In this paper I argue that radiological attempts to elucidate the properties of self -- an endeavor currently popular in the social neurosciences -- are fraught with conceptual difficulties. I first discuss several philosophical criteria that increase the chances we are posing the “right” questions to nature. I then discuss whether these criteria are met when empirical efforts are directed at one of the central constructs in the social sciences – the human self. In particular, I consider whether recent attempts (...) to map the neural correlates of self and its assumed properties using brain scanning technology satisfy the conceptual conditions minimally required to ask well-formed, theoretically satisfying questions of nature. I conclude that much theoretical work remains to be done. (shrink)

Contemplative scholarship has recently reoriented attention towards the neuroscientific study of the soteriological ambition of Buddhist practice, 'awakening'. This article evaluates the project of seeking neural correlates for awakening. Key definitional and operational issues are identified demonstrating that: the nature of awakening is highly contested both within and across Buddhist traditions; the meaning of awakening is both context- and concept-dependent; and awakening may be non-conceptual and ineffable. It is demonstrated that operationalized secular conceptions of awakening, divorced from soteriological and (...) cultural factors, have little relationship to traditional Buddhist construct(s) of awakening. This article identifies methodological issues for secular conceptions of awakening concerning introspection and neuroimaging yet demonstrates also the value of recent advancements in empirical first-person phenomenology for attenuating introspective bias. Overall, it is contended that significant problems arise when decontextualizing awakening and placing it within a scientific naturalistic framework. Careful attention to the definitional, operational, and methodological neuroscientific obstacles identified herein is required in the responsible approach to the investigation of awakening states. (shrink)

A common misunderstanding of the selected effects theory of function is that natural selection operating over an evolutionary time scale is the only functionbestowing process in the natural world. This construal of the selected effects theory conflicts with the existence and ubiquity of neurobiological functions that are evolutionary novel, such as structures underlying reading ability. This conflict has suggested to some that, while the selected effects theory may be relevant to some areas of evolutionary biology, its relevance to neuroscience is (...) marginal. This line of reasoning, however, neglects the fact that synapses, entire neurons, and potentially groups of neurons can undergo a type of selection analogous to natural selection operating over an evolutionary time scale. In the following, I argue that neural selection should be construed, by the selected effect theorist, as a distinct type of function-bestowing process in addition to natural selection. After explicating a generalized selected effects theory of function and distinguishing it from similar attempts to extend the selected effects theory, I do four things. First, I show how it allows one to identify neural selection as a distinct function-bestowing process, in contrast to other forms of neural structure formation such as neural construction. Second, I defend the view from one major criticism, and in so doing I clarify the content of the view. Third, I examine drug addiction to show the potential relevance of neural selection to neuroscientific and psychological research. Finally, I endorse a modest pluralism of function concepts within biology. (shrink)

Researchers in the cognitive sciences often seek neural correlates of psychological constructs. In this paper, I argue that even when these correlates are discovered, they do not always lead to reductive outcomes. To this end, I examine the psychological construct of a critical period and briefly describe research identifying its neural correlates. Although the critical period is correlated with certain neural mechanisms, this does not imply that there is a reductionist relationship between this psychological construct and its (...) neural correlates. Instead, this case study suggests that there may be many-to-many psychological-neural mappings, not just one-to-one or even one-to-many relations between psychological kinds and types of neural mechanisms. (shrink)

This paper adopts two modeling tools, namely, multiple linear regression (MLR) and artificial neural networks (ANNs), to predict the concentrations of heavy metals (zinc, boron, and manganese) in surface waters of the Oued Inaouen watershed flowing towards Inaouen, using a set of physical-chemical parameters. XLStat was employed to perform multiple linear and nonlinear regressions, and Statista 10 was chosen to construct neural networks for modeling and prediction. The effectiveness of the ANN- and MLR-based stochastic models was assessed by (...) the determination coefficient (R²), the sum squared error (SSE) and a review of fit graphs. The results demonstrate the value of ANNs for prediction modeling. Drawing on supervised learning and back propagation, the ANN-based prediction models adopt an architecture of [18-15-1] for zinc, [18-11-1] for manganese, and [18-8-1] for boron, and perform effectively with a single cached layer. It was found that the MLR-based prediction models are substantially less accurate than those based on the ANNs. In addition, the physical-chemical parameters being investigated are nonlinearly correlated with the levels of heavy metals in the surface waters of the Oued Inaouen watershed flowing towards Inaouen. (shrink)

Abstract: This research delves into the utilization of Artificial Neural Networks (ANNs) as a powerful tool for predicting the overall ratings of books by leveraging a diverse set of attributes. To achieve this, we employ a comprehensive dataset sourced from Goodreads, enabling us to thoroughly examine the intricate connections between the different attributes of books and the ratings they receive from readers. In our investigation, we meticulously scrutinize how attributes such as genre, author, page count, publication year, and reader (...) reviews influence a book's overall rating. Through rigorous analysis and experimentation, we construct an advanced ANN model tailored for predictive analysis in the realm of book ratings. The outcomes of our study reveal the remarkable potential of ANNs in this domain. The ANN model exhibits an impressive level of accuracy when it comes to forecasting book ratings, underlining the efficacy and promise of artificial neural networks in enhancing our understanding and prediction of book evaluations. This research opens up new avenues for leveraging machine learning techniques to gain deeper insights into the dynamics of book ratings and reader preferences. (shrink)

We contest the unsubstantiated assumption of both materialists and non-materialist that the ontological status they propose applies to all humans and that the competing claim is false for all - ie we reject both the claim of non-materialists that all humans share the same fundamental aspect of having a "non-material consciousness" (nmc), as well as the contrasting claim of materialists that none do (being fully material as according to eliminative materialists/reductive physicalists etc). Instead, the basic proposition of this paper, our (...) ‘ontological conecture’ (OC) - an updated version of our 1998 website article “Mindless Materialists” - is that the central tenets of proponents on both sides are true, but only regarding themselves. A signature feature of nmc is that brains associated to it are capable of knowing of its existence directly, and as a corollary we would propose that if a sophisticated brain does not know it is associated to nmc then most likely this is because it is in fact not so associated. Thus, in accordance with our OC we will consider a brain’s statements on this issue (that it does or does not possess nmc) as not merely its ‘philosophical position’ but as an authoritative statement, a reflection of an ontological fact. Furthermore, we propose that only those who possess nmc are capable of understanding what it is, so that although they are well-qualified to know directly that they themselves possess it, in contrast those who lack nmc and possess only material consciousness (mc) cannot even comprehend what non-materiality means - they will understandably tend to consider it a non-existent absurdity, which contention would be quite correct in a purely-material reality, such as they effectively inhabit. -/- Terminology reflecting ontological status: Since according to our OC those brains which categorize themselves as non-materialists are presumed to indeed be associated to nmc, for usefulness in phraseology we’ll refer to people whose brain is so associated as being nmc’s (not merely “espousing nmc”). Materialists say they are conscious but that theirs is a material consciousness (abbrev: “mc”) , and since according to our OC we accept this self-determination we will refer to them as being “mc’s” or being materialist (not merely “espousing materialism”). -/- Are materialists mindless or are non-materialists delusional? Notoriously, it is impossible to prove that one possesses nmc (which is a sort of corollary to the fact that it is directly self-known) - being non-material, nmc cannot be detected via the scientific method. However just as those with nmc cannot prove they possess it so too one cannot prove or determine via measurement that materialists do not possess it. Nevertheless we feel that our OC is the simplest solution to the conundrum of how there are materialists if a brain can directly sense its associated nmc. In sum, although of course it is impossible to prove that any particular person possesses or lacks nmc, given all the above our OC considers - as stated by the title of this paper - a brain’s self-identification as "materialist" or “non-materialist” (dualist, panpsychist, idealist etc) as reflecting the absence or presence of an associated real non-material awareness/consciousness, rather than merely as a statement of a philosophical stance. An alternative solution is implicit in the above - that all humans possess nmc just that materialists are those whose brains lack the awareness of theirs, or that the brain-aspect which communicates to others has no access to it. -/- Towards developing more constructive dialogue between mc’s/nmc’s, and greater self-confidence and independence among nmc’s in the face of materialist dominance of the intellectual-climate: A large part of the overall paper is devoted to not just pointing out the futility of communication of the sort usually engaged in - ie based on the erroneous underlying assumption that both sides of the debate are ontologically the same - but also suggesting ways to make the debate less frustrating by a recognition of the OC. Greater clarity in discussions can be achieved partly via a deeper understanding of the different meanings the same term might have to nmc’s/mc’s and suggestions relevant the construction of a sort of translation algorithm to utilize in discussions (as a simple example, substituting ‘higher-level cognitive material brain-processes’ for ‘mind’ when that term is used by an mc); by the frank statement by nmc’s engaged in dialogue with mc’s that the existence of nmc is not up for discussion; by learning how the existence of our nmc colors the meaning we give to certain terms in ways we were not aware of, meanings which are absent for mc’s; and in general via the recognition by the nmc that the dialogue is with a person lacking nmc. -/- Perhaps materialists are right: In the interest of ‘reciprocity’, the paper also offers an alternative/opposing view to the central proposition, to the effect that the materialist claim is correct, and it is a defect of brain wiring or structure which is the source of an active illusion of "self-awareness" underlying the philosophical claims of non-materialists. A prospective source of this illusion is offered - an analog of the sense of presence experienced during ‘sleep paralysis’. (shrink)

