One method for uncovering the subprocesses of mental processes is the “Additive Factors Method” (AFM). The AFM uses reaction time data from factorial experiments to infer the presence of separate processing stages. This paper investigates the conceptual status of the AFM. It argues that one of the AFM’s underlying assumptions is problematic in light of recent developments in cognitive neuroscience. Discussion begins by laying out the basic logic of the AFM, followed by an analysis of the challenge presented by (...) class='Hi'>neural reuse. Following this, implications are analysed and avenues of response considered. (shrink)

Anderson suggests that theories of sensorimotor grounding are too narrow to account for his findings of widespread supporting multiple different cognitive I call some of the methodological assumptions underlying this conclusion into question, and suggest that his examples reaffirm rather than undermine the special status of sensorimotor processes in cognitive evolution.

The basic human ability to treat quantitative information can be divided into two parts. With proto-arithmetical ability, based on the core cognitive abilities for subitizing and estimation, numerosities can be treated in a limited and/or approximate manner. With arithmetical ability, numerosities are processed (counted, operated on) systematically in a discrete, linear, and unbounded manner. In this paper, I study the theory of enculturation as presented by Menary (2015) as a possible explanation of how we make the move from the proto-arithmetical (...) ability to arithmetic proper. I argue that enculturation based on neural reuse provides a theoretically sound and fruitful framework for explaining this development. However, I show that a comprehensive explanation must be based on valid theoretical distinctions and involve several stages in the development of arithmetical knowledge. I provide an account that meets these challenges and thus leads to a better understanding of the subject of enculturation. (shrink)

The body is a highly complex, coordinated system engaged in coping with many environmental problems. It can be considered as some sort of opportunity or obstacle, with which internal processing must deal. Internal processing must take into account the possibilities and limitations of the particular body. In other words, even if the body is not involved in the realization of some cognitive explicit task, it is not a neutral factor of our understanding of why a system solves a task in (...) one way or another. Therefore, when conducting research on embodiment and the body’s cognitive system we should not neglect internal, cognitive processing. I appeal to Goldman’s research on embodied cognition to sketch the broader framework for internal processing in embodied cognition. I believe that even if we don’t accept Goldman’s approach as the viable proposal for embodied cognition in general, it’s a quite natural starting point for our analysis. Goldman (2012; 2014, and with de Vignemont 2009) argue for the essential role of the bodily formats or bodily codes (respectively: B-formats and B-codes) in embodied cognition. B-codes are here described as the processing of regions or sub-regions of the central nervous system. They are primarily employed for body control or monitoring, and reused for cognitive tasks. Beyond doubt, this conception provides an excellent starting point for analyzing the internal (mostly neural) processing in cases of embodied cognition. At the end of this paper, I will argue that the embodiment of cognition needs a conceptual twist. Following Keijzer’s (2015) interest in the evolution of the nervous system, and the minimal forms of cognition, I argue that in investigating embodied cognition, we should investigate the role played by cognitive processing for specific kinds of organisms, meaning organisms with a body of a particular morphology (size, shape, kinds, and distribution of sensors and effectors). Doing that, I refer to some conceptual and empirical considerations. I will also try to show that research on embodied cognition is still not sufficiently anchored in evolutionary and comparative studies on cognition, nor on the nervous system and body morphology. Bigger reliance on these kinds of studies, will make it make possible to gain a deeper understanding of internal processing in embodied cognition. (shrink)

The adaptive reuse of industrial heritage, a critical component in addressing radical climate change-related urban transitions, is increasingly pertinent. This paper distinguishes ‘urban transitions’ from ‘urban transformation,’ emphasizing a more gradual, adaptive approach to urban development under the pressures of climate change. It explores the repurposing of industrial buildings and spaces, maintaining their cultural and historical value while meeting current urban needs. Through a mixed-methods approach, the paper analyses how adaptive reuse contributes to sustainable urban development, examines the (...) scale and impact of such projects from local quarters to city-wide implications, and discusses the potential negative consequences, including gentrif ication. The study spans various global regions, including Europe, the United States, Latin America, Canada, and Australia, using case studies to illustrate the effectiveness of adaptive reuse in promoting sustainability, revitalizing urban areas, and preserving cultural heritage. The paper questions the viability of traditional long-term sustainable urban development strategies in the face of rapid environmental and societal changes, suggesting a potential need for paradigm shifts in urban planning. (shrink)

