Vimal, 2009j). (4) We found over 40 different but overlapping meanings attributed to the term ‘consciousness’ and suggested that authors must specify which aspect ofconsciousness they refer to when using this term to minimize confusion (Vimal, 2009f). (5) We proposed definitions of consciousness, qualia, mind, and awareness (Vimal, 2009h). (6) We investigated the necessary ingredients for access (reportable) consciousness: wakefulness, re-entry, attention, working memory and so on (Vimal, 2009g). (7) We discussed Nâgârjuna’s philosophy of dependent co-origination with respect to our PE-SE framework (Vimal, 2009a). (8) We linked dynamic systems theory and fractal catalytic theory with standard representation theory using our framework (Vimal, 2009d). (9) We introduce the PE-SE aspects of consciousness in theoretical classical and quantum physics including loop quantum gravity and string theory (Vimal, 2009k). (10) In (Vimal, 2009c), we proposed that the SE of subject or ‘self’ in self-related neural-network is tuned to the self-related SEs/PEs superposed in other innumerable entities during samadhi state via matching and selection processes. This leads to bliss, ecstasy, or exceptionally high degree of climax at samadhi state. We conclude that, so far, the dualaspect-dual-mode PE-SE framework with hypothesis H1 is the most optimal framework for explaining our conventional reality because it has the least number of problems. Keywords: Evolution of consciousness; Internal representation; Sensorimotor interaction; Dual-aspect model; Subjective experience; Protoexperiences, Explanatory gap; Mind-brain problem; Purusha; Prakriti; Eastern and Western perspectives; Yoga; Sublimation process; Whitehead; Process and Reality; Occasions of experience; Superposition; Subquantum dual-aspect primal entities (bhutatmas); Superposition-then-integration-emergence; Integration-emergence; Intelligent mechanism; Vacuum/Aether; qualia; Mind; Awareness; Nâgârjuna; Classical and quantum physics; Loop quantum gravity; String theory. (shrink)

The ubiquitin proteasome system is a potent regulatory mechanism used to control protein stability in numerous cellular processes, including neural development. Many neurodegenerative diseases are featured by the accumulation of UPS‐associated proteins, suggesting the UPS dysfunction may be crucial for pathogenesis. Recent experiments have highlighted the UPS as a key player during synaptic development. Here we summarize recent discoveries centered on the role of the UPS in synapse remodeling and draw attention to the potential link between (...) the synaptic UPS dysfunction and the pathology of neurodegenerative diseases. BioEssays 30:1075–1083, 2008. © 2008 Wiley Periodicals, Inc. (shrink)

Recent extensions of mechanistic explanation into psychology suggest that cognitive models are only explanatory insofar as they map neatly onto, and serve as scaffolding for more detailed neural models. Filling in those neural details is what these accounts take the integration of cognitive psychology and neuroscience to mean, and they take this process to be seamless. Critics of this view have given up on cognitive models possibly explaining mechanistically in the course of arguing for cognitive models having explanatory (...) value independent of how well they align with neural mechanisms. We can have things both ways, however. The problem with seamless integration accounts is their seamlessness, not that they take cognitive models to be mechanistic. A non-componential view of mechanisms allows for cognitive and neural models that cross cut one another, and for cognitive models that don’t decompose into parts. I illustrate the inadequacy of seamless accounts of integration by contrasting how “filter” models of attention in psychology and of sodium channels in neuroscience developed; by questioning whether the mappings generated by neuroimaging subtraction studies achieve integration; and by reinterpreting the evidence for cognitive models of memory having been successfully integrated with neural models. I argue that the integrations we can realistically expect are more partial, patchy, and full of loose threads than the mosaic unity Craver describes. (shrink)

This paper explores whether the Clean Development Mechanism (CDM), a flexibility mechanism under the Kyoto Protocol, has contributed to poverty alleviation in countries that host CDM projects. We argue that the CDM should deliver pro-poor benefits to the communities in which projects are established, since poverty alleviation is integral to sustainable development, which is one of the main purposes of the CDM. After briefly discussing the background of the CDM, we discuss assessment difficulties to which research (...) is prone when evaluating CDM projects for alleged sustainable development contributions. Section 4 brings together and analyses available empirical research on the pro-poor benefits the CDM purportedly delivers to host country communities, concluding that the CDM has failed to deliver poverty alleviation. Therefore, without attempting to be exhaustive, we suggest policy reforms that aim to redirect the CDM to those most in need of assistance. (shrink)

