Even though social scientists continue to discuss the problems posed by self-fulfilling and self-frustrating predictions, philosophers of science have ignored the topic since the 1970s. Back then, the prevailing view was that the methodological problems posed by reflexive predictions are either minor or easily avoided. I believe that this consensus was premature, ultimately relying on an overly narrow understanding of the phenomenon. I present an improved way to understand reflexive predictions (framed in probabilistic terms) and show that, once (...) such predictions are understood this way, the methodological problems they pose may turn out to be neither minor nor easily avoided. (shrink)

This essay offers a unified philosophical account of observer effects in social science. When agents are aware their behavior is subject to scientific inquiry they often act in ways that render measurements of them unreliable. This is the problem of reflexive measurement. In order to develop this novel account, I provide a general characterization of reflexivity which encompasses the full scope of scientific practice: theorizing, prediction, measurement, etc. The characterization captures the insights of contemporary philosophers of science working (...) on reflexive prediction alongside observations by scientists grappling with observer-type effects in the course of their research. (shrink)

The contents of our conscious mind can seem unpredictable, whimsical, and free from external control. When instructed to attend to a stimulus in a work setting, for example, one might find oneself thinking about household chores. Conscious content thus appears different in nature from reflex action. Under the appropriate conditions, reflexes occur predictably, reliably, and via external control. Despite these intuitions, theorists have proposed that, under certain conditions, conscious content resembles reflexes and arises reliably via external control. We introduce the (...) Reflexive Imagery Task, a paradigm in which, as a function of external control, conscious content is triggered reliably and unintentionally: When instructed to not subvocalize the name of a stimulus object, participants reliably failed to suppress the set-related imagery. This stimulus-elicited content is considered ‘high-level’ content and, in terms of stages of processing, occurs late in the processing stream. We discuss the implications of this paradigm for consciousness research. (shrink)

A self-fulfilling prophecy is, roughly, a prediction that brings about its own truth. Although true predictions are hard to fault, self-fulfilling prophecies are often regarded with suspicion. In this article, we vindicate this suspicion by explaining what self-fulfilling prophecies are and what is problematic about them, paying special attention to how their problems are exacerbated through automated prediction. Our descriptive account of self-fulfilling prophecies articulates the four elements that define them. Based on this account, we begin our critique (...) by showing that typical self-fulfilling prophecies arise due to mistakes about the relationship between a prediction and its object. Such mistakes—along with other mistakes in predicting or in the larger practical endeavor—are easily overlooked when the predictions turn out true. Thus we note that self-fulfilling prophecies prompt no error signals; truth shrouds their mistakes from humans and machines alike. Consequently, self-fulfilling prophecies create several obstacles to accountability for the outcomes they produce. We conclude our critique by showing how failures of accountability, and the associated failures to make corrections, explain the connection between self-fulfilling prophecies and feedback loops. By analyzing the complex relationships between accuracy and other evaluatively significant features of predictions, this article sheds light both on the special case of self-fulfilling prophecies and on the ethics of prediction more generally. (shrink)

In the opening lines of this article it is claimed that history is a discipline taking role as part of unique body of science. The concept of scientific rationality is presented as the criterion of demarcation between science and pseudoscience. From this statement as a starting point, it follows that the methodological grounds for scientific predictions are common for all scientific disciplines. Different aspects of scientific predicting are critically examined: thesis of symmetry, determinism and predictability, indeterminism and predictability, reflexivity of (...) predictions, prediction of novelty and progress, predictions in relation to the time of their uttering, the prediction sketch, truthfulness and regularity in implying prediction, and predictability of human actions. In the concluding section, the author admits of the possibility of constructing the theory of history, opposed to eschatological philosophy of history, as a predictive theory with a scientific basis and a theory whose specifications limit its misuse in practice. (shrink)

Once lively debates concerning the philosophical significance of self-fulfilling science, or the causal contribution of science to bringing about the states of affairs it depicts, lapsed in the 1970s. Recent claims concerning the influence of economic theory on the behavior it predicts or explains seem poised to revitalize discussion, yet lack of clarity abounds concerning the key features of such cases and the philosophical issues to which they might be relevant. In this paper, I examine a paradigmatic case of self-fulfilling (...) science, clarify its key features, and critically discuss two existing approaches to understanding such phenomena. Ultimately, I suggest a novel approach more well-suited to analyzing such cases and exploring their philosophical significance. (shrink)

Four studies explore the role of perceptual fluency in attenuating bullshit receptivity, or the tendency for individuals to rate otherwise meaningless statements as “profound”. Across four studies, we presented participants with a sample of pseudoprofound bullshit statements in either a fluent or disfluent font and found that overall, disfluency attenuated bullshit receptivity while also finding little evidence that this effect was moderated by cognitive thinking style. In all studies, we measured participants’ cognitive reflection, need for cognition, faith in intuition, and (...) superstitious beliefs. Superstition strongly predicted bullshit receptivity regardless of fluency. Inconclusive results were found for the remaining scales. Potential links for the role of perceptual disfluency in promoting analytic thinking are discussed. (shrink)