Public sentiment regarding climate change in the United States is starkly divided, with the Republicans and Democrats holding markedly different views. Given the inherent connection between the water crisis and climate change, this research aimed to investigate the interplay between the residents’ beliefs about the impact of climate change on water supply unpredictability, their political identity, and their willingness to adopt direct and indirect potable water reuse. The Bayesian Mindsponge Framework (BMF) analytics was conducted on a dataset of 1,831 (...) water users in the arid region of Albuquerque, New Mexico. The findings reveal a complicated interplay among examined factors. Specifically, we discovered a positive association between the belief in the impacts of climate change and the willingness to adopt potable water reuse among non-Republican water users. In contrast, Republican water users exhibit a negative association between climate change belief and willingness to adopt potable water reuse. Interestingly, despite this, Republicans generally show a higher overall willingness to adopt potable water reuse compared to non-Republicans. These findings suggest that contextual factors in Albuquerque, New Mexico, in which challenges of aridity and economic dependence on water resources are pronounced, might be important aspects contributing to the Republican’s high willingness to adopt potable water reuse. Also, the current climate change communication strategy with the Republicans might not be appropriate, and it should be tailored to align with the Republicans’ beliefs and persuade them better. (shrink)

The ongoing refugee crisis is described as the most important concern since the Second World War, which has caused a great displacement of people. Many of these immigrants have been departing towards Mediterranean countries, as first-line states, seeking for a chance to enter Europe. This situation has created a challenging condition for many refugee accepting cities as well as for the migrants to get integrated within the new society. This fact has had a great influence on the sustainability condition while (...) the rapid and uncontrolled inflows can overwhelm the host countries' capacities to integrate new arrivals. In this regard, some European countries including Germany and Italy are coming about with strategies for accommodation and integration of these refugees in their countries. This paper aims to study and analyze two of the current case studies reflecting adaptive reuse strategies in European context for providing refugees' temporary housing facilities. In the context of this research, using the existing building stock introduced as the dominant strategy which can provide refugees with a proper shelter and also while providing the chance for their urban integration can contribute to revitalization of urban areas with the newcomers' participation. By analyzing the Berlin's largest refugee shelter inside Tempelhof Airport as the first case study, the major policies in Germany for providing refugee housing in national and local levels have been investigated. However, the second case study -Ex-Moi in Turin, is characteristically different from the case of Tempelhof airport of Berlin; since the refugees occupied the abandoned facility of the Olympic Village and settled down there. Regarding the fact that there is an urgent need for long-term policies and sustainable approaches to cope with the current refugee crisis, this research tries to shed a light on the path towards providing temporary housings by analyzing the challenges and opportunities of two different current case studies in Germany and Italy. (shrink)

Given the close relatedness of local issues, water scarcity, and sustainability, this research sought to investigate the factors affecting residents’ willingness to reuse direct and indirect potable water in the arid region. Utilizing the Bayesian Mindsponge Framework (BMF), an analysis was undertaken with a sample of 1,831 water consumers in the City of Albuquerque, the most populous city in New Mexico, United States. The primary analysis revealed positive associations between local concerns about drought or water scarcity and population growth (...) with residents' willingness to reuse direct and indirect potable water. Conversely, concerns about water quality and water bills were found to be negatively associated with the willingness to utilize directly and indirectly recycled water for drinking. In light of these findings, the study explores the potential of fostering an eco-surplus culture by cultivating a sense of environmental responsibility and acknowledging the relationship between local issues and environmental conditions. Simultaneously, the study proposes collaboration between the government and residents to promote water reuse programs to conserve water resources effectively. (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)

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)

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)

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)

The current study has two objectives: - To examine whether the residents’ perceived impact of climate change on water supply affects their direct and indirect potable water reuse willingness in Albuquerque, New Mexico. - To examine whether the residents’ awareness of water scarcity issues in New Mexico moderates the relationship between the perceived impact of climate change on water supply and potable water reuse willingness. Findings from this study are expected to contribute to promoting the eco-surplus culture for (...) achieving the environmental semiconducting principle. (shrink)

After the WHS inscription of the Historic Centre of Macau in 2005, the relationship between citizens of Macau and their heritage is not distanced. Most of these monuments remain functional for religious and social purposes and are actively engaged in public commercial activities such as the annual Macau Light Festival. Several historic houses have been transformed into either a permanent library or a museum where people can experience various events. With such frequent interaction, these monuments are more than just heritage (...) to locals; they are the venues where people’s daily lives are attached. However, this familiarity will cause a paradox in preservation since losing the distance might mean being unable to deliver the message of protection. It would be difficult to find a balance between preservation and reuse of heritage. This paper will use the Historic Centre of Macau as a case study to discuss the types of adaptive reuse in Macau and propose potential risks of these approaches. (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...

