I would like to assume that Reichenbach's distinction of Justification and Discovery lives on, and to seek arguments in his texts that would justify their relevance in this field. The persuasive force of these arguments transcends the contingent circumstances apart from which their genesis and local transmission cannot be made understandable. I shall begin by characterizing the context distinction as employed by Reichenbach in "Experience and Prediction" to differentiate between epistemology and science (1). Following Thomas Nickles and Kevin T. (...) Kelly, one can distinguish two meanings of the context distinction in Reichenbach's work. One meaning, which is primarily to be found in the earlier writings, conceives of scientific discoveries as potential objects of epistemological justification. The other meaning, typical for the later writings, removes scientific discoveries from the possible domain of epistemology. The genesis of both meanings, which demonstrates the complexity of the relationships obtaining between epistemology and science, can be made understandable by appealing to the historical context (2). Both meanings present Reichenbach with the task of establishing the autonomy of epistemology through the justification of induction. Finally, I shall expound this justification and address some of its elements of rationality characterizing philosophy of science(3). (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)

The predictive processing literature contains at least two different versions of the framework with different theoretical resources at their disposal. One version appeals to so-called optimistic priors to explain agents’ motivation to act (call this optimistic predictive processing). A more recent version appeals to expected free energy minimization to explain how agents can decide between different action policies (call this preference predictive processing). The difference between the two versions has not been properly appreciated, and they are not sufficiently separated in (...) the literature. They constitute two different theories with strikingly different accounts of motivation and action. By reducing all desire-like constructs to belief-like constructs, optimistic predictive processing entails a substantial revision of standard accounts of motivation and action in philosophy and cognitive science. By contrast, preference predictive processing introduces desire-like constructs that play Humean motivational roles in the explanation of action. In this Humean iteration, predictive processing resembles other prominent computational frameworks implementing a distinction between beliefs and desires, such as reinforcement learning and Bayesian decision theory. Ultimately, predictive processing faces a dilemma between parsimony of mental constructs and completeness of its explanations of agency and the mind. (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)

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 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)

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)

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)

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.

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.

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.

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)

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)

In the last decade, the cases of breast and cervical cancer have been positioned at the top rank of cancer statistics worldwide. Consequently, many husbands become family caregivers (FCGs) and get the burden of cancer caregiving. Being blind and incompetent, they need supportive care from healthcare professionals (HCPs). To support them, HCPs provide various healthcare information to meet their needs. Further, their demand for a specific type of healthcare information may reflect their need for emotional support from the HCPs to (...) reduce the burden. This study aims to examine the predictors of the need for emotional support among FCGs of female cancer patients by analyzing the demanded healthcare information. The mindsponge theory was used in conceptual development and interpretation. Bayesian Mindsponge Framework (BMF) analytics were used for statistical analysis on a dataset of 48 husbands and 12 significant others in five congested communities of Surabaya, Indonesia. Results showed that among the six types of healthcare information, the demanded cancer-specific information and information on cancer physical needs significantly predict the need for emotional support among respondents. Meanwhile, FCGs with a higher demand for information on alternative therapies are less likely to need emotional support. Other types of information have ambiguous effects on the need for emotional support. This study reveals that the demanded cancer-specific information, e.g., cancer prognosis or likely outcome, and information on cancer physical needs must be prioritized in information disclosure to meet the needs for emotional support among FCGs of female cancer patients. (shrink)

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)

Should we insist on prediction, i.e. on correctly forecasting the future? Or can we rest content with accommodation, i.e. empirical success only with respect to the past? I apply general considerations about this issue to the case of economics. In particular, I examine various ways in which mere accommodation can be sufficient, in order to see whether those ways apply to economics. Two conclusions result. First, an entanglement thesis: the need for prediction is entangled with the methodological role (...) of orthodox economic theory. Second, a conditional predictivism: if we are not committed to orthodox economic theory, then we should demand prediction rather than accommodation – against most current practice. (shrink)