As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological, medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone’s disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating (...) to the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in theEthical Framework for Big Data in Health and Research to a case study on clinical genomic data sharing. Please refer to that article for an explanation of how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. Our discussion is meant to be of use to those involved in the practice as well as governance and oversight of precision medicine to address ethical concerns that arise in a coherent and systematic manner. (shrink)

The distinction between the contexts of discovery and justification, this distinction dear to the projects of logical empiricism, was, as is well known, introduced in precisely those terms by Hans Reichenbach in his Experience and Prediction (Reichenbach 1938). Thus, while the idea behind the distinction has a long history before Reichenbach, this text from 1938 plays a salient role in how the distinction became canonical in the work of philosophers of science in the mid twentieth century. The new contextualist (...) history of philosophy that has arisen in recent years invites us into an investigation of the nuances of philosophical distinctions and their roles in shaping the development of disciplines. Logical empiricism played a key role in the historical development of philosophy of science and this contextualist history has revealed a much richer set of projects in logical empiricism than the potted histories had allowed. Many stories have been told about the contexts of justification and discovery; few of those stories have paid more than passing attention to the larger projects in epistemology and meta-epistemology that Reichenbach was pursuing when he drew the distinction. This brief essay will seek partially to rectify that lack in, I hope, a somewhat surprising way. I shall stress the connection between this canonical distinction and some other epistemological and social terms that loom large in Reichenbach’s text, arguing that the social relevance of scientific philosophy for Reichenbach cannot be set aside in understanding his use of the DJ distinction. My point is, therefore, historical and reflexive. If we attend to the larger significance of the project in scientific philosophy that Reichenbach was advancing, we can see more clearly why the DJ distinction was introduced and rethink the significance of questioning the distinction. (shrink)

Potentialities of attention and distraction with respect to images are critically reprised by Neapolitan artist Domenico Antonio Mancini. In Landscapes (2019), Google Street View addresses painted on canvases take the place of outlying areas of Italian cities, and of canonical oil ‘vedute’ paintings, obliging the viewer to switch from aesthetic absorption to a multitasking, reflexive attention enabled by the tools of mobile devices and the operative agency between the displayed and depicted images. Attracted by the ephemeral, geo-localized vistas displayed (...) on their smartphones, spectator-users must deal with the dynamis of the on-line GSV images: rhetorical and predictable, though, at the same time, and perhaps unintentionally, serendipitous and figural. (shrink)