Roboethics is a recently developed field of applied ethics which deals with the ethical aspects of technologies such as robots, ambient intelligence, direct neural interfaces and invasive nano-devices and intelligent soft bots. In this article we look specifically at the issue of (moral) responsibility in artificial intelligent systems. We argue for a pragmatic approach, where responsibility is seen as a social regulatory mechanism. We claim that having a system which takes care of certain tasks intelligently, learning from experience (...) and making autonomous decisions gives us reasons to talk about a system (an artifact) as being “responsible” for a task. No doubt, technology is morally significant for humans, so the “responsibility for a task” with moral consequences could be seen as moral responsibility. Intelligent systems can be seen as parts of socio-technological systems with distributed responsibilities, where responsible (moral) agency is a matter of degree. Knowing that all possible abnormal conditions of a system operation can never be predicted, and no system can ever be tested for all possible situations of its use, the responsibility of a producer is to assure proper functioning of a system under reasonably foreseeable circumstances. Additional safety measures must however be in place in order to mitigate the consequences of an accident. The socio-technological system aimed at assuring a beneficial deployment of intelligent systems has several functional responsibility feedback loops which must function properly: the awareness and procedures for handling of risks and responsibilities on the side of designers, producers, implementers and maintenance personnel as well as the understanding of society at large of the values and dangers of intelligent technology. The basic precondition for developing of this socio-technological control system is education of engineers in ethics and keeping alive the democratic debate on the preferences about future society. (shrink)

The identification of neural substrates underlying the long lasting debilitating impact of drug cues is critical for developing novel therapeutic tools. Metabolic coupling has long been considered a key mechanism through which astrocytes and neurons actively interact in response of neuronal activity, but recent findings suggested that disrupting metabolic coupling may represent an innovative approach to prevent memory formation, in particular drug‐related memories. Here, we review converging evidence illustrating how memory and addiction share neural circuitry and molecular (...) mechanisms implicating lactate‐mediated metabolic coupling between astrocytes and neurons. With several aspects of addiction depending on mnemonic processes elicited by drug experience, disrupting lactate transport involved in the formation of a pathological learning, linking the incentive, and motivational effects of drugs with drug‐conditioned stimuli represent a promising approach to encourage abstinence. (shrink)

Development and maintenance of diverse organ systems require context‐specific regulation of stem cell behaviour. We hypothesize that this is achieved via reciprocal regulation between the cell cycle machinery and differentiation factors. This idea is supported by the parallel evolutionary emergence of differentiation pathways, cell cycle components and complex multicellularity. In addition, the activities of different cell cycle phases have been found to bias cells towards stem cell maintenance or differentiation. Finally, several direct mechanistic links between these two processes have (...) been established. Here, we focus on interactions between cyclin‐CDK complexes and differentiation regulators of the Notch pathway and Sox family of transcription factors within the context of pluripotent and neural stem cells. Thus, this hypothesis formalizes the links between these two processes as an integrated network. Since such factors are common to all stem cells, better understanding their interconnections will help to explain their behaviour in health and disease. (shrink)

The paper develops a semi-formal model of learning which modifies the traditional paradigm of artificial neural networks, implementing deep learning by means of a key insight borrowed from the works of Marvin Minsky: the so-called Principle of Non-Compromise. The principle provides a learning mechanism which states that conflicts in the processing of data to be integrated are a mark of unreliability or irrelevance; hence, lower-level conflicts should lead to higher-level weight-adjustments. This internal mechanism augments the external (...) class='Hi'>mechanism of weight adjustment by back-propagation, which is typical for the standard models of machine learning. The text is structured as follows: (§1) opens the discussion by providing an informal overview of real-world decision-making and learning; (§2) sketches a typology of decision architectures: the individualistic approach of classical decision theory, the general aggregation mechanism of social choice theory, the local aggregation mechanism of agent-based modeling, and the intermediate hierarchical model of Marvin Minsky's “Society of Mind”; (§3) sketches the general outline of ANIMA – a new model of decision-making and learning that borrows insights from Minsky's informal exposition; (§4) is the bulk of the paper; it provides a discussion of a toy exemplification of ANIMA which lets us see the Principle of Non-Compromise at work; (§5) lists some possible scenarios for the evolution of a model of this kind; (§6) is the closing section; it discusses some important differences between the way ANIMA was construed here and the typical formal rendering of learning by means of artificial neural networks and deep learning. (shrink)

