The predictive coding (PC) theory of attention identifies attention with the optimization of the precision weighting of prediction error. Here we provide some challenges for this identification. On the one hand, the precision weighting of prediction error is too broad a phenomenon to be identified with attention because such weighting plays a central role in multimodal integration. Cases of crossmodal illusions such as the rubber hand illusion and the McGurk effect involve the differential precision weighting of prediction error, yet (...) attention does not shift as one would predict. On the other hand, the precision weighting of prediction error is too narrow a phenomenon to be identified with attention, because it cannot accommodate the full range of attentional phenomena. We review criticisms that PC cannot account for volitional attention and affect-biased attention, and we propose that it may not be able to account for feature-based and intellectual attention. (shrink)

Application of the reliability of repairable systems on solving problems from constructing production systems takes an important place in the process of finding the optimal solution among the suggested system choices. The basic hypothesis when using the reliability of the repairable systems is that every machine is representing a component, a fact that is debatable when talking about technical sciences. However, considering the second assumption of the stationary process, the function of the availability is introduced. It represents the measure between (...) the reliability and the benefit of maintaining the components and the system as a whole one. An additional and the last reason for analyzing constructing machine as a whole one has an economic reason. From the aspect of the total cost and the capacity of the system, the function of the availability is catching more and more impact in the process of determinating the total cost in bill of quantities as the most important part of tender documents. Until former works, techno-economic optimisation usually ran over when choosing the system of the construction machines. In the work, there are shown the methodologies for gathering and analyzing the indicators of availabilities per unit of a company's machine park. The time of demission and reparation have been investigated among 34 components in the period of 6 years. Based on the obtained result for different conditions of machine exploitation and facilities, it is suggested the methodology of the future behaviour of the corresponding within the model optimization availability of constructing production system with the aim of eliminating extra criterion. The suggested model takes in consideration the total cost of the components when they are functional, and when they are not. This kind of approach enables the one who take choices decision of the height of profits, in the lenght of work in function of the projection of cash flow and managing with real indicators of functioning the system at the project level. Given the fact that the maintanance policy of machines is based on availability, and not on reliability of components, with the aim of maximizing the availability of different budget scenarios, a logical approach to the selection of the objective function in the terms of management of construction production systems is to minimize costs for a given availability of system constructing machinery is based on the use of historical data base with the values of the operational availability of components, conditions in which they function, in the process of forecasting the project availability for projects that follow. Ranking variant solutions is the result of the adopted function of the aim as a consequence due to enlargement methods of availability for serial parallel systems with the associated restrictions. For optimal system availability is taken a value that belongs to minimal total cost. However, it is predicted that after the end of the financial year, the real and projected indicators of system functions get compared to each other. Informational support to the proposed model is informational company system, which was built in the PHP programming language on the Linux software platform, and with data based in My SQL. The suggested model is tested on the system for the production and embedding bitumen bound materials on the specific project. Obtained results confirm the assumptions used in work. (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)

Clark acknowledges but resists the indirect mind–world relation inherent in prediction error minimization (PEM). But directness should also be resisted. This creates a puzzle, which calls for reconceptualization of the relation. We suggest that a causal conception captures both aspects. With this conception, aspects of situated cognition, social interaction and culture can be understood as emerging through precision optimization.

Importance: Patients with remitted major depressive disorder (MDD) were previously found to display abnormal functional magnetic resonance imaging connectivity (fMRI) between the right superior anterior temporal lobe (RSATL) and the subgenual cingulate cortex and adjacent septal region (SCSR) when experiencing self-blaming emotions relative to emotions related to blaming others (eg, "indignation or anger toward others"). This finding provided the first neural signature of biases toward overgeneralized self-blaming emotions (eg, "feeling guilty for everything"), known to have a key role in cognitive (...) vulnerability to MDD. It is unknown whether this neural signature predicts risk of recurrence, a crucial step in establishing its potential as a prognostic biomarker, which is urgently needed for stratification into pathophysiologically more homogeneous subgroups and for novel treatments. -/- Objective: To use fMRI in remitted MDD at baseline to test the hypothesis that RSATL-SCSR connectivity for self-blaming relative to other-blaming emotions predicts subsequent recurrence of depressive episodes. -/- Design, setting, and participants: A prospective cohort study from June 16, 2011, to October 10, 2014, in a clinical research facility completed by 75 psychotropic medication-free patients with remitted MDD and no relevant comorbidity. In total, 31 remained in stable remission, and 25 developed a recurring episode over the 14 months of clinical follow-up and were included in the primary analysis. Thirty-nine control participants with no personal or family history of MDD were recruited for further comparison. -/- Main outcomes and measures: Between-group difference (recurring vs stable MDD) in RSATL connectivity, with an a priori SCSR region of interest for self-blaming vs other-blaming emotions. -/- Results: We corroborated our hypothesis that during the experience of self-blaming vs other-blaming emotions, RSATL-SCSR connectivity predicted risk of subsequent recurrence. The recurring MDD group showed higher connectivity than the stable MDD group (familywise error-corrected P < .05 over the a priori SCSR region of interest) and the control group. In addition, the recurring MDD group also exhibited RSATL hyperconnectivity with the right ventral putamen and claustrum and the temporoparietal junction. Together, these regions predicted recurrence with 75% accuracy. -/- Conclusions and relevance: To our knowledge, this study is the first to provide a robust demonstration of an fMRI signature of recurrence risk in remitted MDD. Additional studies are needed for its further optimization and validation as a prognostic biomarker. (shrink)