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)

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)

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)

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)

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)

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)

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)

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)

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)

Abstract: Waste polyvinylchloride (PVC) pipes were reused in the modification of commercially available bitumen. Waste PVC/ bitumen blends were prepared by melt-mixing method. Rheological characteristics of the neat bitumen and bitumen blends samples were investigated. The rheological properties were determined using an ARES- Rheometer (Rheometric Scientific, Co.) under nitrogen atmosphere in parallel plate geometry with diameter 25 mm. The measurements were performed over a wide range of temperatures ranged from -10°C to 60°C and frequencies from 100 to 0.1 radians per (...) second. Improvement in rheological properties. (shrink)

The environmental concern of plastic waste (PW) generation has escalated to an alarming level due to the versatility and high demand in various applications. In order to search for an effective way to utilise PW, reusing them for the production of construction material appears as an environmentally-friendly approach. This is also because conventional construction materials often consume high energy during production has caused many environmental impacts. This review paper summarises the previous studies on reusing various PW as raw material and (...) aggregate for construction and its properties with special attention to bricks and paving blocks. This paper begins by illustrating on the properties of plastics and the impacts of PW to the environment. Followed by discussion on reusing PW and its impacts on the overall properties of construction material. This review found that limited studies had been conducted on the usage of PW in the production of the paving block. Besides, most of the studies focused predominantly on compressive strength and water absorption as the main parameters to evaluate the characteristics of bricks and paving blocks. It is concluded that the use of PW in construction material could possibly serve as a sustainable source for construction material in the future. (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)

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)

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)

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)

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)

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)

Using Kantian starting points, we develop a notion of ‘pre-determinant intentionality,’ which refers to the intentionality of judgments that support objective truth-claims. We show how the weight-selections of neural networks can be taken to involve this form of intentionality. We argue that viewing weight selection or ‘internodal and meta-internodal selection’ as involving pre-determinant intentionality allows us to better conceptualize the coordination of computational systems. In particular, it allows us to better conceptualize the coordination of computational activity concerned with the (...) promotion of learning in neural networks.. (shrink)

One of the most important recent developments in the study of concepts has been the resurgence of interest in nativist accounts of the human conceptual system. However, many theorists suppose that a key feature of neural organization—the brain’s plasticity—undermines the nativist approach to concept acquisition. We argue that, on the contrary, not only does the brain’s plasticity fail to undermine concept nativism, but a detailed examination of the neurological evidence actually provides powerful support for concept nativism.

From our perspective, we believe that it is necessary to abandon the idea that all those who suffer from SI or SA are mentally ill, which causes them to be stigmatized and subjected to psychiatric treatment, when what they need is a different vision of life as well as adaptive strategies to employ a new philosophy. Public policies should consider implementing programs that reinforce cognitive flexibility in addition to character strengths.

The higher-order thought (HOT) theory of consciousness is a reductive representational theory of consciousness which says that what makes a mental state conscious is that there is a suitable HOT directed at that mental state. Although it seems that any neural realization of the theory must be somewhat widely distributed in the brain, it remains unclear just how widely distributed it needs to be. In section I, I provide some background and define some key terms. In section II, I (...) argue against the view that HOT theory should treat first-order (i.e. world-directed) conscious states as requiring prefrontal cortical activity though it is reasonable to suppose that conscious states are realized in the brain. In section III, I then explore some of the key background metaphysical issues involved in understanding the nature of consciousness, such as the debate between realism and idealism as well as the prospects for solving the so-called “hard problem” of consciousness. Some of the differences in question often mirror the traditional differences between Western and Eastern perspectives on the nature of consciousness. Overall, I argue that some form of realism and physicalism is more plausible than the opposing views. I also argue that materialists (and especially HOT theorists) can offer plausible replies to the hard problem. (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)

Many human decisions and behaviors in daily life entail a cost-benefit analysis. From selecting what to eat for dinner to determining the career to pursue, we more or less assess the cost and benefit of each choice. Given the frequent occurrences of cost-benefit thinking in our minds, some intriguing questions arise: how do the cost-benefit thinking processes emerge? How does the brain function to generate such thoughts? Although these inquiries have yet to be thoroughly answered, scientists are adding new clues (...) to our knowledge, such as the neural representation of our subjective cost-benefit judgments. (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)

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)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary classification, (...) identifying the presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (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)

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)

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)

