How do separate brain processes bind to form unified, conscious percepts? This is the perceptual binding problem, which straddles neuroscience and psychology. In fact, two problems exist here: (1) the easy problem of how neural processes are unified, and (2) the hard problem of how this yields unified perceptual consciousness. Binding theories face familiar troubles with (1) and they do not come to grips with (2). This paper argues that neuroelectrical (electromagnetic-field) approaches may help with both problems. (...) Concerning the easy problem, standard accounts of neural binding by synchrony, attention, and convergence raise serious difficulties. These are avoided by neuroelectrical approaches in which the brain’s field binds distributed processes in myriad neurons. Concerning the hard problem, binding theories do not squarely address how to get from neural unity to unified consciousness. This raises metaphysical difficulties involving reductions, emergence, etc. Neuroelectrical (and Russellian) approaches may help avoid these difficulties too. These approaches may thus deserve further investigation as binding theories. (shrink)

We argue that human consciousness may be a property of single electron in the brain. We suppose that each electron in the universe has at least primitive consciousness. Each electron subjectively “observes” its quantum dynamics (energy, momentum, “shape” of wave function) in the form of sensations and other mental phenomena. However, some electrons in neural cells have complex “human” consciousnesses due to complex quantum dynamics in complex organic environment. We discuss neurophysiological and physical aspects of this hypothesis and show (...) that: (1) single chemically active electron has enough informational capacity to “contain” the richness of human subjective experience; (2) quantum states of some electrons might be directly influenced by human sensory data and have direct influence upon human behavior in real brain; (3) main physical and philosophical drawbacks of “conventional” “quantum theories of consciousness” may be solved by our hypothesis without much changes in their conceptual basis. We do not suggest any “new physics”, and our neuroscientific assumptions are similar to those used by other proponents of “quantum consciousness”. However, our hypothesis suggests radical changes in our view on human and physical reality. (shrink)

Evidence that minds are neural electromagnetic fields comes from research into how separate brain activities bind to form unified percepts and unified minds. Explanations of binding using synchrony, attention, and convergence are all problematic. But the unity of EM fields explains binding without these problems. These unified fields neatly explain correlations and divergences between synchrony, attention, convergence, and unified minds. The simplest explanation for the unity of both minds and fields is that minds are fields. Treating minds (...) as the fields' underlying, intrinsic nature has the further virtue of avoiding mind-body problems. There's also rising evidence that EM fields interact with brains. McFadden argues here that fields help shift the focus of attention by initiating synchrony in different neural networks, which boosts their own fields and thereby binds their activity. This evidence that minds are fields that bind and guide brain activities supports free will over epiphenomenalism and supervenience. (shrink)

Neuroscience investigates how neuronal processing circuits work, but it has problems explaining experiences this way. For example, it hasn’t explained how colour and shape circuits bind together in visual processing, nor why colours and other qualia are experienced so differently yet processed by circuits so similarly, nor how to get from processing circuits to pictorial images spread across inner space. Some theorists turn from these circuits to their electromagnetic fields to deal with such difficulties concerning the mind’s qualia, unity, privacy, (...) and causality. They include Kohler, Libet, Popper, Lindahl, Arhem, Charman, Pockett, John, McFadden, Fingelkurts, Maxwell, and Jones. They’re classifiable as computationalist, reductionist, dualist, realist, interactionist, epiphenomenalist, globalist, and localist. However, they’ve never been analysed together as a whole, which hinders evaluations of them. This article tries to rectify this. It concludes that while field theories face challenges, they aren’t easily dismissed, for they draw on considerable evidence and may avoid serious problems in neuroscience concerning the mind’s qualia, unity, causality, and ontology. (shrink)