There is converging evidence that high doses of hallucinogenic drugs can produce significant alterations of self-experience, described as the dissolution of the sense of self and the loss of boundaries between self and world. This article discusses the relevance of this phenomenon, known as “drug-induced ego dissolution (DIED)”, for cognitive neuroscience, psychology and philosophy of mind. Data from self-report questionnaires suggest that three neuropharmacological classes of drugs can induce ego dissolution: classical psychedelics, dissociative anesthetics and agonists of the kappa opioid (...) receptor (KOR). While these substances act on different neurotransmitter receptors, they all produce strong subjective effects that can be compared to the symptoms of acute psychosis, including ego dissolution. It has been suggested that neuroimaging of DIED can indirectly shed light on the neural correlates of the self. While this line of inquiry is promising, its results must be interpreted with caution. First, neural correlates of ego dissolution might reveal the necessary neurophysiological conditions for the maintenance of the sense of self, but it is more doubtful that this method can reveal its minimally sufficient conditions. Second, it is necessary to define the relevant notion of self at play in the phenomenon of DIED. This article suggests that DIED consists in the disruption of subpersonal processes underlying the “minimal” or “embodied” self, i.e., the basic experience of being a self rooted in multimodal integration of self-related stimuli. This hypothesis is consistent with Bayesian models of phenomenal selfhood, according to which the subjective structure of conscious experience ultimately results from the optimization of predictions in perception and action. Finally, it is argued that DIED is also of particular interest for philosophy of mind. On the one hand, it challenges theories according to which consciousness always involves self-awareness. On the other hand, it suggests that ordinary conscious experience might involve a minimal kind of self-awareness rooted in multisensory processing, which is what appears to fade away during DIED. (shrink)

A generalized information theory is proposed as a natural extension of Shannon's information theory. It proposes that information comes from forecasts. The more precise and the more unexpected a forecast is, the more information it conveys. If subjective forecast always conforms with objective facts then the generalized information measure will be equivalent to Shannon's information measure. The generalized communication model is consistent with K. R. Popper's model of knowledge evolution. The mathematical foundations of the new information theory, the generalized communication (...) model , information measures for semantic information and sensory information, and the coding meanings of generalized entropy and generalized mutual information are introduced. Assessments and optimizations of pattern recognition, predictions, and detection with the generalized information criterion are discussed. For economization of communication, a revised version of rate-distortion theory: rate-of-keeping-precision theory, which is a theory for datum compression and also a theory for matching an objective channels with the subjective understanding of information receivers, is proposed. Applications include stock market forecasting and video image presentation. (shrink)

Biological order provided by α-helical secondary protein structures is an important resource exploitable by living organisms for increasing the efficiency of energy transport. In particular, self-trapping of amide I energy quanta by the induced phonon deformation of the hydrogen-bonded lattice of peptide groups is capable of generating either pinned or moving solitary waves following the Davydov quasiparticle/soliton model. The effect of applied in-phase Gaussian pulses of amide I energy, however, was found to be strongly dependent on the site of application. (...) Moving solitons were only launched when the amide I energy was applied at one of the α-helix ends, whereas pinned solitons were produced in the α-helix interior. In this paper, we describe a general mechanism that launches moving solitons in the interior of the α-helix through phase-modulated Gaussian pulses of amide I energy. We also compare the predicted soliton velocity based on effective soliton mass and the observed soliton velocity in computer simulations for different parameter values of the isotropy of the exciton-phonon interaction. The presented results demonstrate the capacity for explicit control of soliton velocity in protein α-helices, and further support the plausibility of gradual optimization of quantum dynamics for achieving specialized protein functions through natural selection. (shrink)