In a matter of mere milliseconds, conversational partners can transform their expectations about the world in a way that accords with another person's perspective. At the same time, in similar situations, the exact opposite also appears to be true. Rather than being at odds, these findings suggest that there are multiple contextual and processing constraints that may guide when and how people consider perspective. These constraints are shaped by a host of factors, including the availability of social and environmental cues, (...) and intrinsic biases and cognitive abilities. To explain how these might be integrated in a new way forward, we turn to an adaptive account of interpersonal interaction. This account draws from basic principles of dynamical systems, principles that we argue are already expressed, both implicitly and explicitly, within a broad landscape of existing research. We then showcase an initial attempt to develop a computational framework to instantiate some of these principles. This framework, consisting of what we argue to be important mechanistic insights rendered by neural network models, is based on a promising and long-standing approach that has yet to take hold in the current domain. We argue that by bridging this gap, new insights into other theoretical accounts, such as the connections between memory and common ground information, might be revealed. (shrink)

The connectivity patterns of many neural circuits are highly ordered and often impressively complex. The intricate order and complexity of neuronal wiring remain not only a challenge for questions related to circuit functions but also for our understanding of how they develop with such an apparent precision. The chemotropic guidance of the growing axon by target‐derived cues represents a central paradigm for how neurons get connected with the correct target cells. However, many studies reveal a remarkable variety of important (...) target‐independent wiring mechanisms. These mechanisms include axonal sorting, axonal tiling, growth cone polarization, as well as cell‐intrinsic mechanisms underlying growth cone sprouting, and neurite branching. Our review focuses on target independent wiring mechanisms and in particular on recent progress emerging from studies on three different sensory systems: olfactory, visual, and somatosensory. We discuss molecular mechanisms that operate during axon‐axon interactions or constitute axon‐intrinsic functions and outline how they complement the well‐known target‐dependent wiring mechanisms. (shrink)

The study hypothesizes that the environmental, social, and governance of the host country have a significant effect on clean development mechanism implementation. As CDM incorporates sustainable development as one of the objectives for the green transition, many countries endeavor to adopt and implement CDM as their cleaner production method. Based on the institutional theory, the study aims to investigate the mechanism by which the institutional process of each ESG pillar makes an opportunity for a host country (...) and to see how such country-specific factors influence the implementation of CDM projects. A county-year unbalanced sample drawn from World Bank and multinational CDM project data was analyzed using panel logistic and Poisson regression. Panel regression results show that high-energy intensity and low renewable electricity output as an environmental pillar positively affect CDM implementation. Unemployment and undernourishment as a social pillar positively affect CDM whereas low government effectiveness and the high rule of law positively affect CDM. In the results of zero-inflated Poisson regression, the direction of government effectiveness was upturned. The findings have broadened and deepened the ESG pillar based on the institutional theory and emphasized sustainable development rather than economic outputs. (shrink)

Identifying biomarkers of developing mental disorder is crucial to improving early identification and treatment—a key strategy for reducing the burden of mental disorders. Cross-frequency coupling between two different frequencies of neural oscillations is one such promising measure, believed to reflect synchronization between local and global networks in the brain. Specifically, in adults phase–amplitude coupling (PAC) has been shown to be involved in a range of cognitive processes, including working and long-term memory, attention, language, and fluid intelligence. Evidence suggests that (...) increased PAC mediates both temporary and lasting improvements in working memory elicited by transcranial direct-current stimulation and reductions in depressive symptoms after transcranial magnetic stimulation. Moreover, research has shown that abnormal patterns of PAC are associated with depression and schizophrenia in adults. PAC is believed to be closely related to cortico-cortico white matter (WM) microstructure, which is well established in the literature as a structural mechanism underlying mental health. Some cognitive findings have been replicated in adolescents and abnormal patterns of PAC have also been linked to ADHD in young people. However, currently most research has focused on cross-sectional adult samples. Whereas initial hypotheses suggested that PAC was a state-based measure due to an early focus on cognitive, task-based research, current evidence suggests that PAC has both state-based and stable components. Future longitudinal research focusing on PAC throughout adolescent development could further our understanding of the relationship between mental health and cognition and facilitate the development of new methods for the identification and treatment of youth mental health. (shrink)