Computational neuroscience attributes coloured areas and other perceptual qualia to calculations that are realizable in multiple cellular forms. This faces serious issues in explaining how the various qualia arise and how they bind to form overall perceptions. Qualia may instead be neuroelectrical. Growing evidence indicates that perceptions correlate with neuroelectrical activity spotted by locally activated EEGs, the different qualia correlate with the different electrochemistries of unique detector cells, a unified neural-electromagnetic field binds this activity to form overall perceptions, and (...) this field interacts with sensory circuits to help attentively guide perception. The coloured areas in images may thus be seated in the electrochemistry of unique cells, while constancy mechanisms and other multiply realizable computations just help refine these images behind the scenes. This theory is ultimately testable. (shrink)

Science is a crowning glory of the human spirit and its applications remain our best hope for social progress. However, there are limitations to existing science and perhaps to any science. The general mind-body problem is known to be currently intractable and mysterious (8). This is one of many deep problems that are generally agreed to be beyond the present purview of Science, including many quantum phenomena, etc. However, all of these famous unsolved problems are either remote from everyday experience (...) (entanglement, dark matter) or are hard to even define sharply (phenomenology, consciousness, etc.). In this note, we will consider some computational problems in vision that arise every time that we open our eyes and yet are demonstrably inconsistent with current theories of neural computation. The focus will be on two famous related phenomena, known as the neural binding problem and the experience of a detailed stable visual world. I, among many others, have struggled with these issues for more than fifty years (1, 2, 3). Somewhat paradoxically, the continuing progress in scientific methods and knowledge reveals that these are both unsolvable within existing neuroscience. By considering some basic facts about how the brain processes image input, we will show that, under the standard theory, there are not nearly enough brain neurons to compute what we experience as vision. Inconsistencies like the ones shown here have had a profound effect on paradigm change in the sciences. More directly, the discussions below suggest possible new theories and experiments. (shrink)

Studies have shown that both serotonin and glutamate receptor systems play a crucial role in the mechanisms underlying drug-induced synesthesia. The specific nature of these mechanisms, however, continues to remain elusive. Here we propose two distinct hypotheses for how synesthesia triggered by hallucinogens in the serotonin-agonist family may occur. One hypothesis is that the drug-induced destabilization of thalamic projections via GABAergic neuronal circuits from sensory areas leads to a disruption of low-level, spontaneous integration of multisensory stimuli. This sort of integration (...) regularly occurs when spatial and temporal attributes match. Destabilization of feedback loops, however, can result in incongruent experiences or binding of random thalamus activation with sensory input in a particular sensory modality. The second hypothesis builds on embodied cognition, cases in which visual images of external stimuli activate task-related neural regions. On this proposal, binding processes that do not normally generate awareness become accessible to consciousness as a result of decreased attentional discrimination among incoming stimuli. (shrink)

This chapter distinguishes between two kinds of ordinary multisensory experience that go beyond mere co-consciousness of features (e.g., the experience that results from concurrently hearing a sound in the hallway and seeing the cup on the table). In one case, a sensory experience in one modality creates a perceptual demonstrative to whose referent qualities are attributed in another sensory modality. For example, when you hear someone speak, auditory experience attributes audible qualities to a seen event, a person’s speaking motions. The (...) second kind of multisensory experience attributes features experienced in several sensory modalities to one and the same object via a process of amodal perceptual integration, i.e., integration that occurs separately from processing within the individual sensory modalities. Multisensory experiences arising from holding and seeing a tomato or from seeing the Indian curry boil and smelling it are examples of the second kind of multisensory experience. At the end of the chapter we look at synesthesia, a kind of atypical multisensory experience, and argue that one version of this phenomenon may be able to shed light on the neural mechanism underlying amodally integrated multisensory experience. (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)

Computationalism treats minds as computations. It hasn't explained how our quite similar sensory circuits encode our quite different qualia, nor how these circuits encode the binding of the different qualia into unifi ed perceptions. But there is growing evidence that qualia and binding come from neural electrochemical substances such as sensory detectors and the strong continuous electromagnetic field they create. Qualia may thus be neural substances, not neural computations (though computations may still help modulate qualia). (...) This neuroelectrical view not only avoids computationalism's empirical issues but also its problematic metaphysics. (shrink)