This paper examines a looming reproducibility crisis in the core of the hard sciences. Namely, it concentrates on molecular modeling and simulation (MMS), a family of methods that predict properties of substances through computing interactions on a molecular level and that is widely popular in physics, chemistry, materials science, and engineering. The paper argues that in order to make quantitative predictions, sophisticated models are needed which have to be evaluated with complex simulation procedures that amalgamate theoretical, technological, and social (...) factors – leading to problems with reproducibility. Thus, for methodological reasons, the predictive success causes a reproducibility problem. (shrink)

Development of the world economy bears numerous negative phenomena, and require constant need to rebalance socioeconomic interests of nations, transnational subjects, and individuals. Socialisation is an important and effective tool for balancing social and individual; however, despite socialisation is evolving rapidly, its scientific and practical potential is not duly uncovered. In the article theoretical and methodological foundations of socialisation of economy is surveyed in the context of globalisation, and etymology, explanations, scope, historical phases of development, theoretical aspects and practical forms (...) of use, consequences and prospects are analysed. The term «socialisation» was determined as a multidisciplinary, used in many scientific fields, increasingly involving various areas of research and is understood as inclusion, adaptation and development of human being in society. It was determined that the economy socialisation is implemented in different fields and semantic structures, contains a large number of methodological tools, is involved at all management levels, and is primarily identified with the increasing role of social component in the life of human resources. The assumptions were made about the future transformation of this category in line with the identified predictive trends. (shrink)

The relevance of the problem under study is stipulated by the need to stabilize the market for tourist services in existing restrictions caused by the SARS-CoV-2 pandemic. The purpose of the research: The purpose of the article is to develop integrated measures for the formation of transport and logistics clusters to increase the synergetic effect of the proposed activities aimed at raising the mobility level of potential consumers of tourist services. Methods of the research: The main research methods include predicting (...) and simulating; they make it possible to consider the problem of forming the package of tourist and transport services as a process of targeted monitoring and optimization of tourist routs. Results of the research: The article presents the concept of creation of the network of transport and tourist clusters in Poland and its functioning; large rail carriers are the core of the clusters. Practical significance: The proposed models for the formation of a package of tourist services are aimed at increasing the synergetic effect for all cluster participants, as well as at the development of tourist activities during the pandemic. (shrink)

The article examines the process of formation inventory of the enterprise and determines the optimal volume of commodity resources for sale. A generalization of author’s approaches to the formation and evaluation of inventories of the enterprise is carried out. The marketing-logistic approach was applied for the purpose of distribution groups of commodity resources due to the risk of non-fulfillment the order for the supply of goods of the enterprise. In order to ensure an effective process of commodity provision of the (...) enterprises, the costs associated with the formation of inventories are determined. The formalized scheme of the formation commodity provision and the process of optimization inventory of the enterprise is offered. The analysis the structure of the company’s inventory is carried out, the volume of goods turnover is defined, the stocks are grouped for various clustering characteristics. To conduct the study, statistical information was used on commodity resources of the enterprises, statistical methods (grouping, structure analysis, estimation of dynamic series), tools for assessing the efficiency of inventory use, HML-FMR clustering were used. The necessity of using XYZ and ABC analysis is indicated in order to obtain more reliable results and forecast values of the product support of the enterprise. Economic-mathematical modeling is applied and graphically shown the difference in the formation of commodity resources by various features of HML-FMR clustering. The calculations allow the enterprise to determine the optimal amount of commodity resources in accordance with the needs of consumers and their solvent demand, to plan financial resources for the formation of inventories, to develop assortment policy in accordance with demand for products and their implementation. The results of calculations the volume of merchandising of the enterprise, taking into account the HML-FMR clustering affect the formation of final financial performance of the enterprise — income and profits. (shrink)

During the last two decades the role of quantitative genetics in evolutionary theory has expanded considerably. Quantitative genetic-based models addressing long term phenotypic evolution, evolution in multiple environments (phenotypic plasticity) and evolution of ontogenies (developmental trajectories) have been proposed. Yet, the mathematical foundations of quantitative genetics were laid with a very different set of problems in mind (mostly the prediction of short term responses to artificial selection), and at a time in which any details of the genetic machinery were virtually (...) unknown. In this paper we discuss what a model is in population biology, and what kind of model we need in order to address the complexities of phenotypic evolution. We review the assumptions of quantitative genetics and its most recent accomplishments, together with the limitations that such assumptions impose on the modelling of some aspects of phenotypic evolution. We also discuss three alternative appr oaches to the theoretical description of evolutionary trajectories (nonlinear dynamics, complexity theory and optimization theory), and their respective advantages and limitations. We conclude by calling for a new theoretical synthesis, including quantitative genetics and not necessarily limited to the other approaches here discussed. (shrink)