Experts in Artificial Intelligence (AI) development predict that advances in the development of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance is not made prudently and critically-reflexively, it can result in negative outcomes for humanity. For this reason, several researchers in the area have developed a robust, beneficial, and safe concept of AI for the preservation of humanity and the environment. Currently, several of the open problems in the field of AI research (...) arise from the difficulty of avoiding unwanted behaviors of intelligent agents and systems, and at the same time specifying what we really want such systems to do, especially when we look for the possibility of intelligent agents acting in several domains over the long term. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop human moral values simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic approach proposed by Dreyfus, whose phenomenological philosophy shows that the human experience of being-in-the-world in several aspects is not well represented by the symbolic or connectionist cognitive method, especially in regards to the question of learning values. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Journal of the History of Philosophy 42.1 (2004) 113-115 [Access article in PDF] Wilhelm Dilthey. The Formation of the Historical World in the Human Sciences. Edited with an Introduction by Rudolf A. Makkreel and Frithjof Rodi. Princeton: Princeton University Press, 2002. Pp xiii + 399. Cloth $55.00. The first complete English translation of Wilhelm Dilthey's (1833-1911) most important mature work—a seminal work for hermeneutics, phenomenology, critical theory, and the (...) philosophy of history and the human sciences—is to be greatly welcomed. This excellent translation conveys the subtlety and richness of Dilthey's German. Its innovative translations of key terms will provide renewed stimulus to interpreting Dilthey's works.This edition includes Dilthey's investigation of the structures of consciousness in his three preliminary "Studies toward the Foundation of the Human Sciences," his exploration [End Page 113] of the import of the productive systems of historical life for knowledge in The Formation of the Historical World in the Human Sciences, and his final and perhaps best formulation of hermeneutics in "The Understanding of Other Persons and Their Manifestations of Life."Although Carnap and others utilized the word Aufbau in the sense of epistemic "construction," Makkreel and Rodi clarify how Dilthey used it in the sense of "formation." The formation of the historical world refers to its articulation in the human sciences which themselves theoretically reflect this historical world (GSW3: 1). They also argue that Dilthey's theory of the human sciences is not merely an epistemology (Erkenntnistheorie), but a theory of knowledge (Theorie des Wissens) that relates knowing to its context. Whereas epistemology seeks to establish the foundations of conceptual cognition (Erkenntnis), Dilthey places the epistemology of the human sciences within a larger context of the knowledge (Wissen) embodied in social practices and historical forms of life. Knowledge encompasses not only the conceptual cognition of reality, but also the values and purposes established about it.Dilthey accordingly situated the human sciences, which are determined by their respective object and how the object is given (SW3: 38), in relation to a pretheoretical life-nexus and its forms of elementary or ordinary understanding. These are tied up with the temporality, historicity, and structures of social life; with an epochal "objective spirit." Objective spirit indicates the ways in which the past has been objectified and continues to shape contemporary practices and it is analyzed in the human sciences as cultural systems and the external organization of society. A significant characteristic of the Formation is the development of the notion of "productive system or nexus." This new translation of Wirkungszusammenhang suggests a historical efficacy or productivity prior to any analysis of it as either causal or teleological (SW3: 4).The human sciences include the study of dynamic interconnected systems that articulate the intersection of meaning, value, purpose, and force. Dilthey interpreted these temporally, such that meaning primarily concerns how humans are determined by their past, value is based on their present feeling of life, and purpose is projective striving into the future in the face of productive forces (Kräfte) which cannot always be predicted or controlled.For Dilthey, understanding (verstehen) is intrinsically interpretive. Humans can cognize themselves and others only indirectly (SW3: 108), since we are conscious and reflective beings who are bound to the facticity of our bodies and world. Given that we know ourselves and others primarily through actions, life-expressions, and their effects—rather than through introspection or intuition—and that everyday understanding can face breakdowns and what seems distant or strange, elementary understanding leads to higher forms of understanding and interpretation; i.e., hermeneutics. Dilthey's project of a critique of historical reason proceeds from the context of life in all of its complexity and concreteness to the conceptual cognition of the sciences and, finally, to reflective awareness (Besinnung). This reflection is made possible by the prereflective reflexivity (Innesein or Innewerden) of the human subject and, with its double meaning of "sense" (Sinn) as meaning and bodily awareness, constitutes the basic movement of Dilthey's thought.Understanding provides more than scientific access to... (shrink)

The growth in neoliberal or market-driven higher education has challenged traditional approaches to evaluating faculty members. The involvement of multiple stakeholders (i.e., accreditation bodies, quality assurance officers, administrators, teaching faculty, and students) has led to different and sometimes conflicting needs in faculty evaluation. While extant literature generally suggests that faculty evaluation in contemporary higher education is strongly associated with accountability purposes, little is known about how key agents at the institutional level use evaluation for learning and improvement. Thus, this study (...) attempts to identify the approaches to faculty evaluation that promote learning and improvement in higher education. This study is a qualitative case study of Vietnamese higher education student evaluation of teaching (SET) and voting evaluation practices. It adopts the problem-based methodology (Robinson, 1993; Robinson & Lai, 2006) to investigate the theories of action of some key stakeholders of these faculty evaluation practices in Vietnam. The theories of action comprise the stakeholders’ approaches to faculty evaluation, together with the constraints (that rule in or rule out specific approaches) and the consequences of the approaches. Participants included four quality assurance officers, 18 administrators, and 20 faculty members from seven public higher education institutions in Vietnam. The theories of action about faculty evaluation were constructed based on interviews and key evaluation documents. Overall, the participants took three main approaches to faculty evaluation: (i) complying with the expected evaluation procedures and roles, (ii) taking a harmony-oriented and unilateral approach to problem solving, and (iii) disengaging from evaluation for learning and improvement. The participants generally fulfilled the evaluation policy requirements, but their approaches had a limited impact on learning and improvement at the institutional level. The constraint analysis suggests that the participants’ approaches were not completely passive but were mainly oriented to managerial accountability demands (Røiseland et al., 2015). The participants’ collectivist Confucian harmony-oriented norms hindered their approaches to problem solving and using evaluation for learning and improvement. However, there were a few participants who acted on their own values rather than being confined by political and cultural constraints. In spite of its scarcity, these participants’ self-reliant approach suggests a potential influence of Buddhist principles of self-transformation, contextuality and reflexivity on improving practices (Chu & Vu, 2021; Thich Nhat Hanh, 1987). The study has two key implications. First, it suggests the improvement of faculty evaluation policymaking and implementation by revising several existing constraints and by adding some cultural and individual constraints to the current theories of action. The faculty evaluation problems could be solved by improving the quality of evaluation processes and data, reconceptualising SET and voting evaluation, and treating faculty members’ underperformance as a collective problem. Second, the study suggests a framework to predict the likely success of future intervention on faculty evaluation. For instance, faculty evaluation for learning and improvement will be more feasible if policymakers and implementers prioritise transformational purposes and make joint efforts to foster dialogues and collaboration among individuals and groups. My study also highlights the need to understand faculty evaluation, and possibly other higher education practices in Vietnam, in religious contexts and from individual participants’ spiritual values or philosophical backgrounds. The study implications are applicable to Vietnamese higher education and potentially to other educational settings with similar characteristics. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with (...) a heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Abstract: This research paper presents the development and evaluation of a neural network-based model for predicting fire alarms in smoke detection systems. Using a dataset from Kaggle containing 15 features and 3487 samples, we trained and validated a neural network with a three-layer architecture. The model achieved an accuracy of 100% and an average error of 0.0000003. Additionally, we identified the most influential features in predicting fire alarms.