The vigorous development of online education has produced massive amounts of education data. How to mine and analyze education big data has become an urgent problem in the field of education and big data knowledge engineering. As for the dynamic learning data, knowledge tracing aims to track learners’ knowledge status over time by analyzing the learners’ exercise data, so as to predict their performance in the next time step. Deep learning knowledge tracking performs well, but they mainly model the (...) knowledge components while ignoring the personalized information of questions and learners, and provide limited interpretability in the interaction between learners’ knowledge status and questions. A context-aware attentive knowledge query network model is proposed in this paper, which combines flexible neural network models with interpretable model components inspired by psychometric theory. We use the Rasch model to regularize the embedding of questions and learners’ interaction tuples, and obtain personalized representations from them. In addition, the long-term short-term memory network and monotonic attention mechanism are used to mine the contextual information of learner interaction sequences and question sequences. It can not only retain the ability to model sequences, but also use the monotonic attention mechanism with exponential decay term to extract the hidden forgetting behavior and other characteristics of learners in the learning process. Finally, the vector dot product is used to simulate the interaction between the learners’ knowledge state and questions to improve the interpretability. A series of experimental results on 4 real-world online learning datasets show that CAKQN has the best performance, and its AUC value is improved by an average of 2.945% compared with the existing optimal model. Furthermore, the CAKQN proposed in this paper can not only track learners’ knowledge status like other models but also model learners’ forgetting behavior. In the future, our research will have high application value in the realization of personalized learning strategies, teaching interventions, and resource recommendations for intelligent online education platforms. (shrink)

A network of gene regulation organized in a hierarchical and combinatorial manner is crucially involved in the development of the neural network, and has to be considered one of the main substrates of genetic change in its evolution. Though qualitative features may emerge by way of the accumulation of rather unspecific quantitative changes, it is reasonable to assume that at least in some cases specific combinations of regulatory parts of the genome initiated new directions of evolution, leading to (...) novel capabilities of the brain. These notions are applied, in this paper, to the evolution of the capability of cognition-based human empa­thy. It is suggested that it has evolved as a secondary effect of the evolution of strategic thought. Development of strategies depends on abstract representations of one’s own pos­sible future states in one’s own brain to allow assessment of their emotional desirability, but also on the representation and emotional evaluation of possible states of others, allowing anticipation of their behaviour. This is best achieved if representations of others are con­nected to one’s own emotional centres in a manner similar to self-representations. For this reason, the evolution of the human brain is assumed to have established representations with such linkages. No group selection is involved, because the quality of strategic thought affects the fitness of the individual. A secondary effect of this linkage is that both the actual states and the future perspectives of others elicit vicarious emotions, which may contribute to the motivations of altruistic behaviour. (shrink)

In this chapter, I will discuss employing exemplars in moral and character education promoting virtue development with the involvement of embodiment. Virtue ethicists propose two phases of virtue development: early virtue habituation and later phronesis cultivation. I will overview prior research on the mechanism of habituation at the biological and neural levels to examine why embodiment is fundamental during the first phase, virtue habituation. Then, I will review recent philosophical and psychological studies about the nature of (...) phronesis, i.e., practical wisdom, to consider how educational activities cultivate it. Based on these preliminary examinations of the course of virtue development, I will examine the previous literature about the role and importance of using exemplars in moral and character education involving concrete and interactive activities with the exemplars involving embodiment during the learning process. Finally, I will draw upon the Self-determination theory about supporting students’ need for autonomy, competence, and relatedness to consider a way to improve the effectiveness of exemplar-applied education along with embodiment. After examining the relevant theoretical frameworks mentioned above, I will discuss several practical guidelines for using exemplars involving character embodiment for virtue development. (shrink)

Philosophy of science is positioned to make distinctive contributions to cognitive science by providing perspective on its conceptual foundations and by advancing normative recommendations. The philosophy of science I embrace is naturalistic in that it is grounded in the study of actual science. Focusing on explanation, I describe the recent development of a mechanistic philosophy of science from which I draw three normative consequences for cognitive science. First, insofar as cognitive mechanisms are information‐processing mechanisms, cognitive science needs an account (...) of how the representations invoked in cognitive mechanisms carry information about contents, and I suggest that control theory offers the needed perspective on the relation of representations to contents. Second, I argue that cognitive science requires, but is still in search of, a catalog of cognitive operations that researchers can draw upon in explaining cognitive mechanisms. Last, I provide a new perspective on the relation of cognitive science to brain sciences, one which embraces both reductive research on neural components that figure in cognitive mechanisms and a concern with recomposing higher‐level mechanisms from their components and situating them in their environments. (shrink)

