A new theory is taking hold in neuroscience. It is the theory that the brain is essentially a hypothesis-testing mechanism, one that attempts to minimise the error of its predictions about the sensory input it receives from the world. It is an attractive theory because powerful theoretical arguments support it, and yet it is at heart stunningly simple. Jakob Hohwy explains and explores this theory from the perspective of cognitive science and philosophy. The key argument throughout The Predictive Mind is (...) that the mechanism explains the rich, deep, and multifaceted character of our conscious perception. It also gives a unified account of how perception is sculpted by attention, and how it depends on action. The mind is revealed as having a fragile and indirect relation to the world. Though we are deeply in tune with the world we are also strangely distanced from it. (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 experimental research paradigm lies at the core of empirical psychology. New data analytical and computational tools continually enrich its methodological arsenal, while the paradigm’s mission remains the testing of theoretical predictions and causal explanations. Predictions regarding experimental results necessarily point to the future. Once the data are collected, the causal inferences refer to a hypothesis now lying in the past. The experimental paradigm is not designed to permit strong inferences about particular incidents that occurred before predictions were made. In (...) contrast, historical research and scholarship in other humanities focus on this backward direction of inference. The disconnect between forward-looking experimental psychology and backward-looking historical (i.e., narrative) psychology is a challenge in the postmodern era, which can be addressed. To illustrate this possibility, I discuss three historical case studies in light of theory and research in contemporary psychology. (shrink)

an observation to formulate a theory, it is no surprise that the resulting theory accurately captures that observation. However, when the theory makes a novel prediction—when it predicts an observation that was not used in its formulation—this seems to provide more substantial confirmation of the theory. This paper presents a new approach to the vexed problem of understanding the epistemic difference between prediction and accommodation. In fact, there are several problems that need to be disentangled; in all of (...) them, the key is the concept of overfitting. We float the hypothesis that accommodation is a defective methodology only when the methods used to accommodate the data fail to guard against the risk of overfitting. We connect our analysis with the proposals that other philosophers have made. We also discuss its bearing on the conflict between instrumentalism and scientific realism. Introduction Predictivisms—a taxonomy Observations Formulating the problem What might Annie be doing wrong? Solutions Observations explained Mayo on severe tests The miracle argument and scientific realism Concluding comments. (shrink)

Ghosn, Mariam Predictive genetic testing Part 2 will examine the issues and ethical aspects that must be considered when adolescents below the age of majority make a request to undergo predictive genetic testing for Huntington's disease.

This chapter seeks to recover an approach to consciousness from a general theory of brain function, namely the prediction error minimization theory. The way this theory applies to mental and developmental disorder demonstrates its relevance to consciousness. The resulting view is discussed in relation to a contemporary theory of consciousness, namely the idea that conscious perception depends on Bayesian metacognition; this theory is also supported by considerations of psychopathology. This Bayesian theory is first disconnected from the higher-order thought theory, (...) and then, via a prediction error conception of action, connected instead to the global neuronal workspace theory. Considerations of mental and developmental disorder therefore show that a very general theory of brain function is relevant to explaining the structure of conscious perception; furthermore, this theory can subsume and unify two contemporary approaches to consciousness, in a move that seeks to elucidate the fundamental mechanism for selection of representational content into consciousness. (shrink)

The prediction of the secondary structure of a protein from its amino acid sequence is an important step towards the prediction of its three-dimensional structure. However, the accuracy of ab initio secondary structure prediction from sequence is about 80 % currently, which is still far from satisfactory. In this study, we proposed a novel method that uses binomial distribution to optimize tetrapeptide structural words and increment of diversity with quadratic discriminant to perform prediction for protein three-state (...) secondary structure. A benchmark dataset including 2,640 proteins with sequence identity of less than 25 % was used to train and test the proposed method. The results indicate that overall accuracy of 87.8 % was achieved in secondary structure prediction by using ten-fold cross-validation. Moreover, the accuracy of predicted secondary structures ranges from 84 to 89 % at the level of residue. These results suggest that the feature selection technique can detect the optimized tetrapeptide structural words which affect the accuracy of predicted secondary structures. (shrink)