In this dissertation, I address the question of how to construct scientific theories of emotions that are both conceptually sound and empirically fruitful. To do this, I offer an analysis of the main challenges scientific theories of emotions face, and I propose a meta-theoretical framework to construct scientific concepts of emotions as explications of folk emotion concepts. Part I discusses the main challenges theories of emotions in psychology and neuroscience encounter. The first states that a proper scientific theory of emotions (...) must explain all and only the phenomena under the vernacular term ‘emotion’ with a common set of conceptual resources and under an overarching generic concept of emotion. The second demands that each emotion category corresponds to well-coordinated sets of neural, physiological, and behavioral patterns of responses. I argue that none of the best contemporary theories of emotions in psychology and neuroscience overcomes these challenges. As a result, a new theory of emotions is required. In Part II, I develop the meta-theoretical framework to construct a theory of emotions that overcomes the challenges above. First, I propose a pluralistic account of scientific kinds based on different patterns of projection that various disciplines may take to justify inductive inferences. These are essentialist, historical, and social patterns. Each of these patterns provides a framework to construct different types of scientific concepts. I argue that among the frameworks for scientific kinds available, the one that is best suited to explicate emotion concepts is a functional framework. Consequently, I conclude by recommending scientists pursue functionalist theories of emotions over essentialist, historical, or social theories. (shrink)

A rigorous approach to the study of the mind–body problem is suggested. Since humans are able to talk about consciousness (produce phenomenal judgments), it is argued that the study of neural mechanisms of phenomenal judgments can solve the hard problem of consciousness. Particular methods are suggested for: (1) verification and falsification of materialism; (2) verification and falsification of interactionism; (3) falsification of epiphenomenalism and parallelism (verification is problematic); (4) verification of particular materialistic theories of consciousness; (5) a non-Turing test (...) for machine consciousness. A complex research program is constructed that includes studies of intelligent machines, numerical models of human and artificial creatures, language, neural correlates of con- sciousness, and quantum mechanisms in brain. (shrink)

The debate about the ethical implications of Artificial Intelligence dates from the 1960s :741–742, 1960; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by Neural Networks and Machine Learning techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on (...) principles—the ‘what’ of AI ethics —rather than on practices, the ‘how.’ Awareness of the potential issues is increasing at a fast rate, but the AI community’s ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs. (shrink)

In recent years, the scientific study of meditation and psychedelic drugs has seen remarkable developments. The increased focus on meditation in cognitive neuroscience has led to a cross-cultural classification of standard meditation styles validated by functional and structural neuroanatomical data. Meanwhile, the renaissance of psychedelic research has shed light on the neurophysiology of altered states of consciousness induced by classical psychedelics, such as psilocybin and LSD, whose effects are mainly mediated by agonism of serotonin receptors. Few attempts have been made (...) at bridging these two domains of inquiry, despite intriguing evidence of overlap between the phenomenology and neurophysiology of meditation practice and psychedelic states. In particular, many contemplative traditions explicitly aim at dissolving the sense of self by eliciting altered states of consciousness through meditation, while classical psychedelics are known to produce significant disruptions of self-consciousness, a phenomenon known as drug-induced ego dissolution. In this article, we discuss available evidence regarding convergences and differences between phenomenological and neurophysiological data on meditation practice and psychedelic drug-induced states, with a particular emphasis on alterations of self-experience. While both meditation and psychedelics may disrupt self-consciousness and underlying neural processes, we emphasize that neither meditation nor psychedelic states can be conceived as simple, uniform categories. Moreover, we suggest that there are important phenomenological differences even between conscious states described as experiences of self-loss. As a result, we propose that self-consciousness may be best construed as a multidimensional construct, and that “self-loss,” far from being an unequivocal phenomenon, can take several forms. Indeed, various aspects of self-consciousness, including narrative aspects linked to autobiographical memory, self-related thoughts and mental time travel, and embodied aspects rooted in multisensory processes, may be differently affected by psychedelics and meditation practices. Finally, we consider long-term outcomes of experiences of self-loss induced by meditation and psychedelics on individual traits and prosocial behavior. We call for caution regarding the problematic conflation of temporary states of self-loss with “selflessness” as a behavioral or social trait, although there is preliminary evidence that correlations between short-term experiences of self-loss and long-term trait alterations may exist. (shrink)

Almost everyone who has a limb amputated will experience a phantom limb--the vivid impression that the limb is not only still present, but in some cases, painful. There is now a wealth of empirical evidence demonstrating changes in cortical topography in primates following deafferentation or amputation, and this review will attempt to relate these in a systematic way to the clinical phenomenology of phantom limbs. With the advent of non-invasive imaging techniques such as MEG (magnetoencephalogram) and functional MRI, topographical reorganization (...) can also be demonstrated in humans, so that it is now possible to track perceptual changes and changes in cortical topography in individual patients. We suggest, therefore, that these patients provide a valuable opportunity not only for exploring neural plasticity in the adult human brain but also for understanding the relationship between the activity of sensory neurons and conscious experience. We conclude with a theory of phantom limbs, some striking demonstrations of phantoms induced in normal subjects, and some remarks about the relevance of these phenomena to the question of how the brain constructs a 'body image.'. (shrink)

In the late summer of 1998, the authors, a cognitive scientist and a logician, started talking about the relevance of modern mathematical logic to the study of human reasoning, and we have been talking ever since. This book is an interim report of that conversation. It argues that results such as those on the Wason selection task, purportedly showing the irrelevance of formal logic to actual human reasoning, have been widely misinterpreted, mainly because the picture of logic current in psychology (...) and cognitive science is completely mistaken. We aim to give the reader a more accurate picture of mathematical logic and, in doing so, hope to show that logic, properly conceived, is still a very helpful tool in cognitive science. The main thrust of the book is therefore constructive. We give a number of examples in which logical theorizing helps in understanding and modeling observed behavior in reasoning tasks, deviations of that behavior in a psychiatric disorder (autism), and even the roots of that behavior in the evolution of the brain. (shrink)

In this paper I argue that much of the confusion and mystery surrounding the concept of "self" can be traced to a failure to appreciate the distinction between the self as a collection of diverse neural components that provide us with our beliefs, memories, desires, personality, emotions, etc (the epistemological self) and the self that is best conceived as subjective, unified awareness, a point of view in the first person (ontological self). While the former can, and indeed has, been (...) extensively studied by researchers of various disciplines in the human sciences, the latter most often has been ignored -- treated more as a place holder attached to a particular predicate of interest (e.g., concept, reference, deception, esteem, image, regulation, etc). These two aspects of the self, I contend, are not reducible -- one being an object (the epistemological self) and the other a subject (the ontological self). Until we appreciate the difficulties of applying scientific methods and analysis to what cannot be reduced to an object of inquiry without stripping it of its essential aspect (its status as subject), progress on the "self", taken as a pluralistic construct, will continue to address only one part of the problems we face in understanding this most fundamental aspect of human experience. (shrink)