Recently, the mechanistic framework of active inference has been put forward as a principled foundation to develop an overarching theory of consciousness which would help address conceptual disparities in the field (Wiese 2018 ; Hohwy and Seth 2020 ). For that promise to bear out, we argue that current proposals resting on the active inference scheme need refinement to become a process theory of consciousness. One way of improving a theory in mechanistic terms is to use formalisms such as computational (...) models that implement, attune and validate the conceptual notions put forward. Here, we examine how computational modelling approaches have been used to refine the theoretical proposals linking active inference and consciousness, with a focus on the extent and success to which they have been developed to accommodate different facets of consciousness and experimental paradigms, as well as how simulations and empirical data have been used to test and improve these computational models. While current attempts using this approach have shown promising results, we argue they remain preliminary in nature. To refine their predictive and structural validity, testing those models against empirical data is needed i.e., new and unobserved neural data. A remaining challenge for active inference to become a theory of consciousness is to generalize the model to accommodate the broad range of consciousness explananda; and in particular to account for the phenomenological aspects of experience. Notwithstanding these gaps, this approach has proven to be a valuable avenue for theory advancement and holds great potential for future research. (shrink)

How can we explain the intentional nature of an expert’s actions, performed without immediate and conscious control, relying instead on automatic cognitive processes? How can we account for the differences and similarities with a novice’s performance of the same actions? Can a naturalist explanation of intentional expert action be in line with a philosophical concept of intentional action? Answering these and related questions in a positive sense, this dissertation develops a three-step argument. Part I considers different methods of explanations in (...) cognitive neuroscience (Bennett & Hacker’s philosophical, conceptual analysis; Marr’s three levels of explanation; Neural Correlates of Consciousness research; mechanistic explanation), defending ‘mechanistic explanation’ as a method that provides the necessary tools for integrating interdisciplinary insights into human action. Furthermore, a dynamic, explanatory mechanism allows the assessment of the impact of learning and development on expert action in a valuable way that other methods don’t. Part II continues by scrutinizing several cognitive neuroscientific theories of learning and development (neuroconstructivism; dual-processing theories; simulation theory; extended mind/cognition hypothesis), arguing for the complex interactions between different types of processing and different action representations involved in expert action performances. Moreover, according to our discussion of a particular ‘simulation theory’ these interactions can be influenced in several ways with the use of language, allowing an agent to configure a specific action representation for performance at a later stage. The results of Parts I and II are then applied in Part III to a parallel discussion of philosophical analyses of intentional action (discussing i.a. Frankfurt, Bratman, Pacherie and Ricoeur) and cognitive neuroscientific insights in it. Both approaches are found to converge in emphasizing the importance for an expert to develop stable patterns of actions that comply maximally not only with his intentions, but also with his motor expertise and with situational conditions. Consequently, his actions – automatic, or not – rely on this ‘sculpted space of actions’. (shrink)

This paper aims to offer a new view of the role of connectionist models in the study of human cognition through the conceptualization of the history of connectionism – from the simplest perceptrons to convolutional neural nets based on deep learning techniques, as well as through the interpretation of criticism coming from symbolic cognitive science. Namely, the connectionist approach in cognitive science was the target of sharp criticism from the symbolists, which on several occasions caused its marginalization and almost (...) complete abandonment of its assumptions in the study of cognition. Criticisms have mostly pointed to its explanatory inadequacy as a theory of cognition or to its biological implausibility as a theory of implementation, and critics often focused on specific shortcomings of some connectionist models and argued that they apply to connectionism in general. In this paper, we want to show that both types of critique are based on the assumption that the only valid explanations in cognitive science are instances of homuncular functionalism and that by removing this assumption and by adopting an alternative methodology – exploratory mechanistic strategy, we can reject most objections to connectionism as irrelevant, explain the progress of connectionist models despite their shortcomings and sketch the trajectory of their future development. By adopting mechanistic explanations and by criticizing functionalism, we will reject the objections of explanatory inadequacy, by characterizing connectionist models as generic rather than concrete mechanisms, we will reject the objections of biological implausibility, and by attributing the exploratory character to connectionist models we will show that practice of generalizing current to general failures of connectionism is unjustified. (shrink)