Social scientists increasingly use social media data to illuminate long-standing substantive questions in social science research. However, a key challenge of analyzing such data is their lower level of individual detail compared to highly detailed survey data. This limits the scope of substantive questions that can be addressed with these data. In this study, we provide a method to upgrade individual detail in terms of ethnicity in data gathered from social media via the use of register data. Our research aim (...) is twofold: first, we predict the most likely value of ethnicity, given one's first name, and second, we show how one can test hypotheses with the predicted values for ethnicity as an independent variable while simultaneously accounting for the uncertainty in these predictions. We apply our method to social network data collected from Facebook. We illustrate our approach and provide an example of hypothesis testing using our procedure, i.e., estimating the relation between predicted network ethnic homogeneity on Facebook and trust in institutions. In a comparison of our method with two other methods, we find that our method provides the most conservative tests of hypotheses. We discuss the promise of our approach and pinpoint future research directions. (shrink)

We suggest that the cerebellum generates sensory or estimates based on outgoing motor commands and sensory feedback. Thus, it is not a motor pattern generator (HOUK et al.) but a predictive system which is intimately involved in motor behavior. This theory may explain the sensitivity of the climbing fibers to both unexpected external events and motor errors (SIMPSON et al.), and we speculate that unusual biophysical properties of the inferior olive might allow the cerebellum to develop multiple asynchronous sensory estimates, (...) [HOUK et al.; SIMPSON et al.; THACH]. (shrink)

This research examined factors predicting university students' intentions to enroll in a philosophy of life course. One hundred and ninety subjects participated in two surveys. The first was qualitative, identifying factors students considered in forming intentions, but without ranking the factors. The second study used a quantitative model to predict student intentions from their beliefs about the course, themselves, and other people. The model was based on Ajzen's theory of planned behavior, a theory that successfully predicts many different behaviors. Analysis (...) was done using partial least squares, a robust structural equation modeling technique. The results show that students focused primarily on personal outcomes from the course. They wanted the course to help them understand life and themselves. To a lesser extent, students' perceptions of other people's views affected their intentions, especially the opinions of parents, friends, and religious people. Some students thought all beliefs are subjective, and discussing them would be pointless. Recommendations are given for designing and promoting philosophy of life courses. (shrink)

Though predictive processing approaches to the mind were originally applied to exteroceptive perception, i.e., vision and action, recent work has started to explore the role of interoceptive perception, i.e., emotion and affect. This article builds on this work by extending PP beyond emotion to the construction of emotional dispositions. I employ principles from dynamical systems theory and PP to provide a model of how dispositional anger can develop in response to early experiences of psychosocial stress. The model is then deployed (...) to explain the established link between psychosocial stress in early life and the appearance of certain organic disease phenotypes in later life. This phenomenon can appear mysterious when viewed through the standard biomedical explanatory lens, which has difficulty accounting for the causal influence of subjective perceptions and evaluations of the social and material environment on the development of organic disease processes. The model provided presents such cases as instances of developmental mismatch. They occur when an organism develops an emotional disposition that leads them to make habitually-biased appraisals of what the social environment affords. The model provides a novel explanation of certain organic disease phenotypes with top-down and developmental causes, and demystifies one class of cases involving apparently spooky ‘mind-to-matter’ causation. (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)

Advancement in internet of things and proliferation in the use of smart devices have raised concerns about the data privacy of online users. This study predicts the consequences of perceived data privacy risks on consumer behaviours in Lagos State, Nigeria using the integrated Entropy-Technique for Order Preference by Similarity to Ideal Solution. We employed Entropy to assign weights to each criterion. Subsequently, responses were systematically ranked to arrive at an inference using TOPSIS. 84.8% agree that any perceived cyber security threat (...) or a breach in their data privacy would stop them from proceeding with the transaction or activity online, or the use of a digital product. Similarly,, agree it is critical that online businesses only ask for customer information that is relevant to the use of the product or service. Thus, the findings indicate that the privacy paradox of enlightened online consumers tends to diminish when they are faced with perceived data privacy and cybersecurity risks. (shrink)