A general definition of consciousness is: ‘consciousness is a mental aspect of a system or a process, which is a conscious experience, a conscious function, or both depending on the context’, where the term context refers to metaphysical views, constraints, specific aims, and so on. One of the aspects of visual consciousness is the visual subjective experience (SE) or the first person experience that occurs/emerges in the visual neural-network of thalamocortical system (which includes dorsal and ventral visual pathways and (...) frontal cortex) during dynamic interactions among widely distributed neuronal groups. In this article, however, consciousness and SE are interchangeably used unless noted. Consciousness can be either access (reportable) or phenomenal consciousness. For access consciousness, the interactions are between feed forward stimulus dependent signals and fronto-parietal feedback attentional signals. The necessary ingredients for access (reportable) consciousness are (i) wakefulness, (ii) reentrant interactions among neural populations, (iii) fronto-parietal and thalamic-reticular-nucleus attentional signals that modulate consciousness, (iv) working memory that retains information for consciousness, (v) stimulus at or above threshold level, and (vi) neural-network proto-experiences (PEs) that are superposed SEs embedded in a neural-network. Attention and the ability to report are not necessary for phenomenal consciousness. The neural source for the arousal system is the ascending reticular activating system in the brain stem, which brings the thalamocortical neural networks to wakeful state as a baseline for consciousness to occur. Reentrant interactions among neural populations bind stimulus attributes (such as location and features) and entail consciousness. Attention could be the results of reentry and competitive interactions, and modulates the stimulus related feed forward signal and consciousness. The ‘sources’ of attention may be thalamic reticular nucleus for bottom-up or frontal cortex for top-down direction. The ‘target’ of visual attention is ‘V4/V8/VO’ for Red-Green (R-G) channel. The neural correlates of the psychophysical entity R-G channel appear to be ‘V4/V8/VO’-neural-network (retina → LGN ↔ V1 ↔ V2 ↔ ‘V4/V8/VO’, and areas for attention, memory, and wakefulness). The psychophysical Red-Green Channel, its neurophysiological correlates V4/V8/VO-neural-network, and related experience (such as redness) are integrated. The dual-aspect-dual-mode PE-SE framework (Vimal, J Integr Neurosci 7:49–73, 2008) was used to address the explanatory gap problem of materialism. Neural-network and related SEs are the results of the co-evolution and co-development of the material aspect (mass, charge, spin, force, quanta, and space-time) and the mental aspect of fundamental particles (strings or elementary particles (fermions and bosons)). Their mental aspects are considered as the carriers of superposed multiple possible experiences (SEs/PEs) in unexpressed form. These possibilities are actualized when neural-networks are formed via neural Darwinism, and a specific SE is selected by a matching process when the necessary ingredients of consciousness/SE are satisfied. A simple experimental design is suggested to address the necessary and sufficient attributes of consciousness. (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)

In “Bayesianism, Infinite Decisions, and Binding”, Arntzenius et al. (Mind 113:251–283, 2004 ) present cases in which agents who cannot bind themselves are driven by standard decision theory to choose sequences of actions with disastrous consequences. They defend standard decision theory by arguing that if a decision rule leads agents to disaster only when they cannot bind themselves, this should not be taken to be a mark against the decision rule. I show that this claim has surprising implications for (...) a number of other debates in decision theory. I then assess the plausibility of this claim, and suggest that it should be rejected. (shrink)

In temporal binding, the temporal interval between one event and another, occurring some time later, is subjectively compressed. We discuss two ways in which temporal binding has been conceptualized. In studies showing temporal binding between a voluntary action and its causal consequences, such binding is typically interpreted as providing a measure of an implicit or pre-reflective “sense of agency”. However, temporal binding has also been observed in contexts not involving voluntary action, but only the passive (...) observation of a cause-effect sequence. In those contexts, it has been interpreted as a top-down effect on perception reflecting a belief in causality. These two views need not be in conflict with one another, if one thinks of them as concerning two separate mechanisms through which temporal binding can occur. In this paper, we explore an alternative possibility: that there is a unitary way of explaining temporal binding both within and outside the context of voluntary action as a top-down effect on perception reflecting a belief in causality. Any such explanation needs to account for ways in which agency, and factors connected with agency, have been shown to affect the strength of temporal binding. We show that principles of causal inference and causal selection already familiar from the literature on causal learning have the potential to explain why the strength of people’s causal beliefs can be affected by the extent to which they are themselves actively involved in bringing about events, thus in turn affecting binding. (shrink)