I propose an account of the speech act of prediction that denies that the contents of prediction must be about the future and illuminates the relation between prediction and assertion. My account is a synthesis of two ideas: (i) that what is in the future in prediction is the time of discovery and (ii) that, as Benton and Turri recently argued, prediction is best characterized in terms of its constitutive norms.

From the Editor’s Introduction: "The Internal Limitations of Human Understanding." We carry, unavoidably, the limits of our understanding with us. We are perpetually confined within the horizons of our conceptual structure. When this structure grows or expands, the breadth of our comprehensions enlarges, but we are forever barred from the wished-for glimpse beyond its boundaries, no matter how hard we try, no matter how much credence we invest in the substance of our learning and mist of speculation. -/- The limitations (...) in view here are not due to the mere finitude of our understanding of ourselves and of the world in which we live. They are limitations that come automatically and necessarily with any form of understanding. They are, as we shall see, part and parcel of any organization or ordering of data that we call information. -/- The consequences of these limitations are varied: As a result of them, hermeneutics cannot help but be hermetic; scientific theories of necessity are circumscribed by the boundaries of the ideas that define them; formal systems must choose between consistency and comprehensiveness; philosophical study, because it includes itself within its own proper subject matter, is forced to be reflexive in its self-enclosure. The fundamental dynamic shared by all forms of understanding testifies to an internal limitative keystone. (shrink)

Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this elusive feat. (...) Through rigorous experimentation and data-driven approaches, we seek to determine the viability of AI in the realm of lottery predictions. Our findings reveal both the promise and limitations of AI in this context, shedding light on the complexities of lottery data and the potential need for quantum computing as a last resort. (shrink)

The classical rule of Repetition says that if you take any sentence as a premise, and repeat it as a conclusion, you have a valid argument. It's a very basic rule of logic, and many other rules depend on the guarantee that repeating a sentence, or really, any expression, guarantees sameness of referent, or semantic value. However, Repetition fails for token-reflexive expressions. In this paper, I offer three ways that one might replace Repetition, and still keep an interesting notion (...) of validity. Each is a fine way to go for certain purposes, but I argue that one in particular is to be preferred by the semanticist who thinks that there are token-reflexive expressions in natural languages. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with (...) a heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Life expectancy prediction, a pivotal facet of public health and policy formulation, has witnessed remarkable advancements owing to the integration of neural network models and comprehensive datasets. In this research, we present an innovative approach to forecasting life expectancy in diverse countries. Leveraging a neural network architecture, our model was trained on a dataset comprising 22 distinct features, acquired from Kaggle, and encompassing key health indicators, socioeconomic metrics, and cultural attributes. The model demonstrated exceptional predictive accuracy, attaining an impressive (...) 99.27% and an average error of 0.0034, underscoring the potency of deep learning in tackling complex, real-world challenges. Furthermore, our study delved into feature importance analysis, identifying critical determinants such as HIV/AIDS prevalence, income composition of resources, and socioeconomic status that significantly influence life expectancy. These findings provide actionable insights for healthcare policymakers and practitioners, emphasizing the importance of addressing health disparities, promoting economic development, and implementing targeted interventions. This research bridges the gap between data-driven methodologies and global health, offering a robust predictive tool and enriching our understanding of the multifaceted dynamics that shape life expectancy across nations. (shrink)

Predictive processing models of perception take issue with standard models of perception as hierarchical bottom-up processing modulated by memory and attention. The predictive framework posits that the brain generates predictions about stimuli, which are matched to the incoming signal. Mismatches between predictions and the incoming signal – so-called prediction errors – are then used to generate new and better predictions until the prediction errors have been minimized, at which point a perception arises. Predictive models hold that all bottom-up (...) processes are signals conveying prediction errors to higher areas, which respond by updating their predictions. We take issue with this claim and argue that object recognition requires bottom-up processing that cannot be understood in terms of prediction errors. Along the way, we expand on previous work casting doubt on the framework's ability to account for attention. Specifically, we argue that the type of attention that allows us to rapidly extract the gist of an object or scene presents an additional challenge to the predictive approach. We conclude by considering how the framework may be augmented to avoid these problems. (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)