How should a machine intelligence perform unsupervised structure discovery over streams of sensory input? One approach to this problem is to cast it as an apperception task [1]. Here, the task is to construct an explicit interpretable theory that both explains the sensory sequence and also satisfies a set of unity conditions, designed to ensure that the constituents of the theory are connected in a relational structure. However, the original formulation of the apperception task had one fundamental limitation: it assumed (...) the raw sensory input had already been parsed using a set of discrete categories, so that all the system had to do was receive this already-digested symbolic input, and make sense of it. But what if we don't have access to pre-parsed input? What if our sensory sequence is raw unprocessed information? The central contribution of this paper is a neuro-symbolic framework for distilling interpretable theories out of streams of raw, unprocessed sensory experience. First, we extend the definition of the apperception task to include ambiguous (but still symbolic) input: sequences of sets of disjunctions. Next, we use a neural network to map raw sensory input to disjunctive input. Our binary neural network is encoded as a logic program, so the weights of the network and the rules of the theory can be solved jointly as a single SAT problem. This way, we are able to jointly learn how to perceive (mapping raw sensory information to concepts) and apperceive (combining concepts into declarative rules). (shrink)

The nature of the cognition-motor interface has been brought to prominence by Butterfill & Sinigaglia, who argue that the representations employed by the cognitive and motor systems should not be able to interact with each other. Here I argue that recent empirical evidence concerning the interface contradicts several of the assumptions incorporated in Butterfill & Sinigaglia’s account, and I seek to develop a theoretical picture that will allow us to explain the structure of the interface presented by this evidence. The (...) central idea is that neural plasticity incorporates metarepresentational rules for constructing representational systems and linking them. The structure of the cognition-motor interface is constructed flexibly during development and skill learning based on information processing demands. (shrink)

This paper attempts to answer a central question in unsupervised learning: what does it mean to “make sense” of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the causal theory – objects, properties, and laws – must be integrated into a coherent whole. On our account, making sense of sensory input is a (...) type of program synthesis, but it is unsupervised program synthesis. Our second contribution is a computer implementation, the Apperception Engine, that was designed to satisfy the above requirements. Our system is able to produce interpretable human-readable causal theories from very small amounts of data, because of the strong inductive bias provided by the unity conditions. A causal theory produced by our system is able to predict future sensor readings, as well as retrodict earlier readings, and impute (fill in the blanks of) missing sensory readings, in any combination. In fact, it is able to do all three tasks simultaneously. We tested the engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction intelligence tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The Apperception Engine performs well in all these domains, significantly out-performing neural net baselines. We note in particular that in the sequence induction intelligence tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve intelligence tests, but a general-purpose system that was designed to make sense of any sensory sequence. (shrink)

In this article, the results of conceptualization of the definition of mind as an object of interdisciplinary applied research are described. The purpose of the theoretical analysis is to generate a methodological discourse suitable for a functional understanding of the mind in the context of the problem of natural language processing as one of the components of developments in the field of artificial intelligence. The conceptual discourse was realized with the help of the author's method of structural-ontological analysis, and developed (...) in the mainstream of the system-methodological tradition of the school of G.P. Shchedrovitsky and intended for descriptive research of subject areas of interdisciplinary objects of scientific study. As a result of the structural and ontological analysis of the super-system, the relevant place and role of the directly studied system (mind) are determined, and its primary process and material are localized and structural and functional connections are described. At the basic level, the mind is conceptualized as an energy process unfolding in a spatio-temporal environment and accompanied by archetypal structuring of neural impulses into images. The genesis of the system is separately analyzed by constructing a structural-ontological matrix that reflects the initial stage of the development of the mind. The primary process is concretized with the help of hetero- and homeostatic dichotomy, and also the most significant features of the consistent transformation of the material of the system and its ascent to verbal morphology are described. The structural-ontological comparison of the functioning of the verbal intelligence with the preverbal level has been carried out. The transformation of neural impulses of needs into words, as verbal units fixing semiotic values is analyzed. Structural-ontological connections that determine the reactive and prospective characteristics of the functioning of the system are disclosed. The position of the chronological primacy of "semiotic readiness" for language with respect to the debut of the latter as an information-sign model of the environment is argued. The hypothesis of domination at the initial stage of the development of the mind of exopsychic functions over endopsychic ones is formulated. The theoretical substantiation of the hypothesis of the inevitability of the ontology of language in the functional understanding of the mind is given, corresponding structural and ontological arguments are given, including those based on the ideas about the information relationship between affects and needs, according to the views of P.V. Simonov. The arguments are presented in favor of the non-alternative methodology of A. Turing in studies of artificial intelligence. (shrink)

The brain is composed of electrically excitable neuronal networks regulated by the activity of voltage-gated ion channels. Further portraying the molecular composition of the brain, however, will not reveal anything remotely reminiscent of a feeling, a sensation or a conscious experience. In classical physics, addressing the mind–brain problem is a formidable task because no physical mechanism is able to explain how the brain generates the unobservable, inner psychological world of conscious experiences and how in turn those conscious experiences steer the (...) underlying brain processes toward desired behavior. Yet, this setback does not establish that consciousness is non-physical. Modern quantum physics affirms the interplay between two types of physical entities in Hilbert space: unobservable quantum states, which are vectors describing what exists in the physical world, and quantum observables, which are operators describing what can be observed in quantum measurements. Quantum no-go theorems further provide a framework for studying quantum brain dynamics, which has to be governed by a physically admissible Hamiltonian. Comprising consciousness of unobservable quantum information integrated in quantum brain states explains the origin of the inner privacy of conscious experiences and revisits the dynamic timescale of conscious processes to picosecond conformational transitions of neural biomolecules. The observable brain is then an objective construction created from classical bits of information, which are bound by Holevo’s theorem, and obtained through the measurement of quantum brain observables. Thus, quantum information theory clarifies the distinction between the unobservable mind and the observable brain, and supports a solid physical foundation for consciousness research. (shrink)

I defend a theory of mental representation that satisfies naturalistic constraints. Briefly, we begin by distinguishing (i) what makes something a representation from (ii) given that a thing is a representation, what determines what it represents. Representations are states of biological organisms, so we should expect a unified theoretical framework for explaining both what it is to be a representation as well as what it is to be a heart or a kidney. I follow Millikan in explaining (i) in terms (...) of teleofunction, explicated in terms of natural selection. -/- To explain (ii), we begin by recognizing that representational states do not have content, that is, they are neither true nor false except insofar as they both “point to” or “refer” to something, as well as “say” something regarding whatever it is they are about. To distinguish veridical from false representations, there must be a way for these separate aspects to come apart; hence, we explain (ii) by providing independent theories of what I call f-reference and f-predication (the ‘f’ simply connotes ‘fundamental’, to distinguish these things from their natural language counterparts). -/- Causal theories of representation typically founder on error, or on what Fodor has called the disjunction problem. Resemblance or isomorphism theories typically founder on what I’ve called the non-uniqueness problem, which is that isomorphisms and resemblance are practically unconstrained and so representational content cannot be uniquely determined. These traditional problems provide the motivation for my theory, the structural preservation theory, as follows. F-reference, like reference, is a specific, asymmetric relation, as is causation. F-predication, like predication, is a non-specific relation, as predicates typically apply to many things, just as many relational systems can be isomorphic to any given relational system. Putting these observations together, a promising strategy is to explain f-reference via causal history and f-predication via something like isomorphism between relational systems. -/- This dissertation should be conceptualized as having three parts. After motivating and characterizing the problem in chapter 1, the first part is the negative project, where I review and critique Dretske’s, Fodor’s, and Millikan’s theories in chapters 2-4. Second, I construct my theory about the nature of representation in chapter 5 and defend it from objections in chapter 6. In chapters 7-8, which constitute the third and final part, I address the question of how representation is implemented in biological systems. In chapter 7 I argue that single-cell intracortical recordings taken from awake Macaque monkeys performing a cognitive task provide empirical evidence for structural preservation theory, and in chapter 8 I use the empirical results to illustrate, clarify, and refine the theory. (shrink)

Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function (e.g., Piccinini & Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience (MBCN) epitomizes the integrative mechanistic perspective (...) and concentrates the most revolutionary elements of the cognitive neuroscience revolution (Boone & Piccinini, 2016). I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration. (shrink)

In contemporary human brain mapping, it is commonly assumed that the “mind is what the brain does”. Based on that assumption, task-based imaging studies of the last three decades measured differences in brain activity that are thought to reflect the exercise of human mental capacities (e.g., perception, attention, memory). With the advancement of resting state studies, tractography and graph theory in the last decade, however, it became possible to study human brain connectivity without relying on cognitive tasks or constructs. It (...) therefore is currently an open question whether the assumption that “the mind is what the brain does” is an indispensable working hypothesis in human brain mapping. This paper argues that the hypothesis is, in fact, dispensable. If it is dropped, researchers can “meet the brain on its own terms” by searching for new, more adequate concepts to describe human brain organization. Neuroscientists can establish such concepts by conducting exploratory experiments that do not test particular cognitive hypotheses. The paper provides a systematic account of exploratory neuroscientific research that would allow researchers to form new concepts and formulate general principles of brain connectivity, and to combine connectivity studies with manipulation methods to identify neural entities in the brain. These research strategies would be most fruitful if applied to the mesoscopic scale of neuronal assemblies, since the organizational principles at this scale are currently largely unknown. This could help researchers to link microscopic and macroscopic evidence to provide a more comprehensive understanding of the human brain. The paper concludes by comparing this account of exploratory neuroscientific experiments to recent proposals for large-scale, discovery-based studies of human brain connectivity. (shrink)

Decision-making has traditionally been modelled as a serial process, consisting of a number of distinct stages. The traditional account assumes that an agent first acquires the necessary perceptual evidence, by constructing a detailed inner repre- sentation of the environment, in order to deliberate over a set of possible options. Next, the agent considers her goals and beliefs, and subsequently commits to the best possible course of action. This process then repeats once the agent has learned from the consequences of her (...) actions and subsequently updated her beliefs. Under this interpretation, the agent’s body is considered merely as a means to report the decision, or to acquire the relevant goods. However, embodied cognition argues that an agent’s body should be understood as a proper part of the decision-making pro- cess. Accepting this principle challenges a number of commonly held beliefs in the cognitive sciences, but may lead to a more unified account of decision-making. This thesis explores an embodied account of decision-making using a recent frame- work known as predictive processing. This framework has been proposed by some as a functional description of neural activity. However, if it is approached from an embodied perspective, it can also offer a novel account of decision-making that ex- tends the scope of our explanatory considerations out beyond the brain and the body. We explore work in the cognitive sciences that supports this view, and argue that decision theory can benefit from adopting an embodied and predictive perspective. (shrink)

The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge data set, (...) the cybersecurity warning systems index system is first constructed, then the index factors are picked and measured, and finally, the situation evaluation is done.Numerous bio-inspired techniques were used to enhance the productivity of an IDS by lowering the data dimensionality and deleting unnecessary and noisy input. The Grey Wolf Optimization algorithm (GWO) is a developed bio-inspired algorithm that improves the efficacy of the IDS in detecting both regular and abnormal congestion in the network. The smart initialization step integrates the different pre-processing strategies to make sure that informative features are incorporated in the early development stages, has been improved. Researchers pick multi-source material in a big data environment for the identification and verification of index components and present a parallel reduction approach based on the classification significance matrix to decrease data underlying data characteristics. For the simulation of this situation, grey wolf optimization and whale optimization were combined to detect the attack prevention and the deep learning approach was presented. Utilizing system software plagiarism, the TensorFlow deep neural network is intended to classify stolen software. To reduce the noise from the signal and to zoom the significance of each word in the perspective of open-source plagiarism, the tokenization and weighting feature approaches are utilized. Malware specimens have been collected from the Mailing database for testing purposes. The experimental findings show that the suggested technique for measuring cyber security hazards in IoT has superior classification results to existing methods. Hence to detect the attack prevention in IoT process Whale with Grey wolf optimization (WGWO) and deep convolution network is used. (shrink)

Perceptual neuroscience has identified mechanisms of perceptual grouping which account for the ways in which visual sensitivity to ordered structure and regularities expresses itself, in behavior and in the brain. The need to actively construct order, notably representations of objects in depth, is mandated as soon as visual signals reach the retina, given the occlusion of retinal signals by retinal veins and other retinal elements or blur. Multiple stages of neural processing transform fragmented signals into visual key representations of (...) 3D scenes that can be used to control effective behaviors. Since our survival depends on our ability to pick up order in the physical world, and since we conceive the physical world as an ordered one, our perception must somehow be sensitive to order. The articles from this collection illustrate how texture dissimilarity, boundary completion, surface filling-in, and figure-ground segregation generate perceptual order. (shrink)

The abstract basis of modern computation is the formal description of a finite state machine, the Universal Turing Machine, based on manipulation of integers and logic symbols. In this contribution to the discourse on the computer-brain analogy, we discuss the extent to which analog computing, as performed by the mammalian brain, is like and unlike the digital computing of Universal Turing Machines. We begin with ordinary reality being a permanent dialog between continuous and discontinuous worlds. So it is with computing, (...) which can be analog or digital, and is often mixed. The theory behind computers is essentially digital, but efficient simulations of phenomena can be performed by analog devices; indeed, any physical calculation requires implementation in the physical world and is therefore analog to some extent, despite being based on abstract logic and arithmetic. The mammalian brain, comprised of neuronal networks, functions as an analog device and has given rise to artificial neural networks that are implemented as digital algorithms but function as analog models would. Analog constructs compute with the implementation of a variety of feedback and feedforward loops. In contrast, digital algorithms allow the implementation of recursive processes that enable them to generate unparalleled emergent properties. We briefly illustrate how the cortical organization of neurons can integrate signals and make predictions analogically. While we conclude that brains are not digital computers, we speculate on the recent implementation of human writing in the brain as a possible digital path that slowly evolves the brain into a genuine (slow) Turing machine. (shrink)