ABSTRACT Quine insisted that the satisfaction of an open modalised formula by an object depends on how that object is described. Kripke's ‘objectual’ interpretation of quantified modal logic, whereby variables are rigid, is commonly thought to avoid these Quinean worries. Yet there remain residual Quinean worries for epistemic modality. Theorists have recently been toying with assignment-shifting treatments of epistemic contexts. On such views an epistemic operator ends up binding all the variables in its scope. One might worry that this (...) yields the undesirable result that any attempt to ‘quantify in’ to an epistemic environment is blocked. If quantifying into the relevant constructions is vacuous, then such views would seem hopelessly misguided and empirically inadequate. But a famous alternative to Kripke's semantics, namely Lewis' counterpart semantics, also faces this worry since it also treats the boxes and diamonds as assignment-shifting devices. As I'll demonstrate, the mere fact that a variable is bound is no obstacle to binding it. This provides a helpful lesson for those modelling de re epistemic contexts with assignment sensitivity, and perhaps leads the way toward the proper treatment of binding in both metaphysical and epistemic contexts: Kripke for metaphysical modality, Lewis for epistemic modality. (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)

The capacity for feature binding is typically explained in terms of the attribution model: a perceptual state selects an individual and attributes properties to it (Kahneman & Treisman 1984; Clark 2004; Burge 2010). Thus features are bound together in virtue of being attributed to the same individual. While the attribution model successfully explains some cases of binding in perception, not all binding need be understood as property attribution. This chapter argues that some forms of binding—those involving (...) holistic iconic representations, which bind features outside the limits of attention and object files—don’t fit the attribution model. The chapter then sketches an alternative coordination model of binding that construes icons as complex analog representations. (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...

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)

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)

Binding specificity is a centrally important concept in molecular biology, yet it has received little philosophical attention. Here I aim to remedy this by analyzing binding specificity as a causal property. I focus on the concept’s role in drug design, where it is highly prized and hence directly studied. From a causal perspective, understanding why binding specificity is a valuable property of drugs contributes to an understanding of causal selection—of how and why scientists distinguish between causes, not (...) just causes from noncauses. In particular, the specificity of drugs is precisely what underwrites their value as experimental interventions on biological processes. (shrink)

I give an account of the structure of “oppressive double binds,” the double binds that exist in virtue of oppression. I explain how these double binds both are a product of and serve to reinforce o...

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)

Accurate assessment and comprehension of smoking behavior are pivotal for elucidating associated health risks and formulating effective public health strategies. In this study, we introduce an innovative approach to predict and analyze smoking prevalence using an artificial neural network (ANN) model. Leveraging a comprehensive dataset spanning multiple years and geographic regions, our model incorporates various features, including demographic data, economic indicators, and tobacco control policies. This research investigates smoking trends with a specific focus on gender-based analyses. These findings are (...) pivotal for enhancing public health initiatives aimed at mitigating tobacco use, assessing associated health risks, and mitigating smoking-related diseases. (shrink)