This article is a comparative study between predictive processing (PP, or predictive coding) and cognitive dissonance (CD) theory. The theory of CD, one of the most influential and extensively studied theories in social psychology, is shown to be highly compatible with recent developments in PP. This is particularly evident in the notion that both theories deal with strategies to reduce perceived error signals. However, reasons exist to update the theory of CD to one of “predictive dissonance.” First, the hierarchical PP (...) framework can be helpful in understanding varying nested levels of CD. If dissonance arises from a cascade of downstream and lateral predictions and consequent prediction errors, dissonance can exist at a multitude of scales, all the way up from sensory perception to higher order cognitions. This helps understand the previously problematic dichotomy between “dissonant cognitive relations” and “dissonant psychological states,” which are part of the same perception-action process while still hierarchically distinct. Second, since PP is action-oriented, it can be read to support recent action-based models of CD. Third, PP can potentially help us understand the recently speculated evolutionary origins of CD. Here, the argument is that responses to CD can instill meta-learning which serves to prevent the overfitting of generative models to ephemeral local conditions. This can increase action-oriented ecological rationality and enhanced capabilities to interact with a rich landscape of affordances. The downside is that in today’s world where social institutions such as science a priori separate noise from signal, some reactions to predictive dissonance might propagate ecologically unsound (underfitted, confirmation-biased) mental models such as climate denialism. (shrink)

The spatial-ecological modeling is relating sustainable dispersions with social development. Sustainability with spatial-ecological model gives attention to urban environments in the design review management to comply with Earth’s system. Naturally exchanged patterns of ecosystems have consistent and periodic cycles to preserve energy flows and materials in Earth’s system. The Probabilistic Risk Assessment (PRA) technique is utilized to assess the safety of an industrial complex. The other analytical approach is the Failure-Safe Mode and Effect Analysis (FMEA) for critical components. The plant (...) safety parameters are identified for engineering topology as employed in assessment safety of industrial ecology. In particular, the most severe accidental release of hazardous gaseous is postulated, analyzed and assessment in industrial region. The IAEA-safety assessment procedure is used to account the duration and rate of discharge of liquid chlorine. The ecological model of plume dispersion width and concentration of chlorine gas in the downwind direction is determined using Gaussian Plume Model in urban and rural areas and presented with SURFER®. The prediction of accident consequences is traced in risk contour concentration lines. The local greenhouse effect is predicted with relevant conclusions. The spatial-ecological model is predicted for multiple factors distribution schemes of multi-criteria analysis. The input–output analysis is explored from the spillover effect, and we conducted Monte Carlo simulations for sensitivity analysis. Their unique structure is balanced within “equilibrium patterns”, such as the composite index for biosphere with collective structure of many distributed feedback flows. These dynamic structures are related to have their physical and chemical properties and enable a gradual and prolonged incremental pattern. While this spatial model structure argues from ecology, resource savings, static load design, financial and other pragmatic reasons, the outcomes are not decisive in an artistic/architectural perspective. The hypothesis is deployed to unify analytic and analogical spatial structure in development urban environments using optimization loads as an example of integrated industrial structure where the process is based on engineering topology of systems ecology. (shrink)

Predictive processing (PP) has been repeatedly presented as a unificatory account of perception, action, and cognition. In this paper, we argue that this is premature: As a unifying theory, PP fails to deliver general, simple, homogeneous, and systematic explanations. By examining its current trajectory of development, we conclude that PP remains only loosely connected both to its computational framework and to its hypothetical biological underpinnings, which makes its fundamentals unclear. Instead of offering explanations that refer to the same set of (...) principles, we observe systematic equivocations in PP‐based models, or outright contradictions with its avowed principles. To make matters worse, PP‐based models are seldom empirically validated, and they are frequently offered as mere just‐so stories. The large number of PP‐based models is thus not evidence of theoretical progress in unifying perception, action, and cognition. On the contrary, we maintain that the gap between theory and its biological and computational bases contributes to the arrested development of PP as a unificatory theory. Thus, we urge the defenders of PP to focus on its critical problems instead of offering mere re‐descriptions of known phenomena, and to validate their models against possible alternative explanations that stem from different theoretical assumptions. Otherwise, PP will ultimately fail as a unified theory of cognition. (shrink)