Open peer commentary on the target article “How and Why the Brain Lays the Foundations for a Conscious Self” by Martin V. Butz. Excerpt: Surprisingly, the paper entirely neglects the issue of the dynamic properties of our environment. Focusing on (static, inanimate) objects only, it fails to acknowledge that anticipation becomes especially relevant when things around us change without being under our control: this is when we are forced to adapt quickly to new circumstances. To estimate as precisely as necessary (...) what will when be where is of vital meaning. Surprisingly though, this issue is not addressed at all. Although it is acknowledged that the environment contains dynamics that are to be predicted somehow (§3) and that have to be represented in the brain somehow (§28), concepts about and empirical data on the prediction of external events are not dealt with further. (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)

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

The 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)

In the methodology of scientific research programs (MSRP) there are important features on the problem of prediction, especially regarding novel facts. In his approach, Imre Lakatos proposed three different levels on prediction: aim, process, and assessment. Chapter 5 pays attention to the characterization of prediction in the methodology of research programs. Thus, it takes into account several features: (1) its pragmatic characterization, (2) the logical perspective as a proposition, (3) the epistemological component, (4) its role in the (...) appraisal of research programs, and (5) its place as a value for scientific research. -/- The notion of “novel facts” is highly relevant in his conception, where several aspects are involved: the directions of novel facts, the different kinds of novelty, and the transition from six possible options of “novel facts” to four choices. Thereafter, the prediction of novel facts as the criterion of appraisal is considered. On the one hand, this requires analyzing the theoretical, empirical, and heuristic possibilities of appraisal; and, on the other, whether there is an overemphasis on the role of prediction in methodology of scientific research programs. As a consequence, there is an analysis of Lakatos’ criterion of appraisal in MSRP and economics. (shrink)

This paper examines the relationship between perceiving and imagining on the basis of predictive processing models in neuroscience. Contrary to the received view in philosophy of mind, which holds that perceiving and imagining are essentially distinct, these models depict perceiving and imagining as deeply unified and overlapping. It is argued that there are two mutually exclusive implications of taking perception and imagination to be fundamentally unified. The view defended is what I dub the ecological–enactive view given that it does not (...) succumb to internalism about the mind-world relation, and allows one to keep a version of the received view in play. (shrink)

Are predictions about how one will freely and intentionally behave in the future ever relevant to how one ought to behave? There is good reason to think they are. As imperfect agents, we have responsibilities of self-management, which seem to require that we take account of the predictable ways we're liable to go wrong. I defend this conclusion against certain objections to the effect that incorporating predictions concerning one's voluntary conduct into one's practical reasoning amounts to evading responsibility for that (...) conduct. There is, however, some truth to this sort of objection. To understand the legitimate role of self-prediction in practical reasoning, we need to distinguish instances of coping responsibly with an anticipated failure to behave as one ought, on the one hand, from mere acquiescence in one's flaws, on the other. I argue that, to draw this distinction, we must recognize certain limits on the use of self-prediction as a ground of choice. (shrink)

The spectre of determinism stalks many of the concerns surrounding the impact of genetic research into both disease and normal behaviour. The ability accurately to predict a person's actions would certainly have profound implications for notions of individuality and free will. But to what extent will the current explosion in genetic research provide more accurate predictors than have been available for millennia in the form of wealth, social status and perceived family resemblance? The genetic research program is at too early (...) a stage to answer this question with confidence, but various indicators tend to point in the same direction: the predictive ability of genetic analysis will generally be low. This conclusion runs counter to widely perceived popular notions. The deconstruction of genetic determinism is an essential safeguard against the real concern that genetic information may be used for discrimination by unscrupulous powers. (shrink)