To find the neural substrates of consciousness, researchers compare subjects’ neural activity when they are aware of stimuli against neural activity when they are not aware. Ideally, to guarantee that the neural substrates of consciousness—and nothing but the neural substrates of consciousness—are isolated, the only difference between these two contrast conditions should be conscious awareness. Nevertheless, in practice, it is quite challenging to eliminate confounds and irrelevant differences between conscious and unconscious conditions. In particular, there (...) is an often-neglected confound that is crucial to eliminate from neuroimaging studies: task performance. Unless subjects’ task performance is matched (and hence perceptual signal processing is matched), researchers risk finding the neural correlates of perception, rather than conscious perception. Here, we discuss the theoretical motivations for the performance matching framework and review empirical demonstrations of, and theoretical inferences derived from, obtaining differences in consciousness while controlling for task performance. We summarize signal detection theoretic modeling frameworks that explain how it is that we can derive performance-matched differences in consciousness without the effect being trivially driven by differences in criterion setting, and also provide principles for designing experimental paradigms that yield performance-matched differences in awareness. Finally, we address potential technical and theoretical issues that stem from matching performance across conditions of awareness, and we introduce the notion of “triangulation” for designing comprehensive experimental sets that can better reveal the neural substrates of consciousness. (shrink)

Chapin reviewed this 1972 ZEITSCHRIFT paper that proves the completeness theorem for the logic of variable-binding-term operators created by Corcoran and his student John Herring in the 1971 LOGIQUE ET ANALYSE paper in which the theorem was conjectured. This leveraging proof extends completeness of ordinary first-order logic to the extension with vbtos. Newton da Costa independently proved the same theorem about the same time using a Henkin-type proof. This 1972 paper builds on the 1971 “Notes on a Semantic Analysis (...) of Variable Binding Term Operators” (Co-author John Herring), Logique et Analyse 55, 646–57. MR0307874 (46 #6989). A variable binding term operator (vbto) is a non-logical constant, say v, which combines with a variable y and a formula F containing y free to form a term (vy:F) whose free variables are exact ly those of F, excluding y. Kalish-Montague 1964 proposed using vbtos to formalize definite descriptions “the x: x+x=2”, set abstracts {x: F}, minimization in recursive function theory “the least x: x+x>2”, etc. However, they gave no semantics for vbtos. Hatcher 1968 gave a semantics but one that has flaws described in the 1971 paper and admitted by Hatcher. In 1971 we give a correct semantic analysis of vbtos. We also give axioms for using them in deductions. And we conjecture strong completeness for the deductions with respect to the semantics. The conjecture, proved in this paper with Hatcher’s help, was proved independently about the same time by Newton da Costa. (shrink)

The aim of this paper is to develop a consistent reading of Deleuze and Guattari’s account of capitalism by taking seriously their use of Kant’s philosophy in formulating it. In Sect. 1, I will set out the two different roots of the term axiomatic in Deleuze and Guattari’s thought. The first of these is the axiomatic approach to formalising fields of mathematics, and the second the Kantian account of the indeterminate relationship between the transcendental unity of apperception and the transcendental (...) object. In Sect. 2, we will see how this transcendental aspect of Deleuze and Guattari’s account of axiomatics is expressed in the notion of binding, which Deleuze and Guattari take to be a process that forces us to understand a field of entities in a certain manner, namely as clearly delimited and deployed in a homogeneous space. I will argue that this process of binding operates as a transcendental condition for capitalism for Deleuze and Guattari. Section 3 addresses some of the details of the capitalist axiomatic itself, drawing out why a Kantian reading of Deleuze and Guattari’s account of the axiomatic provides a response to some of the criticisms of it. Section 4 then analyses Deleuze and Guattari’s account of the outside of capitalism, using the double signification of the noumenal to understand the complex relationship between what they call the war machine and its representation within the axiomatic. (shrink)

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural (...) networks to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3%. (shrink)

Neural structural representations are cerebral map- or model-like structures that structurally resemble what they represent. These representations are absolutely central to the “cognitive neuroscience revolution”, as they are the only type of representation compatible with the revolutionaries’ mechanistic commitments. Crucially, however, these very same commitments entail that structural representations can be observed in the swirl of neuronal activity. Here, I argue that no structural representations have been observed being present in our neuronal activity, no matter the spatiotemporal scale of (...) observation. My argument begins by introducing the “cognitive neuroscience revolution” (Sect. 1 ) and sketching a prominent, widely adopted account of structural representations (Sect. 2 ). Then, I will consult various reports that describe our neuronal activity at various spatiotemporal scales, arguing that none of them reports the presence of structural representations (Sect. 3 ). After having deflected certain intuitive objections to my analysis (Sect. 4 ), I will conclude that, in the absence of neural structural representations, representationalism and mechanism can’t go together, and so the “cognitive neuroscience revolution” is forced to abandon one of its commitments (Sect. 5 ). (shrink)