Predictive syntactic processing plays an essential role in language comprehension. In our previous study using Japanese object-verb (OV) sentences, we showed that the left inferior frontal gyrus (IFG) responses to a verb increased at 120–140 ms after the verb onset, indicating predictive effects caused by a preceding object. To further elucidate the automaticity of the predictive effects in the present magnetoencephalography study, we examined whether a subliminally presented verb (“subliminal verb”) enhanced the predictive effects on the sentence-final verb (“target verb”) (...) unconsciously, i.e., without awareness. By presenting a subliminal verb after the object, enhanced predictive effects on the target verb would be detected in the OV sentences when the transitivity of the target verb matched with that of the subliminal verb (“congruent condition”), because the subliminal verb just after the object could determine the grammaticality of the sentence. For the OV sentences under the congruent condition, we observed significantly increased left IFG responses at 140–160 ms after the target verb onset. In contrast, responses in the precuneus and midcingulate cortex (MCC) were significantly reduced for the OV sentences under the congruent condition at 110–140 and 280–300 ms, respectively. By using partial Granger causality analyses for the OV sentences under the congruent condition, we revealed a bidirectional interaction between the left IFG and MCC at 60–160 ms, as well as a significant influence from the MCC to the precuneus. These results indicate that a top-down influence from the left IFG to the MCC, and then to the precuneus, is critical in syntactic decisions, whereas the MCC shares its task-set information with the left IFG to achieve automatic and predictive processes of syntax. (shrink)

In light of the recent emergence of predictive techniques in law enforcement to forecast crimes before they occur, this paper examines the temporal operation of power exercised by predictive policing algorithms. I argue that predictive policing exercises power through a paranoid style that constitutes a form of temporal governmentality. Temporality is especially pertinent to understanding what is ethically at stake in predictive policing as it is continuous with a historical racialized practice of organizing, managing, controlling, and stealing time. After first (...) clarifying the concept of temporal governmentality, I apply this lens to Chicago Police Department’s Strategic Subject List. This predictive algorithm operates, I argue, through a paranoid logic that aims to preempt future possibilities of crime on the basis of a criminal past codified in historical crime data. (shrink)

If the laws of nature are deterministic, then it seems possible that a Laplacean intelligence that knows the initial conditions and the laws would be able to accurately predict everything that will ever happen. However, it would be easy to construct a counterpredictive device that falsifies any revealed prediction about its future behavior. What would then occur if a Laplacean intelligence encountered a counterpredictive device? This is the paradox of predictability. A number of philosophers have proposed solutions to it, though (...) part of my aim here is to argue that the paradox is more pernicious than has thus far been appreciated, and therefore that extant solutions are inadequate. My broader aim is to argue that the paradox motivates Humeanism about laws of nature. (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)

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)

In this chapter we first elucidate the subjective flow of time particularly as developed by Husserl. We next discuss time and timescales in predictive processing. We then consider how the phenomenological analysis of time can be naturalized within a predictive processing framework. In the final section, we develop an analysis of the temporal disturbances characteristic of depression using the resources of both phenomenology and predictive processing.

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)

I distinguish two roles for a fitness concept in the context of explaining cumulative adaptive evolution: fitness as a predictor of gene frequency change, and fitness as a criterion for phenotypic improvement. Critics of inclusive fitness argue, correctly, that it is not an ideal fitness concept for the purpose of predicting gene-frequency change, since it relies on assumptions about the causal structure of social interaction that are unlikely to be exactly true in real populations, and that hold as approximations only (...) given a specific type of weak selection. However, Hamilton took this type of weak selection, on independent grounds, to be responsible for cumulative assembly of complex adaptations. In this special context, I argue that inclusive fitness is distinctively valuable as a criterion for improvement and a standard for optimality. Yet to call inclusive fitness a criterion for improvement and a standard for optimality is not to make any claim about the frequency with which inclusive fitness optimization actually occurs in nature. This is an empirical question that cannot be settled by theory alone. I close with some reflections on the place of inclusive fitness in the long running clash between ‘causalist’ and ‘statisticalist’ conceptions of fitness. (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)