For many employees, ‘work’ is no longer something performed while sitting at a computer in an office. Employees in a growing number of industries are expected to carry mobile devices and be available for work-related interactions even when beyond the workplace and outside of normal business hours. In this article it is argued that a future step will increasingly be to move work-related information and communication technology (ICT) inside the human body through the use of neuroprosthetics, to create employees who (...) are always ‘online’ and connected to their workplace’s digital ecosystems. At present, neural implants are used primarily to restore abilities lost through injury or illness, however their use for augmentative purposes is expected to grow, resulting in populations of human beings who possess technologically altered capacities for perception, memory, imagination, and the manipulation of physical environments and virtual cyberspace. Such workers may exchange thoughts and share knowledge within posthuman cybernetic networks that are inaccessible to unaugmented human beings. Scholars note that despite their potential benefits, such neuroprosthetic devices may create numerous problems for their users, including a sense of alienation, the threat of computer viruses and hacking, financial burdens, and legal questions surrounding ownership of intellectual property produced while using such implants. Moreover, different populations of human beings may eventually come to occupy irreconcilable digital ecosystems as some persons embrace neuroprosthetic technology, others feel coerced into augmenting their brains to compete within the economy, others might reject such technology, and still others will simply be unable to afford it. In this text we propose a model for analyzing how particular neuroprosthetic devices will either facilitate human beings’ participation in new forms of socioeconomic interaction and digital workplace ecosystems – or undermine their mental and physical health, privacy, autonomy, and authenticity. We then show how such a model can be used to create device ontologies and typologies that help us classify and understand different kinds of advanced neuroprosthetic devices according to the impact that they will have on individual human beings. (shrink)

Orthodox neural materialists think mental states are neural events or orthodox material properties of neutral events. Orthodox material properties are defining properties of the “physical”. A “defining property” of the physical is a type of property that provides a necessary condition for something’s being correctly termed “physical”. In this paper I give an argument against orthodox neural materialism. If successful, the argument would show at least some properties of some mental states are not orthodox material properties of (...) neural events. Opposing philosophical orthodoxy, I show there are no posteriori identities -- identities that cannot be known of a priori. (shrink)

A constant theme in Sherry Turkle’s work is the idea that computers shape our social and psychological lives. This idea is of course in a sense trivial, as can be observed when walking down any city street and noting how many of the passers-by have their heads buried in screens. In The Second Self, however, Turkle makes a stronger claim to the effect that where people confront machines that seem to think this suggests a new way for us to think (...) – about human thought, emotion, memory, and understanding and thereby affects the way we think and see ourselves as humans. I here attempt here to throw a sceptical light on claims of this sort by examining the Chinese chatbot “Xiǎoice”, which is described by its authors as “the most popular social chatbot in the world”. (shrink)

In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in hospitals. The (...) evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the birth weight with 100% accuracy. (shrink)

Objective. Study of the validity and reliability of the discourse approach for the psycholinguistic understanding of the nature, structure, and features of the linguistic consciousness functioning. -/- Materials & Methods. This paper analyzes artificial neural network models built on the corpus of texts, which were obtained in the process of experimental research of the coronavirus quarantine concept as a new category of linguistic consciousness. The methodology of feedforward artificial neural networks (multilayer perceptron) was used in order to assess (...) the possibility of predicting the leading texts semantics based on the discourses ranks and their place in the respective linear sequence. Same baseline parameters were used to predict respondents' self-assessments of changes in their psychological well-being and in daily life routine during the quarantine, as well as to predict their preferences of the quarantine strategies. The study relied on basic ideas about discourse as a meaning constituted by the dispersion of other meanings (Foucault). The same dispersion mechanism realizes itself in interdiscourse interaction, forming a discursive formation at a higher level. The method of T-units (Hunt) was used to identify and count discourses in the texts. The ranking of discourses was provided based on the criterion of their semantic-syntactic autonomy. -/- Results. The conducted neural network modeling revealed a high accuracy in predicting the work of the linguistic consciousness functions associated with retrospective self-assessment and anticipatory imagination of the respondents. Another result of this modeling is a partial confirmation of the assumption concerning existence a relationship between the structural parameters of the discursive field (the rank of the discourses and their place in the respective linear sequence) and the leading semantics of the text. -/- Conclusions. A discourse approach to the study of linguistic consciousness, understanding of its structure and functioning features seems to be reasonably appropriate. The implementation of the approach presupposes the need to form a base of linguistic corpora with the inclusion in each text markup of such parameters as: the presence of specific discourses, their ranks, positions in the linear sequence of discourses. (shrink)