In this study, the focus was on the development of green energy and future prediction for the consumption of current energy sources and green energy development using an improved deep learning algorithm. In addition to the analysis of the current energy consumption used for the natural gas and oil as fuel, deep neural network algorithm is used to train the system as well as to process the data obtained previously, ranging from literature from the year 2003 until the year (...) 2019, for consumption of fuel. Also, using the proposed algorithm to predict the development of green energy consumption till 2030 is presented in terms of solar and wind generators. The resulting study also focuses on depletion of energy currently used or pollution caused because of it. The green energy controlling issue can take effect by using multiple layers of handling different features extracted from different sources and then learning the system to control it.This study aims to take advantage of carbon emissions to reduce their impact and dependence in the future on environmentally friendly renewable energies. Predicting the correct and precise amount of energy consumption and increasing the amount of environmentally friendly energy lead to a healthy ecosystem. The expected green energy consumption in the future is almost 78.25 EJ in 2030 and will be, in total energy average, 56% in 2045. The aim is to reduce dependency on costly and environmentally harmful fuels. (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)

The predictive processing framework posits that people continuously use predictive principles when interacting with, learning from, and interpreting their surroundings. Here, we suggest that the same framework may help explain how people process self-relevant knowledge and maintain a stable and positive self-concept. Specifically, we recast two prominent self-relevant motivations, self-verification and self-enhancement, in predictive processing (PP) terms. We suggest that these self-relevant motivations interact with the self-concept (i.e., priors) to create strong predictions. These predictions, in turn, influence how people interpret (...) information about themselves. In particular, we argue that these strong self-relevant predictions dictate how prediction error, the deviation from the original prediction, is processed. In contrast to many implementations of the PP framework, we suggest that predictions and priors emanating from stable constructs (such as the self-concept) cultivate belief-maintaining, rather than belief-updating, dynamics. Based on recent findings, we also postulate that evidence supporting a predicted model of the self (or interpreted as such) triggers subjective reward responses, potentially reinforcing existing beliefs. Characterizing the role of rewards in self-belief maintenance and reframing self-relevant motivations and rewards in predictive processing terms offers novel insights into how the self is maintained in neurotypical adults, as well as in pathological populations, potentially pointing to therapeutic implications. (shrink)

This paper and its predecessor () are about the question: 'Are the events in the entire universe encoded in and predictable from any of its parts?' To approach a positive answer in classical physics, the following result is proved and commented on: in Newton's theory of gravitation, the entire trajectory of a particle can be predicted given any segment of it, regardless of how the other particles are moving-provided that there is only a finite number of particles and that their (...) speeds remain bounded. (It is this condition, together with a set of parameters characterising the motion of the other particles, which enables us to estimate the effect of the other particles on the trajectory of the given particle.) The extension of this result to other theories, in particular to special relativity, is discussed. (shrink)

The COVID-19 pandemic has significantly changed university students' life routines, such as prolonged stay at home and learning online without prior preparation. Identifying factors influencing student online learning has become a great concern of educators and researchers. The present study aimed to investigate whether family wellbeing would significantly predict university students' online learning effectiveness indicated by engagement and gains. The mediational role of individual wellbeing such as life satisfaction and sleep difficulties was also tested. This study collected data from 511 (...) undergraduate students via an online survey. Structural equation modeling analysis revealed positive effects of family support on students' learning engagement and gains through the mediational effects of life satisfaction and sleep difficulties. In contrast to our expectation, family conflict during the pandemic also positively predicted students' learning gains, which, however, was not mediated by individual wellbeing. The findings add value to the existing literature by delineating the inter-relationships between family wellbeing, individual wellbeing, and online learning effectiveness. The study also sheds light on the unique meaning of family conflict, which needs further clarification in future studies. (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)

How is it that thoroughly physical material beings such as ourselves can think, dream, feel, create and understand ideas, theories and concepts? How does mere matter give rise to all these non-material mental states, including consciousness itself? An answer to this central question of our existence is emerging at the busy intersection of neuroscience, psychology, artificial intelligence, and robotics.In this groundbreaking work, philosopher and cognitive scientist Andy Clark explores exciting new theories from these fields that reveal minds like ours to (...) be prediction machines - devices that have evolved to anticipate the incoming streams of sensory stimulation before they arrive. These predictions then initiate actions that structure our worlds and alter the very things we need to engage and predict. Clark takes us on a journey in discovering the circular causal flows and the self-structuring of the environment that define "the predictive brain." What emerges is a bold, new, cutting-edge vision that reveals the brain as our driving force in the daily surf through the waves of sensory stimulation. (shrink)