ABSTRACT The individual-community relationship has always been one of the most fundamental topics of social sciences. In sociology, this is known as the micro-macro relationship while in economics it refers to the processes, through which, individual actions lead to macroeconomic phenomena. Based on philosophical discourse and systems theory, many sociologists even use the term "emergence" in their understanding of micro-macro relationship, which refers to collective phenomena that are created by the cooperation of individuals, but cannot be reduced to individual actions. (...) "Emergence" theories attempt to explain the nature of society as a complex system by examining how individuals and their relationships lead to the creation of integrated and macro-social phenomena such as markets, educational systems, cultural beliefs, and shared social practices. As a prelude to activity, every researcher has to answer the question from the methodological point of view, how is it possible to study the behavior of social groups and how can we gain knowledge about the laws related to social groups? Anyone who deals with humanities and social sciences or any reality and phenomenon that affects human beings, inevitably deals with the reality that is emerging. In fact, emergence occurs when one level of reality emerges radically from another level. Examples of emergent levels of reality include how the mind emerges from the body; or the way society emerges from human beings. Therefore, when there is an emerging factor, different scientific disciplines should be used, because it is inevitable to talk about social affairs, psychology and neurobiology, as well as physical and even chemical. -/- INTRODUCTION Since its beginning in the 19th century, sociology has faced the fundamental question of what a social phenomena stands for? At a glance, sociologists deal with the study of social groups, collective behavior, institutions, social structures, social networks, and social dynamics, and ultimately all such phenomena are composed of individuals. Therefore, social phenomena seem to have no independent ontological status, and in this case, sociology is ultimately about reducible individuals. Different approaches about the micro-macro relationship, using the concept of "emergence", argue that although collective phenomena are created by individuals, they cannot be reduced to individual action. The term "emergence" is used to distinguish "emergent results" from " Procedural results". Results are called Procedural that can only be calculated by adding or subtracting the causes that act together. On the contrary, they are emergent outcomes that are qualitatively novel compared to the causes from which they originate. An example of such emergent results is mental characteristics that emerge from neural processes, but are not considered among the characteristics of neural processes of components - which are their origin. Philosophical debates about emergentism and reductionism focus on the mind-brain relationship, but as many philosophers have noted, they can be generalized to apply to any hierarchical set of features. The cognitive revolution reactivated a 19th century controversy between theorists of Identity and dualism. The theorists of Identity believe in reductionist materialism and elimination of metaphysics, according to which the mind is nothing more than the biological, while the dualists believe that the mind and the brain are distinct from each other. Emergence has been considered as a third way between the theories of Identity and dualism. -/- METHODOLOGY Interdisciplinarity Is fundamentally indicated by the dominance of open systems as well as social and emerging systems at different levels. Therefore, as long as we are facing a new emergentism, there will necessarily be different fields and an interdisciplinary method. The commonality of social sciences and cognitive sciences on the issue of emergence provides the possibility of interaction and exchange of concepts between these two for deeper research. Therefore, in this research, apart from the concepts of Subvenience and downward causation, we have used multiple realization and wildly disjunctive, which are used to explain the emerging concept in cognitive sciences, including neurobiology, psychology, and philosophy. For analytical and interdisciplinary approaches, after examining the concepts of subvenience, and wildly disjunctive, Individualistic and collectivist emergentism have been reviewed. And then their adequacy is criticized and evaluated in meeting strong emergence conditions. -/- FINDINGS Philosophers of the mind have been able to define the conditions of irreducibility and downward causation, which are essential for strong emergence, by using the principle of subvenience with the help of multiple realization and wildly disjunctive principles. Therefore, by definition, emergentist individualism suffers from the problem of how to explain downward causality and irreducibility, and emergentist collectivism faces the challenge of reification. The analysis of the challenges facing the two methodological approaches of individualism and collectivism leads us to three unresolved issues about the emerging phenomenon, which are realism, downward causality, and mechanism. Despite the differences in the stances of these two approaches regarding the aforementioned triple problems, individualists and collectivists both agree that some social characteristics are reducible; and some others are irreducible due to their complexity, the determination of which, depends on the case study of the mechanism of emergence of each social characteristic. -/- CONCLUSION An emergence that is consistent with reductionism is called a weak emergence. And strong emergence includes the principle of irreducibility and downward causality. According to strong emergence, the characteristics of social phenomena have downward causal power, which cannot be reduced to the causal forces. The principle of subvenience, together with multiple realization and wildly disjunctive, can provide the conditions of irreducibility and downward causality, which are essential conditions of strong emergence. Based on the philosophical analysis of two methodological approaches, individualist and collectivist, it can be concluded that emergentist individualism suffers from the problem of how to explain downward causality and irreducibility, and reification collectivism faces the challenge of reification. The analysis of the problems faced by these two approaches leads us to three unresolved issues, which are realism, downward causality, and mechanism. individualist emergentists acknowledge the existence of emergent social characteristics, but despite this, they believe that these characteristics are not real but merely analytical constructs that require explanation based on individuals and their mutual actions. But collectivists argue that emergentism yields a categorical ontology and supports social realism. They accept the dependence of social characteristics on individuals, but claim that social entities and structures are ontologically independent. Individualists and collectivists both agree that some social characteristics are reducible; and others are irreducible due to their complexity; and the only way to recognize this is to engage in empirical studies of the mechanisms and temporal processes of "emergence" that create social characteristics. -/- NOVELTY The newness of this compact article is as follows: a) Presenting an interdisciplinary approach using the analysis of philosophy of cognitive sciences, including the philosophy of mind, neurobiology and psychology to formulate the issue of social emergence; b) Criticizing individualist and collectivist methodological approaches to the issue of social emergence and explaining the sufficiency and shortcomings of both in providing weak and strong emerging conditions; c) Explaining and categorizing the challenges facing methodological approaches in explaining the phenomenon of social emergence, in three categories: realism, downward causality, and mechanism. BIBLIOGRAPHY Beckermann, A., Flohr, H., & Kim, J. (eds.) (1992). Emergence or reduction? Essays on the prospects of nonreductive physicalism. Berlin: Walter de Gruyter. Bhaskar, R. (1979). The possibility of Naturalism. New York: Routledge. Bhaskar, R., Danermark, B. & Price, L. (2018). Interdisciplinarity and wellbeing: A critical realist general theory of interdisciplinarity. London and New York: Routledge. Blau, P.M. (1981). Introduction: diverse views of social structure and their common denominator. In Blau, P.M. & Merton, R.K. (eds.), Continuity in Structural Inquiry (pp. 1–23). Beverly Hills: Sage. Blau, P.M. (1987). Microprocess and macrostructure. In K.S. Cook (ed.), Social Exchange Theory (pp. 83–100). Newbury Park, CA: Sage. Brooks, R.A., & Maes, P. (eds). (1994). Artificial life IV; proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems. Cambridge, Mass: MIT Press. Clark, A. (1997). Being there: Putting brain, body, and world together again. Cambridge, Mass.: MIT Press. Coleman, J. S. (1987). Microfoundations and macrosocial behavior. In: J. C. Alexander, B. Giesen, R. Munch, & Neil J. Smelser (Eds.). The Micro-Macro Link (pp. 153–73). Berkeley: University of California. Coleman, J. S. (1990). Foundations of social theory. Cambridge, MA: Harvard University Press. Elster, J. (1985). Making sense of Marx. New York: Cambridge University Press. Fodor, J.A. (1974). Special sciences (Or: The disunity of science as a working hypothesis). Synthese, 28(2), 97–115. Fodor, J.A. (1989). Making mind matter more. Philosophical Topics, 17, 59-79. Giddens, A. (1984). The constitution of society: Outline of the theory of structuration. Berkeley: University of California Press. Hempel, C.G. (1965). Aspects of scientific explanation and other essays in the philosophy of science. New York: The Free Press. Horgan, T. (1993). From supervenience to superdupervenience: meeting the demands of a material world. Mind, 102, 555-586. Humphreys, P. (1997). How properties emerge”. Philosophy of Science, 64, 1-17. Kim, J. (1999). Making sense of emergence. Philosophical Studies, 95, 3–36. Kincaid, H. (1997). Individualism and the unity of science. New York: Rowman & Littlefield. Lewis, D.K. (1986). The plurality of worlds. Oxford: Oxford University Press. Nigel, G., & Conte, R. (eds.) (1995). Artificial societies; The computer simulation of social life. London: UCL Press. O’Connor, T. (1994). Emergent properties. American Philosophical Quarterly, 31, 91-104. Popper, K.R. (1968). Conjectures and refutations: The Growth of Scientific Knowledge. New York: Harper & Row. Sawyer, R. K. (2001). Emergence in sociology: contemporary philosophy of mind and some implications for sociological theory. American Journal of Sociology, 107, 551-585. Sawyer, R. K. (2004). The mechanisms of emergence. Philosophy of the Social Sciences, 34, 260–282. Sawyer, R. K. (2005). Social emergence: societies as complex systems. Cambridge: Cambridge University Press. Sawyer, R.K. (2003). Nonreductive individualism: Part 2, Social causation. Philosophy of the Social Sciences, 33, 203–224. Winch, P. (1993). The idea of a social science and its relation to philosophy‭ (SAMT, Trans.). Tehran, Iran: SAMT. (shrink)

The traditional approach to explanation in cognitive neuroscience is realist about psychological constructs, and treats them as explanatory. On the “standard framework,” cognitive neuroscientists explain behavior as the result of the instantiation of psychological functions in brain activity. This strategy is questioned by results suggesting the distribution of function in the brain, the multifunctionality of individual parts of the brain, and the overlap in neural realization of purportedly distinct psychological constructs. One response to this in the field has been (...) to employ the tools of databasing and machine learning to attempt to find and quantify specific correlations between psychological kinds such as ‘memory’ or ‘attention’ (or sub-kinds thereof) and patterns of activity in the brain. I assess the status and prospects of these projects. I argue that current proponents of the project are vague about their aims, vis-à-vis the standard framework, sometimes suggesting substantiation of the framework, sometimes suggesting retaining the framework but revising the ontology of mental constructs, and sometimes suggesting abandonment of the framework. I argue that extant results from within the projects fail to substantiate the standard framework, and propose an alternative. On my view, psychological constructs should not be viewed as explanantia, but instead as heuristic concepts that help us uncover ways that behaviors can vary and the ways that the brain implements those distinctions. I then discuss the normative upshot of these views for databasing and brain mapping projects. (shrink)