The folk Psychology frames propositional attitudes as fundamental theoretical entities for the construction of a model designed to predict the behavior of a subject. A trivial, such as grasping a pen and writing reveals - something complex - about the behavior. When I take a pen and start writing I do, trivially, because I believe that a certain object in front of me is a pen and who performs a specific function that is, in fact, that of writing. When I (...) believe that the object that stands before me is a pen, I am in relation to "believe" with the propositional content: that in front of me is a pen. Philosophers of the proposition, from Frege onwards, have dedicated their studies to the analysis of what kinds of entities are the propositional attitudes. Jerry Fodor says that now, the proper prediction of the psychology of common sense, can not be questioned and that the propositional attitudes represent the most effective way to describe our behavior. What Fodor says, however, is that propositional attitudes function, but not how they work. Most philosophers interested in the issue, we are dedicated to the search for a theory that can account consistently both a semantics for propositional attitudes, both of these entities that seem to cause the behavior of a rational subject. There are two main paradigms in the theory of the proposition that contributed to the discussion of the propositional attitudes. One is the one that begins with Gottlob Frege, the other with Bertrand Russell. Defenders of Frege argue that the paradigm scrub objects and properties can not be constituents of the propositional content which have a purely conceptual. In other words, the philosophers belonging to the paradigm of Frege, but not all, mean that you can test in a rigorous way the truth conditions of propositional attitudes. Who defends the russellian’s paradigm argues that the propositional content are made by the objects and properties on which propositional attitudes relate. The purpose of this article is not to rebuild - in detail - both paradigms, nor to reconstruct one but, in a sense, my work will be a completely partial objective is to demonstrate how the paradigm is more profitable russell not only to make a coherent semantic theory for propositional attitudes2 but also to predict the behavior of a rational subject thing, completely innovative, given the repeated objections in contemporary literature3. At the end of this paper will be drafted a proposal to build a consistent model to predict the behavior,of a rational agent, based on a referential theory of propositional attitudes. (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)

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)

The use of machine learning, or “artificial intelligence” (AI) in medicine is widespread and growing. In this paper, I focus on a specific proposed clinical application of AI: using models to predict incapacitated patients’ treatment preferences. Drawing on results from machine learning, I argue this proposal faces a special moral problem. Machine learning researchers owe us assurance on this front before experimental research can proceed. In my conclusion I connect this concern to broader issues in AI safety.

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)

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a (...) dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%. (shrink)

In this paper an Artificial Neural Network (ANN) model, for predicting the category of a tumor was developed and tested. Taking patients’ tests, a number of information gained that influence the classification of the tumor. Such information as age, sex, histologic-type, degree-of-diffe, status of bone, bone-marrow, lung, pleura, peritoneum, liver, brain, skin, neck, supraclavicular, axillar, mediastinum, and abdominal. They were used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was established and trained using (...) data set which its title is “primary tumor” and was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia Test data evaluation shows that the ANN model is able to correctly predict the tumor category with 76.67 % accuracy. (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)

Despite their popularity, relatively scant attention has been paid to the upshot of Bayesian and predictive processing models of cognition for views of overall cognitive architecture. Many of these models are hierarchical ; they posit generative models at multiple distinct "levels," whose job is to predict the consequences of sensory input at lower levels. I articulate one possible position that could be implied by these models, namely, that there is a continuous hierarchy of perception, cognition, and action control comprising levels (...) of generative models. I argue that this view is not entailed by a general Bayesian/predictive processing outlook. Bayesian approaches are compatible with distinct formats of mental representation. Focusing on Bayesian approaches to motor control, I argue that the junctures between different types of mental representation are places where the transitivity of hierarchical prediction may be broken, and I consider the upshot of this conclusion for broader discussions of cognitive architecture. (shrink)

Shared activity is often simply willed into existence by individuals. This poses a problem. Philosophical reflection suggests that shared activity involves a distinctive, interlocking structure of intentions. But it is not obvious how one can form the intention necessary for shared activity without settling what fellow participants will do and thereby compromising their agency and autonomy. One response to this problem suggests that an individual can have the requisite intention if she makes the appropriate predictions about fellow participants. I argue (...) that implementing this predictive strategy risks derailing practical reasoning and preventing one from forming the intention. My alternative proposal for reconciling shared activity with autonomy appeals to the idea of acting directly on another's intention. In particular, I appeal to the entitlement one sometimes has to another's practical judgment, and the corresponding authority the other sometimes has to settle what one is to do. (shrink)

Inflationary cosmology has been widely accepted due to its successful predictions: for a “generic” initial state, inflation produces a homogeneous, flat, bubble with an appropriate spectrum of density perturbations. However, the discovery that inflation is “generically eternal,” leading to a vast multiverse of inflationary bubbles with different low-energy physics, threatens to undermine this account. There is a “predictability crisis” in eternal inflation, because extracting predictions apparently requires a well-defined measure over the multiverse. This has led to discussions of anthropic predictions (...) based on a measure over the multiverse, and an assumption that we are typical observers. I will give a pessimistic assessment of attempts to make predictions in this sense, emphasizing in particular problems that arise even if a unique measure can be found. (shrink)