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

Drawing on recent findings in the cognitive and neurosciences, this article discusses how people manage to predict each other’s actions, which is fundamental for joint action. We explore how a common coding of perceived and performed actions may allow actors to predict the what, when, and where of others’ actions. The “what” aspect refers to predictions about the kind of action the other will perform and to the intention that drives the action. The “when” aspect is critical for all joint (...) actions requiring close temporal coordination. The “where” aspect is important for the online coordination of actions because actors need to effectively distribute a common space. We argue that although common coding of perceived and performed actions alone is not sufficient to enable one to engage in joint action, it provides a representational platform for integrating the actions of self and other. The final part of the paper considers links between lower‐level processes like action simulation and higher‐level processes like verbal communication and mental state attribution that have previously been at the focus of joint action research. (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.

ObjectiveTo determine whether gait and accelerometric features can predict disorientation events in young and older adults.MethodsCognitively healthy younger and older participants navigated on a treadmill through a virtual representation of the city of Rostock featured within the Gait Real-Time Analysis Interactive Lab system. We conducted Bayesian Poisson regression to determine the association of navigation performance with domain-specific cognitive functions. We determined associations of gait and accelerometric features with disorientation events in real-time data using Bayesian generalized mixed effect models. The accuracy (...) of gait and accelerometric features to predict disorientation events was determined using cross-validated support vector machines and Hidden Markov models.ResultsBayesian analysis revealed strong evidence for the effect of gait and accelerometric features on disorientation. The evidence supported a relationship between executive functions but not visuospatial abilities and perspective taking with navigation performance. Despite these effects, the cross-validated percentage of correctly assigned instances of disorientation was only 72% in the SVM and 63% in the HMM analysis using gait and accelerometric features as predictors.ConclusionDisorientation is reflected in spatiotemporal gait features and the accelerometric signal as a potentially more easily accessible surrogate for gait features. At the same time, such measurements probably need to be enriched with other parameters to be sufficiently accurate for individual prediction of disorientation events. (shrink)

Theories of blame posit that observers consider causality, controllability, and foreseeability when assigning blame to actors. The present study examined which of these factors, either on their own or in interaction, predicted blame assigned to actors in a case of harm caused by negligence. The findings revealed that only causal impact ratings predicted blame. The findings also revealed a novel form of asymmetric discounting: the causal impact of a negligent actor was used to discount blame assigned to an innocent actor, (...) but the causal impact of the innocent actor was not used to discount the blame assigned to the negligent actor. (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 current study uses a repeated measures design to compare two-time points across the COVID-19 pandemic. The first was conducted at the end of the “first wave” [T1] and the second was carried out on October 12-14 2020 in Israel. The participants completed the same questionnaire at both time points. The study examined the predictions of hope and morale at T2 by psychological and demographic predictors at T1. Results indicated the following: The three types of resilience significantly and positively predicted (...) hope and morale. Well-being significantly and positively predicted hope and morale. Younger age significantly and positively predicts higher hope, but not morale. A higher level of religiosity significantly and positively predicts higher hope and morale. More right-wing political attitudes significantly and positively predict higher hope, but not moral. More economic difficulties due to the pandemic, significantly and negatively predict hope and morale. We concluded that hope and morale can serve as significant indicators of the population's ability to cope with the COVID-19 pandemic. Furthermore, they can serve as a “thermometer” for the general mood of the population and can be used by decision-makers to assess coping ability at varied stages of the pandemic. (shrink)

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

Foreign Language Enjoyment (FLE) plays a facilitative role in FL learning and its contributing factors have been the object of scholarly attention in the Positive Psychology approach to second language acquisition (SLA). The present study examined the predictive effects of gender and academic discipline on overall FLE and each of its subcomponents in a specific Chinese EFL context. Statistical analyses based on a sample of 1,718 high school students showed that: (1) female students scored significantly higher in overall FLE,FLE-Private, andFLE-Atmospherethan (...) their male counterparts, but no significant difference was found in theirFLE-Teacher; (2) students of Humanities and Social Sciences scored significantly higher in overall FLE,FLE-TeacherandFLE-Atmospherethan students of Natural Science, though no significant difference was identified forFLE-Private. Reasons for the statistical variations and their implications were also discussed. (shrink)