As a type of evidence glass can be very useful contact trace material in a wide range of offences including burglaries and robberies, hit-and-run accidents, murders, assaults, ram-raids, criminal damage and thefts of and from motor vehicles. All of that offer the potential for glass fragments to be transferred from anything made of glass which breaks, to whoever or whatever was responsible. Variation in manufacture of glass allows considerable discrimination even with tiny fragments. In this study, we worked glass classification (...) and testing of artificial neural network model created by the JustNN. The aim of the study is help investigator in identifying the type of glass found in arena of the crime. The Neural Network model was trained and validated using the type of glass dataset. The accuracy of model in predicting the type of glass reached 96.7%. Thus neural network is suitable for predicating type of glasses. (shrink)

Cognitive penetration of perception, broadly understood, is the influence that the cognitive system has on a perceptual system. The paper shows a form of cognitive penetration in the visual system which I call ‘architectural’. Architectural cognitive penetration is the process whereby the behaviour or the structure of the perceptual system is influenced by the cognitive system, which consequently may have an impact on the content of the perceptual experience. I scrutinize a study in perceptual learning that provides empirical evidence that (...) cognitive influences in the visual system produce neural reorganization in the primary visual cortex. The type of cognitive penetration can be synchronic and diachronic. (shrink)

Abstract: The emergence of the novel coronavirus (COVID-19) in 2019 has presented the world with an unprecedented global health crisis. The rapid and widespread transmission of the virus has strained healthcare systems, disrupted economies, and challenged societies. In response to this monumental challenge, the intersection of technology and healthcare has become a focal point for innovation. This research endeavors to leverage the capabilities of Artificial Neural Networks (ANNs) to develop an advanced predictive model for forecasting the spread of COVID-19. (...) It involves the collection, analysis, and integration of diverse datasets encompassing epidemiological, clinical, and social factors that influence the virus's dissemination. (shrink)

The purpose of this paper is to address a well-known dilemma for physicalism. If mental properties are type identical to physical properties, then their causal efficacy is secure, but at the cost of ruling out mentality in creatures very different to ourselves. On the other hand, if mental properties are multiply realizable, then all kinds of creatures can instantiate them, but then they seem to be causally redundant. The causal exclusion problem depends on the widely held principle that realized properties (...) inherit their causal powers from their realizers. While this principle holds for functional realization, it fails on a broader notion of realization that permits the realization of complex qualitative properties such as spatial and temporal patterns. Such properties are best seen as dependent powerful qualities, which have their causal roles in virtue of being the qualities they are, and do not inherit powers from their realizers. Recent studies have identified one such property—neural synchrony—as a correlate of consciousness. If synchrony is also partially constitutive of consciousness, then phenomenal properties are both multiply realizable and causally novel. I outline a version of representationalism about consciousness on which this constitution claim holds. (shrink)

One of the most widely recognised intuitions about knowledge is that knowing precludes believing truly as a matter of luck. On Pritchard’s highly influential modal account of epistemic luck, luckily true beliefs are, roughly, those for which there are many close possible worlds in which the same belief formed in the same way is false. My aim is to introduce a new challenge to this account. Starting from the observation—as documented by a number of recent EEG studies—that our capacity to (...) detect visual stimuli fluctuates with the phase of our neural oscillations, I argue that there can be very close possible worlds in which an actual-world detectable stimulus is undetectable. However, this doesn’t diminish our willingness to attribute knowledge in the case that the stimulus is detectable, even when undetectability would result in the same belief formed in the same way being false. As I will argue at length, the modal account appears unable to accommodate this result. (shrink)