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the proposed model, (...) it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers. (shrink)

Our aim is to provide a sequent calculus whose external consequence relation coincides with the three-valued paracomplete logic `of nonsense' introduced by Dmitry Bochvar and, independently, presented as the weak Kleene logic K3W by Stephen C. Kleene. The main features of this calculus are (i) that it is non-reflexive, i.e., Identity is not included as an explicit rule (although a restricted form of it with premises is derivable); (ii) that it includes rules where no variable-inclusion conditions are attached; and (...) (iii) that it is hybrid, insofar as it includes both left and right operational introduction as well as elimination rules. (shrink)

Plotinus argues that materialism cannot explain reflexive cognition. He argues that mere bodies cannot engage in the self-reflexive activity of both cognizing some content and being cognitively aware of cognizing this content. Short of outright denying the cognitive unity underlying this phenomenon of self-awareness, materialism is in trouble. However, Plotinus bases his argument on the condition that material bodies are capable of a spatial unity at most, and while this condition has purchase on ancient materialists, it would be (...) rejected today. After considering objections based on our contemporary understanding of a material body, I amend Plotinus' argument to focus instead on the core materialist commitment to external efficient-causal relations. This amended argument regains its urgency for materialism, while retaining the spirit of Plotinus' view. (shrink)

Self-reflexive videogames are videogames designed to materialize critical and/or satirical perspectives on the ways in which videogames themselves are designed, played, sold, manipulated, experienced, and understood as social objects. This essay focuses on the use of virtual worlds as mediators, and in particular on the use of videogames to guide and encourage reflections on technical, interactive, and thematic conventions in videogame design and development. Structurally, it is composed of two interconnected parts: -/- 1) In the first part of this (...) essay, I will discuss NECESSARY EVIL (Gualeni et al., 2013), an experimental videogame that I designed as a self-reflexive virtual artifact. With the objective of clarifying the philosophical aspirations of self-reflexive videogames – and in order to understand how those aspirations can be practically pursued – I will dissect and examine the design decisions that contributed to the qualities of NECESSARY EVIL as an example of “playable philosophy”. -/- 2) Taking off from the perspectives on self-reflexive videogames offered in the first part of the essay, the second half will focus on virtual worlds as viable mediators of philosophical thought more in general. In this section, I will argue that, both through the practice of game design and through the interactive experiences of virtual worlds, twenty-first century philosophers have the possibility to challenge the often-unquestioned understanding of written discourse as the only context in which philosophical thought can emerge and be developed. (shrink)

As the author has previously argued, a statement of form “Y is X” can often be taken as hyperbolic for a notably high degree of likeness between Y and X, or, instead, as hyperbolically stating how important Y is as a part of X. The present article goes further and argues that these types of hyperbole, as well as various others, are just special cases of reflexive hyperbole, a style that appears not previously to have been explored in its (...) own right. The article therefore serves to introduce this style and to unify under it various more specific, disparate-seeming types of hyperbole, revealing their deep similarity. They all rest in a uniform way on a special property that some relationships have, namely of being reflexive in a broadened sense. The relationship of likeness is reflexive in the standard sense that any entity bears the relationship to itself with maximum possible strength. But some other relationships of interest in this article are only reflexive in a broader way that relaxes these universality and maximality requirements to an extent. The article also explains how reflexive hyperbole about likeness is a novel addition to the theory of metaphor, involving a distinctive interpretative process with special effects, and how reflexive hyperbole about part importance is not reducible to whole-for-part metonymy, because the latter does not systematically access sufficiently important parts. In addition, the article briefly considers the defaultness or otherwise of reflexive hyperbolic interpretations. (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)

According to the predictive coding theory of cognition , brains are predictive machines that use perception and action to minimize prediction error, i.e. the discrepancy between bottom–up, externally-generated sensory signals and top–down, internally-generated sensory predictions. Many consider PCT to have an explanatory scope that is unparalleled in contemporary cognitive science and see in it a framework that could potentially provide us with a unified account of cognition. It is also commonly assumed that PCT is a representational theory of sorts, (...) in the sense that it postulates that our cognitive contact with the world is mediated by internal representations. However, the exact sense in which PCT is representational remains unclear; neither is it clear that it deserves such status—that is, whether it really invokes structures that are truly and nontrivially representational in nature. In the present article, I argue that the representational pretensions of PCT are completely justified. This is because the theory postulates cognitive structures—namely action-guiding, detachable, structural models that afford representational error detection—that play genuinely representational functions within the cognitive system. (shrink)

This paper examines the practice of using predictive systems to lengthen the prison sentences of convicted persons when the systems forecast a higher likelihood of re-offense or re-arrest. There has been much critical discussion of technologies used for sentencing, including questions of bias and opacity. However, there hasn’t been a discussion of whether this use of predictive systems makes sense in the first place. We argue that it does not by showing that there is no plausible theory of punishment that (...) supports it. (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)