1. Introduction 2. Reward-Guided Decision Making 3. Content in the Model 4. How to Deflate a Metarepresentational Reading Proust and Carruthers on metacognitive feelings 5. A Deflationary Treatment of RPEs? 5.1 Dispensing with prediction errors 5.2 What is use of the RPE focused on? 5.3 Alternative explanations—worldly correlates 5.4 Contrast cases 6. Conclusion Appendix: Temporal Difference Learning Algorithms.

The predictive inadequacy of the social sciences is well documented, and philosophers have sought to diagnose it. This paper examines Brian Epstein’s recent diagnosis. He argues that the social sciences treat the social world as entirely composed of individual people. Instead, social scientists should recognize that material, non-individualistic entities determine the social world, as well. First, I argue that Epstein’s argument both begs the question against his opponents and is not sufficiently charitable. Second, I present doubts that his proposal will (...) improve predictive success for the social sciences, which I support with Edith Penrose’s resource-based theory of the firm. (shrink)

Contrary to Aubert's claim, my paper on election predictions does not seek to draw empirical conclusions from mathematical premisses alone. The empirical premiss, approximated by the continuity assumption, is that sufficiently small changes in the predicted vote will cause only small changes in the actual vote. The technical criticisms by ?fsti and ?sterberg of the reaction function are answered by specifying the function's domain. Other criticisms are also answered, and the reply concludes by placing the election prediction theorem in (...) the context of other theorizing about human expectations and outguessing phenomena. (shrink)

With the rapid development of Internet of Things technology, RFID technology has been widely used in various fields. In order to optimize the RFID system hardware deployment strategy and improve the deployment efficiency, the prediction of the RFID system identification rate has become a new challenge. In this paper, a neighborhood rough set and random forest combination model is proposed to predict the identification rate of an RFID system. Firstly, the initial influencing factors of the RFID system identification rate (...) are reduced using neighborhood rough set theory combined with the principle of heuristic attribute reduction of neighborhood weighted dependency, thus obtaining a kernel factor subset. Secondly, a random forest prediction model is established based on the kernel factor subset, and a confusion matrix is established using out-of-bag data to evaluate the prediction results. The test is conducted under the constructed RFID experimental environment, whose results showed that the model can predict the identification rate of the RFID system in a fast and efficient way, and the classification accuracy can reach 90.5%. It can effectively guide the hardware deployment and communication parameter protocol setting of the system and improve the system performance. Compared with BP neural network and other prediction models, NRS-RF has shorter prediction time and faster calculation speed. Finally, the validity of the proposed model was verified by the RFID intelligent archives management platform. (shrink)

In recent years, the green aviation technology draws more attention, and more hybrid power units have been applied to the aerial vehicles. To achieve the high performance and long lifetime of components during varied working conditions, the effective regulation of the energy management is necessary for the vehicles with hybrid power unit. In this paper, power prediction-based model predictive control for energy management strategy is proposed for the vehicle equipped with HPU based on turboshaft engine in order to maintain (...) proper battery’s state of charge and decrease turboshaft engine’s exhaust gas temperature. First, a modeling approach based on data-driven method is adopted to obtain the mathematical model of turboshaft engine considering time delay and inertial of states. An integrated power predictor consisting of the classification of input status and the subpredictors are developed based on the deep learning method to improve the accuracy of the prediction model of the model predictive control. Subsequently, an EMS based on MPC using the proposed power predictor is introduced to regulate the SOC of battery and the EGT of turboshaft engine. The comparison with experimental results shows the high accuracy of mathematical model of turboshaft engine. The simulation results show the effectiveness of the proposed EMS for the vehicle, and the effects of different weight coefficients of objective function on the proposed EMS are discussed. (shrink)

While explaining a large proportion of any variance, accounts of the speed and accuracy of targetting movements use techniques (e.g., log transforms) that typically reduce variability before ''explaining'' the data. Therefore the predictive power of such accounts are important. We consider whether Plamondon's model can account for kinematics of targetting movements of clinical populations.