Investigators increasingly need high quality face photographs that they can use in service of their scholarly pursuits—whether serving as experimental stimuli or to benchmark face recognition algorithms. Up to now, an index of known face databases, their features, and how to access them has not been available. This absence has had at least two negative repercussions: First, without alternatives, some researchers may have used face databases that are widely known but not optimal for their research. Second, a reliance on databases (...) comprised only of young white faces will lead to science that isn't representative of all the people whose tax contributions, in many cases, make that research possible. The “Face Image Meta-Database” (fIMDb) provides researchers with the tools to find the face images best suited to their research, with filters to locate databases with people of a varied racial and ethnic backgrounds and ages. Problems of representation in face databases are not restricted to race and ethnicity or age – there is a dearth of databases with faces that have visible differences (e.g., scars, port wine stains, and cleft lip and palate). A well-characterized database is needed to support programmatic research into perceivers' attitudes, behaviors, and neural responses to anomalous faces. The “ChatLab Facial Anomaly Database” (CFAD) was constructed to fill this gap, with photographs of faces with visible differences of various types, etiologies, sizes, locations, and that depict individuals from various ethnic backgrounds and age groups. Both the fIMDb and CFAD are available from cliffordworkman[dot]com/resources/. (shrink)

Abstract: Alzheimer's disease (AD) is one of the most common types of dementia. Symptoms appear gradually and end with severe brain damage. People with Alzheimer's disease lose the abilities of knowledge, memory, language and learning. Recently, the classification and diagnosis of diseases using deep learning has emerged as an active topic covering a wide range of applications. This paper proposes examining abnormalities in brain structures and detecting cases of Alzheimer's disease especially in the early stages, using features derived from medical (...) images. The entire brain image was passed on through the transmission of Xception learning architectures. The Convolutional Neural Network (CNN) was constructed with the help of separable convolution layers that It can automatically learn general features from imaging data for classification. (shrink)

The current technoscientific progress has led to a sectorization in the philosophy of science. Today the philosophy of science isn't is informal interested in studying old problems about the general characteristics of scientific practice. The interest of the philosopher of science is the study of concepts, problems and riddles of particular disciplines. Then, within this progress of philosophy of science, neuroscientific research stands out, because it invades issues traditionally addressed by the humanities, such as the nature of consciousness, action, knowledge, (...) normativity, etc. As a result, the new area of interdisciplinary study of neuroscience and philosophy arises: neurophilosophy. This emerging area applies neuroscientific concepts to traditional philosophical questions, limiting their responses to neuroscientific revelations about nervous systems. Neurophilosophy research focuses on problems related to the indirect nature of mind and brain, computational or representative analysis of brain process, relationships between psychological and neuroscientific research, adequate adaptations of physical and philosophical concepts in neuroscience and the place of cognitive functions. Now, the temporal representation of conscious experience and the types of the neural architecture to represent objects in time have aroused scientific interest. Under these interests, we focus on the studies on the temporary triadic structure of phenomenological consciousness in Dan Lloyd and Rick Grush. From Grush’s studies, the importance of Kantian ideas for cognitive neuroscience emerges, due to the active way in which Kant conceived space and time as forms of intuition, within which the mind interprets its experience. Under this perspective, the theoretical arguments of Dennett-Kinsbourne and Eagleman-Sejnowski represent winks in the direction of Kant-Husserl within the neuroscientific goal while considering that the contents provided by the mind included space, objects and perception of causal relationships. Then, theories of cognitive neuroscience are beginning to suggest that these elements are also, as Kant argued, interpretative elaborations provided by mind / brain, and not only content received from outside. In other words, current cognitive neuroscientific theories try to pass from its Humean phase to a Kantian phase. So, the challenge has been to explain that these elements are provided by the mind and the world itself, and how they have the content they have come from. These are lacking in current studies. Filling this gap helps to involve the analysis of the scientist’s experience in his theoretical attitude. In this sense, an investigation under the Kantian-Husserlian approach that involves pure intuitions a priori with the experience of the scientific and neuroscientific concepts represents a ground-breaking. At present, a neurophilosophical study about this does not exist. In this sense, one feasible proposal for research would be based on the application of neuroscientific results of Moser-Britt to philosophical problems of foundational notions in relativistic physics: space, time, space-time, field, etc., under the Kantian-Husserlian approach, which allows to demonstrate the multidisciplinary link between neurophilosophy and physics. This represents a ground-breaking area in current interests in scientific research, with a positive impact in the field of neuroscience, and contributing to the study of abstraction emphasizing the importance of Kant’s Copernican turn and Husserl’s phenomenological ideas in the construction of physical theories. -/- . (shrink)

The topic of this dissertation is the nature of linguistic competence, the capacity to understand and produce sentences of natural language. I defend the empiricist account of linguistic competence embedded in the connectionist cognitive science. This strand of cognitive science has been opposed to the traditional symbolic cognitive science, coupled with transformational-generative grammar, which was committed to nativism due to the view that human cognition, including language capacity, should be construed in terms of symbolic representations and hardwired rules. Similarly, linguistic (...) competence in this framework was regarded as being innate, rule-governed, domain-specific, and fundamentally different from performance, i.e., idiosyncrasies and factors governing linguistic behavior. I analyze state-of-the-art connectionist, deep learning models of natural language processing, most notably large language models, to see what they can tell us about linguistic competence. Deep learning is a statistical technique for the classification of patterns through which artificial intelligence researchers train artificial neural networks containing multiple layers that crunch a gargantuan amount of textual and/or visual data. I argue that these models suggest that linguistic competence should be construed as stochastic, pattern-based, and stemming from domain-general mechanisms. Moreover, I distinguish syntactic from semantic competence, and I show for each the ramifications of the endorsement of a connectionist research program as opposed to the traditional symbolic cognitive science and transformational-generative grammar. I provide a unifying front, consisting of usage-based theories, a construction grammar approach, and an embodied approach to cognition to show that the more multimodal and diverse models are in terms of architectural features and training data, the stronger the case is for the connectionist linguistic competence. I also propose to discard the competence vs. performance distinction as theoretically inferior so that a novel and integrative account of linguistic competence originating in connectionism and empiricism that I propose and defend in the dissertation could be put forward in scientific and philosophical literature. (shrink)

Why are some people capable of sympathizing with and/or committing acts of political violence, such as attacks aimed at innocent targets? Attempts to construct terrorist profiles based on individual and situational factors, such as clinical, psychological, ethnic, and socio-demographic variables, have largely failed. Although individual and situational factors must be at work, it is clear that they alone cannot explain how certain individuals are radicalized. In this paper, we propose that a comprehensive understanding of radicalization and of how it may (...) lead to political violence requires the integration of information across multiple levels of analysis and interdisciplinary perspectives from evolutionary theory, social, personality and cognitive psychology, political science and neuroscience. Characterization of the structural-functional relationships between neural mechanisms and the cognitive and affective psychological processes that underpin group dynamics, interpersonal processes, values and narratives, as well as micro-sociological processes may reveal latent drivers of radicalization and explain why some people turn to extreme political violence. These drivers may not be observable within a single individual level of scientific enquiry. The integrative, multilevel approach that characterizes social neuroscience has the potential to provide theoretical and empirical clarity regarding the antecedents of radicalization and support for extreme violence. (shrink)