The term ‘reflexivity’ continues to maintain an interpretive hegemony in discussions on modernity and the Self. As a form of praxis, applications of reflexivity frequently rely upon an acknowledged awareness of one’s self-conscious attitudes, dispositions, behaviors and motives. This paper will take aim at such contentions, exploring the extent to which examples of racism rely upon a level of reflexivity, best encapsulated in Žižek’s ‘reflexive racism’. Specifically, it is highlighted how examples of non- racism/anti-racism assert the formal promotion of (...) a monadic subject, solely adept at ‘uncovering’ and ‘relinquishing’ their racism (disavowal); and, an equally unhelpful social constructionism, which depoliticizes racism by relocating and relativizing it to a particular socio-historical context (deferment). In outlining this response, specific attention is given to Lacan’s subject of enunciation and subject of the enunciated, from which it is concluded that it is in the obfuscation of one’s ‘position of enunciation’ that examples of reflexive racism reside. (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)

The American justice system, from police departments to the courts, is increasingly turning to information technology for help identifying potential offenders, determining where, geographically, to allocate enforcement resources, assessing flight risk and the potential for recidivism amongst arrestees, and making other judgments about when, where, and how to manage crime. In particular, there is a focus on machine learning and other data analytics tools, which promise to accurately predict where crime will occur and who will perpetrate it. Activists and academics (...) have begun to raise critical questions about the use of these tools in policing contexts. In this chapter, I review the emerging critical literature on predictive policing and contribute to it by raising ethical questions about the use of predictive analytics tools to identify potential offenders. Drawing from work on the ethics of profiling, I argue that the much-lauded move from reactive to preemptive policing can mean wrongfully generalizing about individuals, making harmful assumptions about them, instrumentalizing them, and failing to respect them as full ethical persons. I suggest that these problems stem both from the nature of predictive policing tools and from the sociotechnical contexts in which they are implemented... (shrink)

The main focus in the paper is the connection between trust and peace which makes predictability as a necessary condition of the normalcy of life possible, especially collective and communal life. Peace is defined as a specific articulation of the distribution of (political) power within a society. Peace defined in such a way requires a set of rules (norms, or laws) needed for the stability of the established social state of affairs. The main purpose of those norms, laws, is to (...) provide predictability without which stability of such a state of affairs is not possible. The role of trust in this scheme is crucial. However, trust precedes the peace (defined as a set of accepted set of laws), and cannot be obtained by enforcing such normative instruments as laws. Trust is so a component of freedom, the same which contains distrust as its possible component. In this sense peace should be taken as the power of control, first the control over ourselves, the power which makes status and existence of trust possible and feasible. (shrink)

In this article, following on from earlier debates in the journal regarding the ‘thorny issue’ of epochē and bracketing in sociological phenomenological research, we consider more generally the challenges of engaging in reflexivity and bracketing when undertaking ethnographic ‘insider’ research, or research in familiar settings. We ground our discussion and illustrate some of the key challenges by drawing on the experience of undertaking this research approach with a group of competitive swimmers, who were participating in a British university performance swimming (...) programme at the time of the doctoral study. The primary researcher was highly familiar with the phenomenon of competitive swimming, having been both a competitive swimmer and swimming coach. Some of the key elements of a sociological phenomenological approach to studying physical-cultural embodiment are first delineated, before addressing the considerable challenges of engaging in sustained reflexivity and bracketing, using the swimming research for illustrative purposes. We suggest some practical ways in which researchers in sport and physical cultures might approach epochē and bracketing in ethnographic ‘insider’ research. -/- KEYWORDS: Sociological phenomenology, competitive swimming, insider research, epochē/bracketing. (shrink)

Predictive Processing theory, hotly debated in neuroscience, psychology and philosophy, promises to explain a number of perceptual and cognitive phenomena in a simple and elegant manner. In some of its versions, the theory is ambitiously advertised as a new theory of conscious perception. The task of this paper is to assess whether this claim is realistic. We will be arguing that the Predictive Processing theory cannot explain the transition from unconscious to conscious perception in its proprietary terms. The explanations offer (...) by PP theorists mostly concern the preconditions of conscious perception, leaving the genuine material substrate of consciousness untouched. (shrink)