This study examined the degree to which students indulge in six prominent misconducts in Distance Education Institutions (DEIs). The study also quantified how class size, instructional delivery and institutional policies predict students’ indulgence in sharp practices using a general linear modelling approach. A sample of 871 participants was drawn from 1,742 final-year students across two DEIs in Nigeria. A structured questionnaire was used for data collection. The questionnaire had acceptable psychometric estimates of dimensionality, content and construct validity, as well as (...) reliability. Sharp practices such as cheating, plagiarism, falsification, impersonation, and arm-twisting were more prevalent in large classes, whereas only inducement was higher in small classes. Class size influenced students’ indulgence in sharp practices in DEIs. Instructional delivery and institutional policies negatively predicted students’ indulgence in the six sharp practices. Almost all the six sharp practices correlated positively and significantly, except for impersonation and inducement. Therefore, distance education students who cheat, plagiarize, arm-twist, falsify records, induce lecturers, and promote impersonation are more likely to indulge in other forms of sharp practices. It was concluded that large class sizes, poor instructional delivery, and poorly implemented institutional policies promote sharp practices among distance education students. Based on this conclusion, key policy and research implications are discussed. (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)

There is considerable disagreement about the epistemic value of novel predictive success, i.e. when a scientist predicts an unexpected phenomenon, experiments are conducted, and the prediction proves to be accurate. We survey the field on this question, noting both fully articulated views such as weak and strong predictivism, and more nascent views, such as pluralist reasons for the instrumental value of prediction. By examining the various reasons offered for the value of prediction across a range of inferential contexts , we (...) can see that neither weak nor strong predictivism captures all of the reasons for valuing prediction available. A third path is presented, Pluralist Instrumental Predictivism; PIP for short. (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)

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)

Building information modeling can help in predicting the energy efficiency in future based on dynamic patterns obtained by visualization of data. The aim of this study was to investigate the effective parameters of energy consumption using BIM technology which can evaluate the buildings energy performance. First, three forms of general states in the building were modeled to evaluate the proposed designs in Autodesk Revit Software. Then, the main building form for energy modeling and analysis was selected. Autodesk Revit 2020 software (...) was also used to obtain the results of climate data analysis and building energy consumption index. Finally, the most optimal mode was selected by examining different energy consumption modes. The results showed that the use of building information modeling technology in adjusting the parameters affecting energy consumption can save energy cost up to 58.23% in block D. Energy cost savings for block C and the western lobby were obtained as 51.03% and 43.05%, respectively. Based on energy use intensity, energy cost savings for blocks C, D, and the western lobby were estimated as 16.67%, 16.30%, and 11%, respectively. The results of parametric studies on alternative schemes of energy use intensity optimization showed that 16.30% savings could be achieved by the base building model in a 30-year time horizon. Therefore, it was concluded that optimization of energy consumption would reduce the environmental pollutants emission and contribute to preservation and sustainability of the environment. (shrink)

t Intentional sampling methods are non-randomized procedures that select a group of individuals for a sample with the purpose of meeting specific prescribed criteria. In this paper we extend previous works related to intentional sampling, and address the problem of sequential allocation for clinical trials with few patients. Roughly speaking, patients are enrolled sequentially, according to the order in which they start the treatment at the clinic or hospital. The allocation problem consists in assigning each new patient to one, and (...) only one, of the alternative treatment arms. The main requisite is that the profiles in the alternative arms remain similar with respect to some relevant patients’ attributes (age, gender, disease, symptom severity and others). We perform numerical experiments based on a real case study and discuss how to conveniently set up perturbation parameters, in order to yield a suitable balance between optimality – the similarity among the relative frequencies of patients in the several categories for both arms, and decoupling – the absence of a tendency to allocate each pair of patients consistently to the same arm. (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)

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)

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)

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)

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)

Both mindreading and stereotyping are forms of social cognition that play a pervasive role in our everyday lives, yet too little attention has been paid to the question of how these two processes are related. This paper offers a theory of the influence of stereotyping on mental-state attribution that draws on hierarchical predictive coding accounts of action prediction. It is argued that the key to understanding the relation between stereotyping and mindreading lies in the fact that stereotypes centrally involve character-trait (...) attributions, which play a systematic role in the action–prediction hierarchy. On this view, when we apply a stereotype to an individual, we rapidly attribute to her a cluster of generic character traits on the basis of her perceived social group membership. These traits are then used to make inferences about that individual’s likely beliefs and desires, which in turn inform inferences about her behavior. (shrink)