Revista Cunoașterea Științifică este o publicație trimestrială din domeniile științei și filosofiei, și domenii conexe de studiu și practică. -/- Cuprins: -/- EDITORIALE / EDITORIALS -/- Dan D. FARCAȘ Limite ale cunoașterii în cuvânt, logică, matematică și teorii Limits of knowledge in words, logic, mathematics and theories Nicolae SFETCU Cunoașterea științifică – Metodologii Scientific knowledge – Methodologies Nicolae SFETCU Știința schimbărilor climatice The science of climate change Nicolae SFETCU Știință sau pseudoștiință? Science or Pseudoscience? -/- ȘTIINȚE NATURALE / NATURAL SCIENCE (...) -/- Nicolae SFETCU Epistemologia gravitației cuantice Epistemology of Quantum Gravity Nicolae SFETCU Fabrica de apă grea Drobeta Turnu Severin: Construcția Drobeta Turnu Severin Heavy Water Plant: Construction Nicolae SFETCU Fabrica de apă grea Drobeta Turnu Severin: Funcționarea și oprirea Drobeta Turnu Severin Heavy Water Plant: Functioning and Shutting Down Albert TORMA PandemiA: Femei și polițiste Pandemia: Women and Policewomen Adrian KLEIN, Robert Neil BOYD Interacțiuni între creier, biocâmpuri și lumea fizică Interactions between the brain, the biofields, and the physical Adrian KLEIN Conectivitatea psiho-neuronală The Psycho-Neural Connectivity Nicolae SFETCU Probleme cu teoria corzilor din gravitația cuantică Problems with String Theory in Quantum Gravity -/- ȘTIINȚE SOCIALE / SOCIAL SCIENCE -/- Miriam MIRCEA Manipularea ca fenomen corelativ al puterii politice – o sinteză Manipulation as a correlative phenomenon of political power – a synthesis Daniela Georgiana GOLES, Oana Ștefania COȘA Repetabilitatea istorica a problemelor de securitate manageriale și noile cerințe in contextul economic actual The historical repeatability of managerial security issues and the new requirements in the current economic context Ionuț IORDACHE Reglementări internaționale privind falimentul International bankruptcy regulations Vlad BARBU, Ionuț IORDACHE, Adrian-Călin CUCULIS, Naciu Costin MERGEANE Rolul statului în relațiile de muncă The Role of the State in Labour Relations Tiberiu TĂNASE 24 Ianuarie, o zi cu dublă semnificație națională January 24, a day with double national significance Stan PETRESCU Iuliu Maniu – Lumini și umbre: O viață închinată Marii Uniri și propășirii României Mari Iuliu Maniu – Lights and shadows: A life devoted to the Great Union and the prosperity of Greater Romania Dumitru MAZILU Parohia în textul sfintelor canoane și în practică bisericească The parish in the text of the holy canons and in church practice Dumitru MAZILU Despre calitatea de paroh-administrator în funcționarea unei unități parohiale moderne About the quality of parish administrator in the operation of a modern parish unit Alexandra MOCANU Cultura anime în România Anime culture in Romania Alexandra MOCANU Abandonul școlar în România School dropout rate in Romania Emilian M. DOBRESCU Importanța pământurilor rare pentru economia mondială The Importance of the Rare Earths for the World Economy Nicolae SFETCU Modele ale inteligenței emoționale în cercetare și educație Models of Emotional Intelligence in Research and Education Nicolae SFETCU Aventurile lui Pinocchio – Educația The Adventures of Pinocchio – Education Dan D. FARCAȘ Câteva opinii privind etnogeneza românilor (1) Some opinions regarding the ethnogenesis of the Romanians (1) Denisa Oana PĂTRAȘCU Sancțiunile internaționale, măsură suprastatală de coerciție International sanctions, supra-state measure of coercion Tiberiu TĂNASE Puncte de vedere asupra proiectului de democratizare a Orientului Mijlociu Extins Viewpoints on the project of democratization of Middle East Extended -/- ȘTIINȚE FORMALE / FORMAL SCIENCE -/- Alida Monica Doriana BARBU Aplicații ale AI, machine learning, deep learning și big data în domeniul militar Applications of AI, machine learning, deep learning and big data in the military field Nicolae SFETCU Identitatea și inteligența artificială în Aventurile lui Pinocchio Identity and Artificial Intelligence in The Adventures of Pinocchio Sebastian BIDAȘCĂ Etica în inteligența artificială: provocări și perspective Ethics in Artificial Intelligence: Challenges and Prospects Nicolae SFETCU Măsurarea formei populației pentru înțelegerea tendințelor demografice Measuring population shape to understand demographic trends -/- FILOSOFIE / PHILOSOPHY -/- Adrian KLEIN Legea karmică – O abordare sinoptică Karmic Law – A Synoptic Approach Adrian KLEIN, Robert Neil BOYD Principiile subcuantice ale vieții și conștiinței Subquantum Principles of Life and Consciousness Silviu PETRE Pentru un realism al secolului XXI For a 21st century realism Dan D. FARCAȘ Câteva reflecții privind contactele extrasenzoriale Some reflections on extrasensory contacts Nicolae SFETCU Teorii cauzale ale referinței Causal theories of reference Nicolae SFETCU Filosofia inteligenței emoționale Philosophy of Emotional Intelligence -/- RECENZII CĂRȚI / BOOK REVIEWS -/- Traian OSTAHIE Nicolae Mavrocordat – disimulare și prudență la început de secol XVIII - Răgazurile lui Filotheu Nicolae Mavrocordat - concealment and prudence at the beginning of the 18th century - Răgazurile lui Filotheu Nicolae SFETCU Reconstrucția rațională a științei prin programe de cercetare The rational reconstruction of science through research programs Nicolae SFETCU Umanism, devenire și demiurgul în Aventurile lui Pinocchio Humanism, Becoming and the Demiurge in The Adventures of Pinocchio -/- ISSN 2971 – 9070 ISSN-L 2821 – 8086, DOI: 10.58679/CS49741. (shrink)

In this chapter, we discuss a selection of current views of the neural correlates of consciousness (NCC). We focus on the different predictions they make, in particular with respect to the role of prefrontal cortex (PFC) during visual experiences, which is an area of critical interest and some source of contention. Our discussion of these views focuses on the level of functional anatomy, rather than at the neuronal circuitry level. We take this approach because we currently understand more about (...) experimental evidence at this coarse level and because these results are appropriate for arbitrating between current theoretical frameworks. We discuss the Two-Visual-Systems Hypothesis (Milner & Goodale 1995; 2006), Local Recurrency (Lamme 2010; Lamme 2006), Higher Order (Lau 2008; Lau & Rosenthal 2011) and Global Workspace theories (Baars 1997; Baars 2005; Dehaene & Naccache 2001; Dehaene 2014). Despite the apparent stark differences between conscious and unconscious perceptual processing, available evidence suggests that their neural substrates must be largely shared. This indicates that the difference in neural activity between conscious and unconscious perceptual processing is likely to be subtle and highly specialized. We argue that current experimental evidence about the involvement of specific activity in prefrontal cortex supports the higher order neural theory of consciousness. In consequence, imaging techniques that focus only on marked differences between conscious and unconscious level of activity are likely to be insensitive to the relevant neural activity patterns that underlie conscious experiences. Finally, it follows from the evidence we discuss that the functional advantages of conscious over unconscious perceptual processing may be more limited than commonly thought. (shrink)

Cardiotocography data uncertainty is a critical task for the classification in biomedical field. Constructing good and efficient classifier via machine learning algorithms is necessary to help doctors in diagnosing the state of fetus heart rate. *e proposed neutrosophic diagnostic system is an Interval Neutrosophic Rough Neural Network framework based on the backpropagation algorithm. It benefits from the advantages of neutrosophic set theory not only to improve the performance of rough neural networks but also to achieve a better performance (...) than the other algorithms. *e experimental results visualize the data using the boxplot for better understanding of attribute distribution. *e performance measurement of the confusion matrix for the proposed framework is 95.1, 94.95, 95.2, and 95.1 concerning accuracy rate, precision, recall, and F1-score, respectively. WEKA application is used to analyse cardiotocography data performance measurement of different algorithms, e.g., neural network, decision table, the nearest neighbor, and rough neural network. *e comparison with other algorithms shows that the proposed framework is both feasible and efficient classifier. Additionally, the receiver operation characteristic curve displays the proposed framework classifications of the pathologic, normal, and suspicious states by 0.93, 0.90, and 0.85 areas that are considered high and acceptable under the curve, respectively. Improving the performance measurements of the proposed framework by removing ineffective attributes via feature selection would be suitable advancement in the future. Moreover, the proposed framework can also be used in various real-life problems such as classification of coronavirus, social media, and satellite image. (shrink)