Heart failure is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 918 samples with 11 features, such as age, sex, chest pain type, resting blood pressure, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and HeartDisease. A (...) neural network model with four layers (1 input and 1 output) was trained on the dataset and achieved an accuracy of 90% and an average error of 0.009. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

My essay begins by providing a broad vision of how William James’s psychology and philosophy were a two-pronged attempt to revive the self whose foundations had collapsed after the Civil War. Next, I explain how this revival was all too successful insofar as James inadvertently resurrected the imperial self, so that he was forced to adjust and develop his philosophy of religion in keeping with his anti-imperialism. James’s mature philosophy of religion therefore articulates a vision of the radically ethical saint (...) religiously bound to a decidedly pluralistic universe. I evaluate James’s philosophy of religion by comparing it to Enrique Dussel’s psychological portrait of the imperialist ego, Dussel’s attempt to religiously bind this ego, and the more radical philosophy that results. I suggest that Dussel’s philosophy of liberation: 1) better theorizes the religious contraction of the self as a necessary part of ethical and political life and 2) offers a more concrete and radically democratic philosophy. My overarching aim is to show how Dussel’s liberation philosophy can help critically develop James’s pragmatist claim that religion might provide a force for widely and positively transforming our ethical and political lives. (shrink)

Demonstrating that a developing norm is not yet well established in international law is frequently thought to show that states are not bound by the norm as law. More precisely, showing that a purported international legal norm has only limited support from well-established international legal sources is normally seen as sufficient to rebut an obligation on the part of subjects to comply with the norm in virtue of its legal status. I contend that this view is mistaken. Nascent norms of (...) international law (e.g., crystallizing norms of customary law) can be binding in much the same way as better-established doctrine. This point becomes perspicuous, I argue, once we get a clear sense of the plausible options for grounding the moral authority of international law generally. -/- This result is interesting in its own right, but it also reveals two other features of the character of state responsibility under international law. First, the distinction between legislation and compliance is less pronounced compared to domestic law. Consequently, the virtues of good governance will frequently be pertinent to determining the content of states’ obligations under international law. Second, normally more powerful and influential agents will be more strongly bound by international law than other subjects. This is an attractive result, addressing a concern that motivates many international lawyers to view international law as absolutely binding. An absolutist view international legal authority is unnecessary for showing that the most powerful and dangerous states are strongly bound by the terms of much existing law. (shrink)

Visual imagery is a form of sensory imagination, involving subjective experiences typically described as similar to perception, but which occur in the absence of corresponding external stimuli. We used the Activation Likelihood Estimation algorithm (ALE) to identify regions consistently activated by visual imagery across 40 neuroimaging studies, the first such meta-analysis. We also employed a recently developed multi-modal parcellation of the human brain to attribute stereotactic co-ordinates to one of 180 anatomical regions, the first time this approach has been combined (...) with the ALE algorithm. We identified a total 634 foci, based on measurements from 464 participants. Our overall comparison identified activation in the superior parietal lobule, particularly in the left hemisphere, consistent with the proposed ‘top-down’ role for this brain region in imagery. Inferior premotor areas and the inferior frontal sulcus were reliably activated, a finding consistent with the prominent semantic demands made by many visual imagery tasks. We observed bilateral activation in several areas associated with the integration of eye movements and visual information, including the supplementary and cingulate eye fields (SCEFs) and the frontal eye fields (FEFs), suggesting that enactive processes are important in visual imagery. V1 was typically activated during visual imagery, even when participants have their eyes closed, consistent with influential depictive theories of visual imagery. Temporal lobe activation was restricted to area PH and regions of the fusiform gyrus, adjacent to the fusiform face complex (FFC). These results provide a secure foundation for future work to characterise in greater detail the functional contributions of specific areas to visual imagery. (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)