Abstract: This study introduces a machine learning-based model for predicting student performance using a comprehensive dataset derived from educational sources, encompassing 15 key features and comprising 62,631 student samples. Our five-layer neural network demonstrated remarkable performance, achieving an accuracy of 89.14% and an average error of 0.000715, underscoring its effectiveness in predicting student outcomes. Crucially, this research identifies pivotal determinants of student success, including factors such as socio-economic background, prior academic history, study habits, and attendance patterns, shedding light on the (...) nuanced dynamics of student performance. The key influential features identified in this study offer valuable insights into the complex factors shaping student achievement. These insights are vital for educators, policymakers, and institutions seeking to enhance educational outcomes and promote equitable access to quality education. This research provides a data-driven foundation for proactive interventions, personalized learning strategies, and support systems, ultimately contributing to improved student performance and academic success. The high accuracy of the predictive model and the feature analysis it provides empower decision-makers in the education sector. This model holds significant potential for applications in student performance monitoring, early intervention, and tailored educational strategies. By adopting a data-driven approach, this work advances the field of educational analytics and contributes to the goal of fostering student success and educational equity. (shrink)

To succeed, political science usually requires either prediction or contextual historical work. Both of these methods favor explanations that are narrow-scope, applying to only one or a few cases. Because of the difficulty of prediction, the main focus of political science should often be contextual historical work. These epistemological conclusions follow from the ubiquity of causal fragility, under-determination, and noise. They tell against several practices that are widespread in the discipline: wide-scope retrospective testing, such as much large-n statistical (...) work; lack of emphasis on prediction; and resources devoted to ‘pure theory’ divorced from frequent empirical application. I illustrate, via Donatella della Porta’s work on political violence, the important role that is still left for theory. I conclude by assessing the scope for political science to offer policy advice. (shrink)

Regina Fabry has proposed an intriguing marriage of enculturated cognition and predictive processing. I raise some questions for whether this marriage will work and warn against expecting too much from the predictive processing framework. Furthermore I argue that the predictive processes at a sub-personal level cannot be driving the innovations at a social level that lead to enculturated cognitive systems, like those explored in my target paper.

John N. Williams (1994) and Matthew Weiner (2005) invoke predictions in order to undermine the normative relevance of knowledge for assertions; in particular, Weiner argues, predictions are important counterexamples to the Knowledge Account of Assertion (KAA). I argue here that they are not true counterexamples at all, a point that can be agreed upon even by those who reject KAA.

In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we argue that topological (...) predictions can and do guide interventions in science, both inside and outside of neuroscience. Topological models allow researchers to predict many phenomena, including diseases, treatment outcomes, aging, and cognition, among others. Moreover, we argue that these predictions also offer strategies for useful interventions. Topology-based predictions play this role regardless of whether they do or can receive a mechanistic interpretation. We conclude by making a case for philosophers to focus on prediction in neuroscience in addition to explanation alone. (shrink)

Predictive processing is an increasingly popular explanatory framework developed within cognitive neuroscience. It conceives of the brain as a prediction machine that tries to minimise prediction error. Predictive processing has also been employed to explain aspects of conscious experience. In this paper, I critically evaluate current predictive processing approaches to the phenomenology of time-consciousness from a Husserlian perspective. To do so, I introduce the notion of orthodox predictive processing to refer to interpretations of the predictive processing framework that (...) subscribe to representational views of cognition. As it turns out, current predictive processing accounts of time-consciousness are orthodox given their commitment to representational views of both brain functioning and perception, and, on the other hand, their reliance on the primacy of imagination over perception. However, I argue that such accounts are in fact closer to a Kantian-Brentanian approach to the phenomenology of time-consciousness than to the Husserlian account that they attempt to account for. (shrink)

Predictive approaches to the mind claim that perception, cognition, and action can be understood in terms of a single framework: a hierarchy of Bayesian models employing the computational strategy of predictive coding. Proponents of this view disagree, however, over the extent to which perception is direct on the predictive approach. I argue that we can resolve these disagreements by identifying three distinct notions of perceptual directness: psychological, metaphysical, and epistemological. I propose that perception is plausibly construed as psychologically indirect on (...) the predictive approach, in the sense of being constructivist or inferential. It would be wrong to conclude from this, however, that perception is therefore indirect in a metaphysical or epistemological sense on the predictive approach. In the metaphysical case, claims about the inferential properties of constructivist perceptual mechanisms are consistent with both direct and indirect solutions to the metaphysical problem of perception (e.g. naïve realism, representationalism, sense datum theory). In the epistemological case, claims about the inferential properties of constructivist perceptual mechanisms are consistent with both direct and indirect approaches to the justification of perceptual belief. In this paper, I demonstrate how proponents of the predictive approach have conflated these distinct notions of perceptual directness and indirectness, and I propose alternative strategies for developing the philosophical consequences of the approach. (shrink)