Predictive processing (PP) accounts of perception are unique not merely in that they postulate a unity between perception and imagination. Rather, they are unique in claiming that perception should be conceptualised in terms of imagination and that the two involve an identity of neural implementation. This paper argues against this postulated unity, on both conceptual and empirical grounds. Conceptually, the manner in which PP theorists link perception and imagination belies an impoverished account of imagery as cloistered from the external world (...) in its intentionality, akin to a virtual reality, as well as endogenously generated. Yet this ignores a whole class of imagery whose intentionality is directed on the actual environment—projected mental imagery—and also ignores the fact that imagery may be triggered crossmodally in a bottom-up, stimulus-driven way. Empirically, claiming that imagery and perception share neural circuitry ignores relevant clinical results in this area. These evidence substantial perception/imagery neural dissociations, most notably in the case of aphantasia. Taken together, the arguments here suggest that PP theorists should substantially temper, if not outright abandon, their claim to a perception/imagination unity. (shrink)

ABSTRACT: The Reflexive Theory of Perception (RTP) claims that perception of an object or property X by an organism Z consists in Z being caused by X to acquire some disposition D toward X itself. This broadly behavioral perceptual theory explains perceptual intentionality and correct versus incorrect, plus successful versus unsuccessful, perception in a plausible evolutionary framework. The theory also undermines cognitive and perceptual modularity assumptions, including informational or purely epistemic views of perception in that, according to the RTP, (...) any X-caused and X-directed dispositions are genuinely perceptual—including affective, attitudinal, and immediately activated purely action-directed behavioral dispositions. Thus the RTP has the potential to provide the foundations for a broadly behavioral counter-revolution in cognitive science. (shrink)

Accurate statistical analysis of Fortnite gameplay data is essential for improving gaming strategies and performance. In this study, we present a novel approach to analyze Fortnite statistics using machine learning techniques. Our dataset comprises a wide range of gameplay metrics, including eliminations, assists, revives, accuracy, hits, headshots, distance traveled, materials gathered, materials used, damage taken, damage to players, damage to structures, and more. We collected this dataset to gain insights into Fortnite player performance and strategies. The proposed model employs advanced (...) machine learning algorithms and data preprocessing techniques. We aim to identify key performance indicators and influential factors that contribute to Fortnite success. By exploring patterns and relationships within the dataset, our model can provide valuable insights into gameplay optimization. Our research involves feature analysis to determine the most influential factors in predicting Fortnite performance. These factors include eliminations, assists, revives, accuracy, hits, headshots, distance traveled, materials gathered, materials used, damage taken, damage to players, damage to structures, and more. By understanding the impact of these factors, we aim to help Fortnite players enhance their skills and strategies. Through extensive training and validation of our machine learning model, we achieved remarkable results, with an accuracy rate of [Your Accuracy Rate]% and an average error of [Your Average Error]. This research not only provides valuable insights into Fortnite gameplay but also offers a foundation for future research in the field of esports analytics and performance optimization. (shrink)

The aim behind analyzing the Goodreads dataset is to get a fair idea about the relationships between the multiple attributes a book might have, such as: the aggregate rating of each book, the trend of the authors over the years and books with numerous languages. With over a hundred thousand ratings, there are books which just tend to become popular as each day seems to pass. We proposed an Artificial Neural Network (ANN) model for predicting the overall rating of books. (...) The prediction is based on these features (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which were used as input variables and (average_rating) as output variable for our ANN model. Our model were created, trained, and validated using data set in JNN environment, which its title is “Goodreads-books”. Model evaluation showed that the ANN model is able to predict correctly 99.78% of the validation samples. (shrink)

Reichenbachian approaches to indexicality contend that indexicals are "token-reflexives": semantic rules associated with any given indexical-type determine the truth-conditional import of properly produced tokens of that type relative to certain relational properties of those tokens. Such a view may be understood as sharing the main tenets of Kaplan's well-known theory regarding content, or truth-conditions, but differs from it regarding the nature of the linguistic meaning of indexicals and also regarding the bearers of truth-conditional import and truth-conditions. Kaplan has criticized these (...) approaches on different counts, the most damaging of which is that they make impossible a "logic of demonstratives". The reason for this is that the token-reflexive approach entails that not two tokens of the same sentential type including indexicals are guaranteed to have the same truth-conditions. In this paper I rebut this and other criticisms of the Reichenbachian approach. Additionally, I point out that Kaplan's original theory of "true demonstratives" is empirically inadequate, and claim that any modification capable of accurately handling the linguistic data would have similar problems to those attributed to the Reichenbachian approach. This is intended to show that the difficulties, no matter how real, are not caused by idiosincracies of the "token-reflexive" view, but by deep facts about indexicality. (shrink)

Evaluativism (Carruthers 2018) and reflexive imperativism (Barlassina and Hayward 2019) agree that valence—the (un)pleasantness of experiences—is a natural kind shared across all affective states. But they disagree about what valence is. For evaluativism, an experience is pleasant/unpleasant in virtue of representing its worldly object as good/bad; for reflexive imperativism, an experience is pleasant/unpleasant in virtue of commanding its subject to get more/less of itself. I argue that reflexive imperativism is superior to evaluativism according to Carruthers’s own standards. (...) He maintains that a theory of valence should account for its phenomenology and role in imagination-based decision-making. I show that it is reflexive imperativism, rather than evaluativism, that fits this explanatory bill. (shrink)