A major disagreement between different views about the foundations of quantum mechanics concerns whether for a theory to be intelligible as a fundamental physical theory it must involve a ‘primitive ontology’ (PO), i.e. variables describing the distribution of matter in four-dimensional space–time. In this article, we illustrate the value of having a PO. We do so by focusing on the role that the PO plays for extracting predictions from a given theory and discuss valid and invalid derivations of predictions. To (...) this end, we investigate a number of examples based on toy models built from the elements of familiar interpretations of quantum theory. (shrink)

Although prediction has been largely absent from discussions of explanation for the past 40 years, theories of explanation can gain much from a reintroduction. I review the history that divorced prediction from explanation, examine the proliferation of models of explanation that followed, and argue that accounts of explanation have been impoverished by the neglect of prediction. Instead of a revival of the symmetry thesis, I suggest that explanation should be understood as a cognitive tool that assists us (...) in generating new predictions. This view of explanation and prediction clarifies what makes an explanation scientific and why inference to the best explanation makes sense in science. *Received August 2009; revised September 2009. †To contact the author, please write to: Department of Philosophy, University of Tennessee, 801 McClung Tower, Knoxville, TN 37920‐0480; e‐mail: hdouglas@utk.edu. (shrink)

Equal opportunity requires that persons be selected for desirable positions on the basis of their qualifications. To assess an applicant's qualifications, we must both predict how well he would perform if chosen, and compare his projected performance with that of his rivals. Since we lack direct access to future performance, all such predictions must be based on some past– or present-tense information about the applicants, together with some relevant supporting information. But is any and every way of predicting performance acceptable? (...) Or are some methods of predicting improper even if they are more accurate than any available alternatives? And, if some methods of predicting are improper, which ones are these, and why? (shrink)

The management control system aids administrators in guiding a business toward its organizational plans; as a result, management control is primarily concerned with the execution of the plan and plans. Financial and nonfinancial criteria are used to create management control systems. The financial element focuses on net income, earnings, and other financial metrics. The two components of leadership strategy in this study are cost and differentiation, which highlight the strategy of differentiation in attaining higher quality due to the robust strategy’s (...) attention on a particular area of the company. In this study, we presented a novel method named deep interaction neural network to predict the performance of the manufacturing companies based on their leading competitors using features cost leadership and differentiation strategies. Moreover, the management control system is classified into two financial and nonfinancial factors based on machine learning methods. Based on the results, the presented factors can accurately estimate the company’s performance based on management control criteria with a 93.48% R-square. Moreover, it can be seen that the DT method is presented with higher classification performance values. (shrink)

The objective of this study was to examine the association between resilience and trajectories of college students’ negative and positive affect during the COVID-19 pandemic. A total of 391 college students recruited from China completed a daily online negative and positive affect scale for 1 week, and their resilience was also measured. Profiles of brief trajectories of negative and positive affect over time were identified using the latent growth mixture model, and the effect of resilience on these trajectories was further (...) explored. Two latent profiles of negative affect were found: a constant high negative affect profile and a slowly decreasing low negative affect profile, while three latent profiles of positive affect were identified: a slowly increasing high positive affect profile, a rapidly decreasing medium positive affect profile, and a constant medium positive affect profile. The optimism dimension of resilience predicted the membership in the various profiles significantly, whereas the prediction of tenacity and strength dimensions of resilience was not significant. Activities that promote resilience, especially optimism, should be included to improve the daily emotions of college students during COVID-19. (shrink)

A widely endorsed thesis in the philosophy of science holds that if evidence for a hypothesis was not known when the hypothesis was proposed, then that evidence confirms the hypothesis more strongly than would otherwise be the case. The thesis has been thought to be inconsistent with Bayesian confirmation theory, but the arguments offered for that view are fallacious. This paper shows how the special value of prediction can in fact be given Bayesian explanation. The explanation involves consideration of (...) the reliability of the method by which the hypothesis was discovered, and thus reveals an intimate connection between the 'logic of discovery' and confirmation theory. (shrink)