This chapter explores to what extent some core ideas of predictive processing can be applied to the phenomenology of time consciousness. The focus is on the experienced continuity of consciously perceived, temporally extended phenomena (such as enduring processes and successions of events). The main claim is that the hierarchy of representations posited by hierarchical predictive processing models can contribute to a deepened understanding of the continuity of consciousness. Computationally, such models show that sequences of events can be represented as states (...) of a hierarchy of dynamical systems. Phenomenologically, they suggest a more fine-grained analysis of the perceptual contents of the specious present, in terms of a hierarchy of temporal wholes. Visual perception of static scenes not only contains perceived objects and regions but also spatial gist; similarly, auditory perception of temporal sequences, such as melodies, involves not only perceiving individual notes but also slightly more abstract features (temporal gist), which have longer temporal durations (e.g., emotional character or rhythm). Further investigations into these elusive contents of conscious perception may be facilitated by findings regarding its neural underpinnings. Predictive processing models suggest that sensorimotor areas may influence these contents. (shrink)

Testimony about the future dangerousness of a person has become a central staple of many judicial processes. In settings such as bail, sentencing, and parole decisions, in rulings about the civil confinement of the mentally ill, and in custody decisions in a context of domestic violence, the assessment of a person’s propensity towards physical or sexual violence is regarded as a deciding factor. These assessments can be based on two forms of expert testimony: actuarial or clinical. The purpose of this (...) paper is to examine the scientific and epistemological basis of both methods of prediction or risk assessment. My analysis will reveal that this kind of expert testimony is scientifically baseless. The problems I will discuss will generate a dilemma for factfinders: on the one hand, given the weak predictive abilities of the branches of science involved, they should not admit expert clinical or actuarial testimony as evidence; on the other hand, there is a very strong tradition and a vast jurisprudence that supports the continued use of this kind of expert testimony. It is a clear case of the not so uncommon conflict between science and legal tradition. (shrink)

Two studies examined the allegiance bias – the rendering of biased predictions by individuals who are psychologically invested in a desired outcome. In Study 1, fans of either Notre Dame or University of Miami college football read information about an upcoming game between the two teams and then explained a hypothetical victory either by Notre Dame or Miami. Although explaining a hypothetical victory biased the judgments of controls (i.e., fans of neither team) in the direction of the team explained, the (...) judgments of Notre Dame and Miami fans favored their team in every explanation condition. In addition, fans exhibited biased recall for facts favoring their own teams and this biased recall predicted fans’ judgments regarding the upcoming game. Study 2 attempted to specify a debiasing technique that might attenuate the allegiance bias. Indiana University basketball fans described what they thought might happen in an upcoming game between Indiana and the University of Michigan while anticipating having a discussion about the game (i.e., predecisional accountability) with either an Indiana fan, a Michigan fan, or a fan of unknown allegiance. As predicted, anticipating a discussion with a fan of unknown allegiance engendered game predictions that were the least biased in favor of an Indiana victory. Implications for social explanation and prediction research are discussed. (shrink)

Several authors have claimed that prediction is essentially impossible in the general theory of relativity, the case being particularly strong, it is said, when one fully considers the epistemic predicament of the observer. Each of these claims rests on the support of an underdetermination argument and a particular interpretation of the concept of prediction. I argue that these underdetermination arguments fail and depend on an implausible explication of prediction in the theory. The technical results adduced in these (...) arguments can be related to certain epistemic issues, but can only be misleadingly or mistakenly characterized as related to prediction. (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)

_ Source: _Volume 5, Issue 2, pp 228 - 250 Despite inroads made by critical realism against the ‘scientific method’ in social science, the latter remains strong in subject-areas like human resource management. One argument for the alleged superiority of the scientific method lies in the taken-for-granted belief that it alone can formulate empirically testable predictions. Many of those who employ the scientific method are, however, confused about the way they understand and practice prediction. This paper takes as a (...) case study empirical research on the alleged empirical association between human resource management practices and organisational performance. By unpacking the confusion surrounding the two basic notions of prediction used, it reveals what is wrong with them, why the scientific method cannot actually make accurate predictions and why, therefore, the scientific method fails to meet its own criteria for scientificity. Finally, explanation is considered in order to prevent any confusion between it and prediction and to offer what we call _tendential prediction_. (shrink)