An important problem with machine learning is that when label number n>2, it is very difficult to construct and optimize a group of learning functions, and we wish that optimized learning functions are still useful when prior distribution P(x) (where x is an instance) is changed. To resolve this problem, the semantic information G theory, Logical Bayesian Inference (LBI), and a group of Channel Matching (CM) algorithms together form a systematic solution. MultilabelMultilabel A semantic channel in the G theory consists (...) of a group of truth functions or membership functions. In comparison with likelihood functions, Bayesian posteriors, and Logistic functions used by popular methods, membership functions can be more conveniently used as learning functions without the above problem. In Logical Bayesian Inference (LBI), every label’s learning is independent. For Multilabel learning, we can directly obtain a group of optimized membership functions from a big enough sample with labels, without preparing different samples for different labels. A group of Channel Matching (CM) algorithms are developed for machine learning. For the Maximum Mutual Information (MMI) classification of three classes with Gaussian distributions on a two-dimensional feature space, 2-3 iterations can make mutual information between three classes and three labels surpass 99% of the MMI for most initial partitions. For mixture models, the Expectation-Maxmization (EM) algorithm is improved and becomes the CM-EM algorithm, which can outperform the EM algorithm when mixture ratios are imbalanced, or local convergence exists. The CM iteration algorithm needs to combine neural networks for MMI classifications on high-dimensional feature spaces. LBI needs further studies for the unification of statistics and logic. (shrink)

The present paper aims at showing that the phenomenological method is a crucial methodological element of every research that is based on the interpretation of utterances or texts based on experiences, like religious studies. Following the neophenomenological school, the notion of “phenomenon” is understood in a radically relative way: “A phenomenon for a person at a given point of time is a state of affairs for which this person cannot — in spite of trying to vary the presuppositions she makes (...) as much as possible — withdraw the belief that it is a fact” (Schmitz, 2003: 1). Starting from this notion, phenomenology may fruitfully criticise two common strategies: reduction and construction. The first one tries to reduce experiences to allegedly more fundamental processes like electrical impulses in neural nets. Here the phenomenologist must object that in doing so without preceding phenomenological analysis the reductionist will lose large parts of potentially important information. As to the second strategy, constructions — in the sense of presuppositions, ready-made concepts etc. — are present in all texts that are meant to express an experience. In order to describe the underlying experience more adequately, the phenomenological researcher has to remove as many constructions as possible. In this way she does not only produce a description that is ”closer” to the experience (though she can never hope to fully grasp it), but she also paves the way for comparison and dialogue across religions and cultures. (shrink)

Over centuries, philosophers have theorised about what constitutes ‘the good’ regarding behavioural choice. Characteristically, these attempts have tried to decipher the nature and substantive values that link the apparent trichotomous nature of the human psyche, variously articulated in terms of human reasoning, feeling, and desiring. Of the three, most emphasis has focused on the unique human characteristic of reasoned behavioural choice in terms of its relationship to the emotions. This article determines the principle dynamics behind 'ethical' behaviour: In the nervous (...) system, efferent nerves, otherwise known as motor neurones, carry nerve impulses away from the central nervous system to effectors such as muscles. A great deal of neural activity underpins‘efferent information processing’. What follows is a categorisation of the structure of 'efferent information processing' in a manner, that enhances our understanding of the attempts of philosophers, from Plato to Russell, to explain ‘what it is to be behave well’. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City with (...) 97.50 % accuracy. The factor of DriveTrain has the most influence on MPG_City evaluation. Similar studies can be carried out for the evaluation of other characteristics of cars. (shrink)

The centerpiece of the scientific study of consciousness is the search for the neural correlates of consciousness. Yet science is typically interested not only in discovering correlations, but also – and more deeply – in explaining them. When faced with a correlation between two phenomena in nature, we typically want to know why they correlate. The purpose of this chapter is twofold. The first half attempts to lay out the various possible explanations of the correlation between consciousness and its (...) neural correlate – to provide a sort of “menu” of options from which we probably would ultimately have to choose. The second half raises considerations suggesting that, under certain reasonable assumptions, the choice among these various options may be in principle underdetermined by the relevant scientific evidence. (shrink)

Human neural organoid research is advancing rapidly. As Greely notes in the target article, this progress presents an “onrushing ethical dilemma.” We can’t rule out the possibility that suff...

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method (...) is suitable for the evaluation of car acceptability forecasting, can also be extended to all other areas. (shrink)

Water quality assessment is critical for environmental sustainability and public health. This research employs neural networks to predict water quality, utilizing a dataset of 21 diverse features, including metals, chemicals, and biological indicators. With 8000 samples, our neural network model, consisting of four layers, achieved an impressive 94.22% accuracy with an average error of 0.031. Feature importance analysis revealed arsenic, perchlorate, cadmium, and others as pivotal factors in water quality prediction. This study offers a valuable contribution to enhancing (...) water quality monitoring and decision-making for stakeholders and policymakers. (shrink)

This paper offers a critique of an account of explanatory integration that claims that explanations of cognitive capacities by functional analyses and mechanistic explanations can be seamlessly integrated. It is shown that achieving such explanatory integration requires that the terms designating cognitive capacities in the two forms of explanation are stable but that experimental practice in the mind-brain sciences currently is not directed at achieving such stability. A positive proposal for changing experimental practice so as to promote such stability is (...) put forward and its implications for explanatory integration are briefly considered. (shrink)

It appears that consciousness science is progressing soundly, in particular in its search for the neural correlates of consciousness. There are two main approaches to this search, one is content-based (focusing on the contrast between conscious perception of, e.g., faces vs. houses), the other is state-based (focusing on overall conscious states, e.g., the contrast between dreamless sleep vs. the awake state). Methodological and conceptual considerations of a number of concrete studies show that both approaches are problematic: the content-based approach (...) seems to set aside crucial aspects of consciousness; and the state-based approach seems over-inclusive in a way that is hard to rectify without losing sight of the crucial conscious-unconscious contrast. Consequently, the search for the neural correlates of consciousness is in need of new experimental paradigms. (shrink)

Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, consisting (...) of three layers (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (shrink)

We explore the contribution made by oscillatory, synchronous neural activity to representation in the brain. We closely examine six prominent examples of brain function in which neural oscillations play a central role, and identify two levels of involvement that these oscillations take in the emergence of representations: enabling (when oscillations help to establish a communication channel between sender and receiver, or are causally involved in triggering a representation) and properly representational (when oscillations are a constitutive part of the (...) representation). We show that even an idealized informational sender–receiver account of representation makes the representational status of oscillations a non-trivial matter, which depends on rather minute empirical details. (shrink)

Despite the ubiquity of knowledge attribution in human social cognition, its associated neural and cognitive mechanisms are poorly documented. A wealth of converging evidence in cognitive neuroscience has identified independent perspective-taking and inhibitory processes for belief attribution, but the extent to which these processes are shared by knowledge attribution isn't presently understood. Here, we present the findings of an EEG study designed to directly address this shortcoming. These findings suggest that belief attribution is not a component process in knowledge (...) attribution, contra a standard attitude taken by philosophers. Instead, observed differences in P3b amplitude indicate that knowledge attribution doesn't recruit the strong self-perspective inhibition characteristic of belief attribution. However, both belief and knowledge attribution were observed to display a late slow wave widely associated with mental state attribution, indicating that knowledge attribution also shares in more general processing of others' mental states. These results provide a new perspective both on how we think about knowledge attribution, as well as Theory of Mind processes generally. (shrink)

Computer assisted theorem proving is an increasingly important part of mathematical methodology, as well as a long-standing topic in artificial intelligence (AI) research. However, the current generation of theorem proving software have limited functioning in terms of providing new proofs. Importantly, they are not able to discriminate interesting theorems and proofs from trivial ones. In order for computers to develop further in theorem proving, there would need to be a radical change in how the software functions. Recently, machine learning results (...) in solving mathematical tasks have shown early promise that deep artificial neural networks could learn symbolic mathematical processing. In this paper, I analyze the theoretical prospects of such neural networks in proving mathematical theorems. In particular, I focus on the question how such AI systems could be incorporated in practice to theorem proving and what consequences that could have. In the most optimistic scenario, this includes the possibility of autonomous automated theorem provers (AATP). Here I discuss whether such AI systems could, or should, become accepted as active agents in mathematical communities. (shrink)