Abstract: Heart attacks, or myocardial infarctions, are a leading cause of mortality worldwide. Early prediction and accurate analysis of potential risk factors play a crucial role in preventing heart attacks and improving patient outcomes. In this study, we conduct a comprehensive review of datasets related to heart attack analysis and prediction. We begin by examining the various types of datasets available for heart attack research, encompassing clinical, demographic, and physiological data. These datasets originate from diverse sources, including hospitals, research institutions, (...) and public health agencies. Our analysis aims to identify common features, data quality, and potential biases in these datasets. Next, we explore the predictive modeling techniques employed in heart attack prediction. Machine learning algorithms, such as decision trees, support vector machines, and deep neural networks, have gained prominence in predicting heart attacks. We discuss the strengths and limitations of these methods and highlight recent advancements in predictive modeling. Furthermore, we delve into the critical risk factors associated with heart attacks. Factors such as age, gender, hypertension, diabetes, and cholesterol levels are examined for their significance in predicting cardiac events. We also investigate the role of lifestyle factors, including smoking, diet, and physical activity, in heart attack risk assessment. Additionally, this review addresses the importance of data preprocessing and feature engineering in improving prediction accuracy. Feature selection methods, missing data handling, and data scaling techniques are discussed to enhance the robustness of heart attack prediction models. In conclusion, this comprehensive dataset review provides valuable insights into the state of heart attack analysis and prediction. It serves as a resource for researchers and healthcare professionals seeking to better understand the datasets available for heart attack research and the methods employed for accurate prediction. Ultimately, our efforts in dataset analysis and predictive modeling contribute to the advancement of preventive cardiology and the reduction of heart attack-related morbidity and mortality. (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)

Breast cancer remains a significant health concern worldwide, necessitating the development of effective diagnostic tools. In this study, we employ a neural network-based approach to analyze the Wisconsin Breast Cancer dataset, sourced from Kaggle, comprising 570 samples and 30 features. Our proposed model features six layers (1 input, 1 hidden, 1 output), and through rigorous training and validation, we achieve a remarkable accuracy rate of 99.57% and an average error of 0.000170 as shown in the image below. Furthermore, our investigation (...) identifies the most influential features in breast cancer diagnosis, shedding light on the key determinants of malignancy. Notably, we find that factors such as fractal dimension_se, symmetry worst, compactness_worst, symmetry_se, and smoothness_se play pivotal roles in distinguishing between benign and malignant cases. This research contributes to the ongoing efforts to enhance breast cancer diagnosis, providing valuable insights into feature importance and showcasing the potential of neural networks in medical applications. Our findings have implications for improving early detection and treatment strategies, ultimately contributing to improved patient outcomes. (shrink)

The relations between rationality and optimization have been widely discussed in the wake of Herbert Simon's work, with the common conclusion that the rationality concept does not imply the optimization principle. The paper is partly concerned with adding evidence for this view, but its main, more challenging objective is to question the converse implication from optimization to rationality, which is accepted even by bounded rationality theorists. We discuss three topics in succession: (1) rationally defensible cyclical choices, (2) (...) the revealed preference theory of optimization, and (3) the infinite regress of optimization. We conclude that (1) and (2) provide evidence only for the weak thesis that rationality does not imply optimization. But (3) is seen to deliver a significant argument for the strong thesis that optimization does not imply rationality. (shrink)

This paper aims to provide a theoretical framework for explaining the subjective character of pain experience in terms of what we will call ‘embodied predictive processing’. The predictive processing (PP) theory is a family of views that take perception, action, emotion and cognition to all work together in the service of prediction error minimisation. In this paper we propose an embodied perspective on the PP theory we call the ‘embodied predictive processing (EPP) theory. The EPP theory proposes to explain pain (...) in terms of processes distributed across the whole body. The prediction error minimising system that generates pain experience comprises the immune system, the endocrine system, and the autonomic system in continuous causal interaction with pathways spread across the whole neural axis. We will argue that these systems function in a coordinated and coherent manner as a single complex adaptive system to maintain homeostasis. This system, which we refer to as the neural-endocrine-immune (NEI) system, maintains homeostasis through the process of prediction error minimisation. We go on to propose a view of the NEI ensemble as a multiscale nesting of Markov blankets that integrates the smallest scale of the cell to the largest scale of the embodied person in pain. We set out to show how the EPP theory can make sense of how pain experience could be neurobiologically constituted. We take it to be a constraint on the adequacy of a scientific explanation of subjectivity of pain experience that it makes it intelligible how pain can simultaneously be a local sensing of the body, and, at the same time, a more global, all-encompassing attitude towards the environment. Our aim in what follows is to show how the EPP theory can meet this constraint. (shrink)

First published in 1949 expressly to introduce logical positivism to English speakers. Reichenbach, with Rudolph Carnap, founded logical positivism, a form of epistemofogy that privileged scientific over metaphysical truths.