Combinatory logic (Curry and Feys 1958) is a “variable-free” alternative to the lambda calculus. The two have the same expressive power but build their expressions differently. “Variable-free” semantics is, more precisely, “free of variable binding”: it has no operation like abstraction that turns a free variable into a bound one; it uses combinators—operations on functions—instead. For the general linguistic motivation of this approach, see the works of Steedman, Szabolcsi, and Jacobson, among others. The standard view in linguistics is that (...) reflexive and personal pronouns are free variables that get bound by an antecedent through some coindexing mechanism. In variable free semantics the same task is performed by some combinator that identifies two arguments of the function it operates on (a duplicator). This combinator may be built into the lexical semantics of the pronoun, into that of the antecedent, or it may be a free-floating operation applicable to predicates or larger chunks of texts, i.e. a typeshifter. This note is concerned with the case of cross-sentential anaphora. It adopts Hepple’s and Jacobson’s interpretation of pronouns as identity maps and asks how this can be extended to the cross-sentential case, assuming the dynamic semantic view of anaphora. It first outlines the possibility of interpreting indefinites that antecede non-ccommanded pronouns as existential quantifiers enriched with a duplicator. Then it argues that it is preferable to use the duplicator as a type-shifter that applies “on the fly”. The proposal has consequences for two central ingredients of the classical dynamic semantic treatment: it does away with abstraction over assignments and with treating indefinites as inherently existentially quantified. However, cross-sentential anaphora remains a matter of binding, and the idea of propositions as context change potentials is retained. (shrink)

Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including 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. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model was trained (...) and validated, achieving an impressive accuracy of 100% and an average error of 0.000015, indicating its robust predictive capability. Moreover, we conducted feature importance analysis to identify the most influential features for predicting audit risk. The key features found to be critical for classifying fraudulent activities in audit risk prediction are Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, and History_score. This research contributes to the field of audit risk prediction by demonstrating the effectiveness of a neural network-based approach and highlighting the importance of specific features in detecting fraudulent activities. The findings have significant implications for auditors and organizations seeking to enhance their audit risk assessment processes, ultimately leading to improved financial transparency and fraud detection. (shrink)

The segregation of image parts into foreground and background is an important aspect of the neural computation of 3D scene perception. To achieve such segregation, the brain needs information about border ownership; that is, the belongingness of a contour to a specific surface represented in the image. This article presents psychophysical data derived from 3D percepts of figure and ground that were generated by presenting 2D images composed of spatially disjoint shapes that pointed inward or outward relative to the (...) continuous boundaries that they induced along their collinear edges. The shapes in some images had the same contrast (black or white) with respect to the background gray. Other images included opposite contrasts along each induced continuous boundary. Psychophysical results demonstrate conditions under which figure-ground judgment probabilities in response to these ambiguous displays are determined by the orientation of contrasts only, not by their relative contrasts, despite the fact that many border ownership cells in cortical area V2 respond to a preferred relative contrast. Studies are also reviewed in which both polarity-specific and polarity-invariant properties obtain perceptual figure-ground grouping results. The FACADE and 3D LAMINART models are used to explain these data. Keywords: figure-ground separation, border ownership, perceptual grouping, surface filling-in, V2, V4, FACADE Theory. (shrink)

Why should anyone be bound by cognitive norms, such as the norms of reason or mathematics? To become a mathematician is to learn to obey the norms of the mathematical community. A self becomes intentional by binding itself to communal norms. Only then can it have the freedom to think or make assertions about the community’s objects -- triangles or imaginary numbers, for example. Norms do not bind selves from the outside: being bound by norms is what constitutes a (...) self. (shrink)

Abstract Alzheimer's disease has spread insanely throughout the world. Early detection and intervention are essential to improve the chances of a positive outcome. This study presents a new method to predict a person's likelihood of developing Alzheimer's using a neural network model. The dataset includes 373 samples with 10 features, such as Group,M/F,Age,EDUC, SES,MMSE,CDR ,eTIV,nWBV,Oldpeak,ASF.. A four-layer neural network model (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 98.10% and an (...) average error of 0.01. This study provides a valuable tool for early detection and intervention of Alzheimer's, thus contributing to the field of health and medicine. (shrink)