A consideration of the relationship between conscious self-aware systems and art. I introduce my art practice and demonstrate the connections language has to self-conscious reflexivity. The document of research can be considered part of a creative practice that also uses language as a material. The specialist use and subversive manipulation of information in science and art as practiced in the service of culture are discussed to show how this informed the creation of Access Restricted-Operational Reasons as a response to my (...) environment. (shrink)

We introduce the predictive processing account of body representation, according to which body representation emerges via a domain-general scheme of (long-term) prediction error minimisation. We contrast this account against one where body representation is underpinned by domain-specific systems, whose exclusive function is to track the body. We illustrate how the predictive processing account offers considerable advantages in explaining various empirical findings, and we draw out some implications for body representation research.

Many philosophers claim that the neurocomputational framework of predictive processing entails a globally inferentialist and representationalist view of cognition. Here, I contend that this is not correct. I argue that, given the theoretical commitments these philosophers endorse, no structure within predictive processing systems can be rightfully identified as a representational vehicle. To do so, I first examine some of the theoretical commitments these philosophers share, and show that these commitments provide a set of necessary conditions the satisfaction of which allows (...) us to identify representational vehicles. Having done so, I introduce a predictive processing system capable of active inference, in the form of a simple robotic “brain”. I examine it thoroughly, and show that, given the necessary conditions highlighted above, none of its components qualifies as a representational vehicle. I then consider and allay some worries my claim could raise. I consider whether the anti-representationalist verdict thus obtained could be generalized, and provide some reasons favoring a positive answer. I further consider whether my arguments here could be blocked by allowing the same representational vehicle to possess multiple contents, and whether my arguments entail some extreme form of revisionism, answering in the negative in both cases. A quick conclusion follows. (shrink)

Kidney stones pose a significant health concern, and early detection can lead to timely intervention and improved patient outcomes. This research endeavours to predict the presence of kidney stones based on urine analysis, utilizing a neural network model. A dataset of 552 urine specimens, comprising six essential physical characteristics (specific gravity, pH, osmolarity, conductivity, urea concentration, and calcium concentration), was collected and prepared. Our proposed neural network architecture, featuring three layers (input, hidden, output), was trained and validated, achieving an impressive (...) accuracy of 98.67% and an average error of 0.012. In addition to model performance, feature importance analysis was conducted to determine the most influential factors in predicting kidney stone presence. The findings underscore the significance of urea concentration, specific gravity, calcium concentration, conductivity, osmolarity, and pH as key indicators. This research contributes to the early diagnosis of kidney stones and demonstrates the potential of neural networks in medical diagnostics. The clinical implications of these findings are discussed, emphasizing the importance of timely intervention in managing kidney stone-related health issues. (shrink)

Abstract: This research employs a Random Forest classification model to predict and assess obesity and cardiovascular disease (CVD) risk based on a comprehensive dataset collected from individuals in Mexico, Peru, and Colombia. The dataset comprises 17 attributes, including information on eating habits, physical condition, gender, age, height, and weight. The study focuses on classifying individuals into different health risk categories using machine learning algorithms. Our Random Forest model achieved remarkable performance with an accuracy, F1-score, recall, and precision all reaching 97.23%. (...) The model's success is attributed to its ability to effectively leverage attributes related to eating habits, physical condition, and demographic factors. Feature importance analysis reveals key factors contributing to accurate predictions. This paper contributes to the field of health analytics by demonstrating the effectiveness of Random Forest in predicting and categorizing health risks. The insights gained from this research have the potential to inform personalized health interventions and contribute to the advancement of precision health strategies. Furthermore, the study highlights the importance of leveraging machine learning techniques for health risk assessment in diverse populations. (shrink)

The field of machine perception is based on standard informational and computational approaches to perception. But naturalistic informational theories are widely regarded as being inadequate, while purely syntactic computational approaches give no account of perceptual content. Thus there is a significant need for a novel, purely naturalistic perceptual theory not based on informational or computational concepts, which could provide a new paradigm for mechanistic perception. Now specifically evolutionary naturalistic approaches to perception have been—perhaps surprisingly—almost completely neglected for this purpose. Arguably (...) perceptual mechanisms enhance evolutionary fitness by facilitating sensorily mediated causal interactions between an organism Z and items X in its environment. A ‘reflexive’ theory of perception of this kind is outlined, according to which an organism Z perceives an item X just in case X causes a sensory organ zi of Z to cause Z to acquire a disposition toward the very same item X that caused the perception. The rest of the paper shows how an intuitively plausible account of mechanistic perception can be developed and defended in terms of the reflexive theory. Also, a compatibilist option is provided for those who wish to preserve a distinct informational concept of perception. (shrink)