ABSTRACT. The relations between rationality and optimization have been widely discussed in the wake of Herbert Simon’s work, with the common conclusion that the rationality concept does not imply the optimization principle. The paper is partly concerned with adding evidence for this view, but its main, more challenging objective is to question the converse implication from optimization to rationality, which is accepted even by bounded rationality theorists. We discuss three topics in succession: (1) rationally defensible cyclical choices, (...) (2) the revealed preference theory of optimization, and (3) the infinite regress of optimization. We conclude that (1) and (2) provide evidence only for the weak thesis that rationality does not imply optimization. But (3) is seen to deliver a significant argument for the strong thesis that optimization does not imply rationality. (shrink)

Consciousness vehicle externalism is the claim that the material machinery of a subject’s phenomenology partially leaks outside a subject’s brain, encompassing bodily and environmental structures. The DEUTS argument is the most prominent argument for CVE in the sensorimotor enactivists’ arsenal. In a recent series of publications, Kirchhoff and Kiverstein have deployed such an argument to claim that a prominent view of neural processing, namely predictive processing, is fully compatible with CVE. Indeed, in Kirchhoff and Kiverstein’s view, a proper understanding of (...) predictive processing mandates CVE. In this essay, we critically examine Kirchhoff and Kiverstein’s argument. Our aim is to argue in favor of the following three points. First, that Kirchhoff and Kiverstein’s emphasis on cultural practices lends no support to CVE: at best, it vindicates some form of content externalism about phenomenal content. Secondly, the criteria Kirchhoff and Kiverstein propose to identify a subject’s phenomenal machinery greatly overgeneralize, leaving them open to a “consciousness bloat” objection, which is an analog of the cognitive bloat objection against the extended mind. Lastly, we will argue that the “consciousness bloat” problem is inbuilt in the very argumentative structure of the DEUTS argument. We will thus conclude that, contrary to the philosophical mainstream, DEUTS is not the best argument for CVE in the sensorimotor enactivists’ argumentative arsenal. (shrink)

The objective of this study was to employ the theory of planned behavior in examining the inclusion of Islamic religiosity in predicting Islamic ethical work behavior. Islamic religiosity was included as Islam plays a dominant role in Brunei’s society. Participants consisted of 370 Malay Muslim teachers. Structural equation modeling was used to test three proposed models. While Model 1 was based on the theory of planned behavior, it does not take into consideration the distinctive Islamic context of the Bruneian society. (...) Hence, Model 2 was proposed to include Islamic religiosity. To better reflect the population, a model with Islamic religiosity was further augmented leading to Model 3. Results showed support for only two models: Model 1 and Model 3. Model 3 was revised by removing non-significant paths. Model comparison indicated that the revised Model 3 (AIC = 100.82; BIC = 103.55) is a better model than Model 1 (AIC = 141.88; BIC = 143.98). This suggests that the predictive power of the theory of planned behavior can be improved by including dominant cultural factors, such as religiosity, that affect a person’s belief. In conclusion, it can be said that religion is indispensable in exploring and understanding how the people in Brunei think and behave in their workplace and society. (shrink)

A preponderance of empirical research in higher education exists on the use of electronic resources to promote university education and learning. This suggests that this area of research has attracted significant interest worldwide. However, there seems to be inadequate information on the association between specific electronic infrastructures, how they are utilized for learning, and their effects on students’ learning effectiveness in higher institutions in Nigeria. This research draws on previous studies and seeks to establish how different electronic resources are used (...) in universities and how they impact students’ learning effectiveness. Using a descriptive survey design, this survey examined 1,452 participants (undergraduates and postgraduates) from Nigerian universities. The data were analyzed using mean and standard deviation, while a Polytomous Logistic Regression Model was used in testing the hypothesis formulated. Findings showed that the degree of the use of digital infrastructures in universities is low. The extent of learning effectiveness among students in universities is also low. The usage of e-infrastructures (such as e-learning, e-communication, and e-library tools) significantly predicted students’ levels of learning effectiveness. Based on these results, conclusions and relevant theoretical and practical implications are discussed for policy reforms in education and e-learning. This study faces a limitation arising from the small number of universities studied. However, it is suggested that future related studies extend their focus beyond the context of Nigeria. (shrink)