The framework of Solomonoff prediction assigns prior probability to hypotheses inversely proportional to their Kolmogorov complexity. There are two well-known problems. First, the Solomonoff prior is relative to a choice of Universal Turing machine. Second, the Solomonoff prior is not computable. However, there are responses to both problems. Different Solomonoff priors converge with more and more data. Further, there are computable approximations to the Solomonoff prior. I argue that there is a tension between these two responses. This is because (...) computable approximations to Solomonoff prediction do not always converge. (shrink)

Continued urbanization, along with anthropogenic global warming, has and will increase land surface temperature and air temperature anomalies in urban areas when compared to their rural surroundings, leading to Urban Heat Islands (UHI). UHI poses environmental and health risks, affecting both psychological and physiological aspects of human health. Thus, using a deep learning approach that considers morphological variables, this study predicts UHI intensity in 69 European cities from 2007 to 2021 and projects UHI impacts for 2050 and 2080. The research (...) employs Artificial Neural Networks, Deep Neural Networks, and Gated Recurrent Units, combining high-resolution 3D urban models with environmental data to analyze UHI trends. The results indicate strong associations between urban form, weather patterns, and UHI intensity, highlighting the need for customized urban planning and policy measures to reduce UHI impacts and foster sustainable urban settings. This research enhances understanding of UHI dynamics and serves as a valuable tool for urban planners and policymakers to address the challenges of climate change, urbanization, and air pollution, ultimately aiding in the improvement of health outcomes and building energy consumption. Moreover, the methodology effectively demonstrates the ability of the GRU to link its scores with UHI projections, offering crucial insights into potential health impacts. (shrink)

In this study, we predict the different levels of performance in a Nintendo Entertainment System (NES) Tetris session based on the score and the number of matches played by the players. Using the first 45 seconds of gameplay, a Random Forest Classifier was trained on the five keys used in the game obtaining a ROC_AUC score of 0.80. Further analysis revealed that the number of down keys (forced drop) and the number of left keys (left translation) are the most relevant (...) keys in this task, showing that by merely including the data from these two keys our Random Forest Classifier reached a ROC_AUC score of 0.83.We conclude that the keylogger data during the early phases of a game session can be successfully used to predict performance in longer sessions of Tetris. (shrink)

Abstract Heart Attack is the Cardiovascular Disease (CVD) which causes the most deaths among CVDs. We collected a dataset from Kaggle website. In this paper, we propose an ANN model for the predicting whether a patient has a heart attack or not that. The dataset set consists of 9 features with 1000 samples. We split the dataset into training, validation, and testing. After training and validating the proposed model, we tested it with testing dataset. The proposed model reached an accuracy (...) of 98.01% on the Heart Disease Dataset. (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 paper I return to Hubert Dreyfus’ old but influential critique of artificial intelligence, redirecting it towards contemporary predictive processing models of the mind (PP). I focus on Dreyfus’ arguments about the “frame problem” for artificial cognitive systems, and his contrasting account of embodied human skills and expertise. The frame problem presents as a prima facie problem for practical work in AI and robotics, but also for computational views of the mind in general, including for PP. Indeed, some of (...) the issues it presents seem more acute for PP, insofar as it seeks to unify all cognition and intelligence, and aims to do so without admitting any cognitive processes or mechanisms outside of the scope of the theory. I contend, however, that there is an unresolved problem for PP concerning whether it can both explain all cognition and intelligent behavior as minimizing prediction error with just the core formal elements of the PP toolbox, and also adequately comprehend (or explain away) some of the apparent cognitive differences between biological and prediction-based artificial intelligence, notably in regard to establishing relevance and flexible context-switching, precisely the features of interest to Dreyfus’ work on embodied indexicality, habits/skills, and abductive inference. I address several influential philosophical versions of PP, including the work of Jakob Hohwy and Andy Clark, as well as more enactive-oriented interpretations of active inference coming from a broadly Fristonian perspective. (shrink)

Selectionist evolutionary theory has often been faulted for not making novel predictions that are surprising, risky, and correct. I argue that it in fact exhibits the theoretical virtue of predictive capacity in addition to two other virtues: explanatory unification and model fitting. Two case studies show the predictive capacity of selectionist evolutionary theory: parallel evolutionary change in E. coli, and the origin of eukaryotic cells through endosymbiosis.

Pessimists predict that future scientific theories will replace present scientific theories. However, they do not specify when the predicted events will take place, so we do not have the chance to blame them for having made a false prediction, although we might have the chance to praise them for having made a true prediction. Their predictions contrast with astronomers’ predictions. Astronomers specify when the next solar eclipse will happen, so we have both the chance to blame them for (...) having made a false prediction and the chance to praise them for having made a true prediction. The pessimists’ prediction remains unvirtuous until they specify when scientific revolutions will occur. This critical point applies no less to the selectivist’s prediction. (shrink)