What has science actually achieved? A theory of achievement should define what has been achieved, describe the means or methods used in science, and explain how such methods lead to such achievements. Predictive accuracy is one truth‐related achievement of science, and there is an explanation of why common scientific practices tend to increase predictive accuracy. Akaike’s explanation for the success of AIC is limited to interpolative predictive accuracy. But therein lies the strength of the (...) general framework, for it also provides a clear formulation of many open problems of research. (shrink)

What has science actually achieved? A theory of achievement should define what has been achieved, describe the means or methods used in science, and explain how such methods lead to such achievements. Predictive accuracy is one truth-related achievement of science, and there is an explanation of why common scientific practices tend to increase predictive accuracy. Akaike's explanation for the success of AIC is limited to interpolative predictive accuracy. But therein lies the strength of the (...) general framework, for it also provides a clear formulation of many open problems of research. (shrink)

The use of risk assessment tools has come to play an increasingly important role in sentencing decisions in many jurisdictions. A key issue in the theoretical discussion of risk assessment concerns the predictive accuracy of such tools. For instance, it has been underlined that most risk assessment instruments have poor to moderate accuracy in most applications. However, the relation between, on the one hand, judgements of the predictive accuracy of a risk assessment tool and, on (...) the other, conclusions concerning the justified use of such an instrument in sentencing practice, is often very unclear. The purpose of this paper is to examine this relation. More precisely, it is argued that the relation between predictive accuracy and the question as to whether a new risk assessment tool should be introduced in sentencing practice is highly complicated. For instance, there may be cases in which a new risk assessment tool is more accurate than those currently in use, but should nevertheless not be introduced; and conversely, where a new tool is less accurate, but where its introduction instead of current tools would be morally preferable. (shrink)

Prediction is essential for the efficiency of many cognitive processes; however, this process is not always perfect. Predictive coding theory suggests that the brain generates and updates a prediction to respond to an upcoming event. Although an electrophysiological index of prediction, the stimulus preceding negativity, has been reported, it remains unknown whether the SPN reflects the prediction accuracy, or whether it is associated with the prediction error, which corresponds to a mismatch between a prediction and an actual input. (...) Thus, the present study aimed to investigate this question using electroencephalography. Participants were asked to predict the original pictures from pictures that had undergone different levels of pixelation. The SPN amplitude was affected by the level of pixelation and correlated with the subjective evaluation of the prediction accuracy. Furthermore, late positive components were negatively correlated with SPN. These results suggest that the amplitude of SPN reflects the prediction accuracy; more accurate prediction increases the SPN and reduces the prediction error, resulting in reduced LPC amplitudes. (shrink)

Wayne Myrvold (2003) has captured an important feature of unified theories, and he has done so in Bayesian terms. What is not clear is whether the virtue of such unification is most clearly understood in terms of Bayesian confirmation. I argue that the virtue of such unification is better understood in terms of other truth-related virtues such as predictive accuracy.

Wayne Myrvold (2003) has captured an important feature of unified theories, and he has done so in Bayesian terms. What is not clear is whether the virtue of such unification is most clearly understood in terms of Bayesian confirmation. I argue that the virtue of such unification is better understood in terms of other truth-related virtues such as predictive accuracy.

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.

Recent accounts of the speed/accuracy relation for motor tasks have focused on the concept of motor output variability. We outline the advantages of this approach and the limitation of Plamondon's model in explaining movement error. We also examine and present complimentary data for rapid timing tasks. While these tasks do not meet the presented assumptions, the data still fit the model predictions.

The paper reports a study that investigated the relationship between students? self-predicted and actual General Certificate of Secondary Education results in order to establish the extent of over- and under-prediction and whether this varies by subject and across genders and socio-economic groupings. It also considered the relationship between actual and predicted attainment and attitudes towards going to university. The sample consisted of 109 young people in two schools being followed up from an earlier study. Just over 50% of predictions were (...) accurate and students were much more likely to over-predict than to under-predict. Most errors of prediction were only one grade out and may reflect examination unreliability as well as student misperceptions. Girls were slightly less likely than boys to over-predict but there were no differences associated with social background. Higher levels of attainment, both actual and predicted, were strongly associated with positive attitudes to university. Differences between predictions and results are likely to reflect examination errors as well as pupil errors. There is no evidence that students from more advantaged social backgrounds over-estimate themselves compared with other students, although boys over-estimate themselves compared with girls. (shrink)

I present a justification for the intution that more-varied data are more valuable than the same number of less-varied data by showing that the more-varied data help to improve the accuracy of our predictions.

Prediction markets are low volume speculative markets whose prices offer informative forecasts on particular policy topics. Observers worry that traders may attempt to mislead decision makers by manipulating prices. We adapt a Kyle-style market microstructure model to this case, adding a manipulator with an additional quadratic preference regarding the price. In this model, when other traders are uncertain about the manipulator’s target price, the mean target price has no effect on prices, and increases in the variance of the target price (...) can increase average price accuracy, by increasing the returns to informed trading and thereby incentives for traders to become informed. (shrink)

Heart disease is the deadliest disease and one of leading causes of death worldwide. Machine learning is playing an essential role in the medical side. In this paper, ensemble learning methods are used to enhance the performance of predicting heart disease. Two features of extraction methods: linear discriminant analysis and principal component analysis, are used to select essential features from the dataset. The comparison between machine learning algorithms and ensemble learning methods is applied to selected features. The different methods are (...) used to evaluate models: accuracy, recall, precision, F-measure, and ROC.The results show the bagging ensemble learning method with decision tree has achieved the best performance. (shrink)

Base rates are vital in predicting violent criminal recidivism. However, both lay people given simulated prediction tasks and professionals milking real life predictions appear insensitive to variations in the base rate of violent recidivism. Although there are techniques to help decision makers attend to base rates, increased decision accuracy is better sought in improved actuarial models as opposed to improved clinicians.

Link prediction is a key tool for studying the structure and evolution mechanism of complex networks. Recommending new friend relationships through accurate link prediction is one of the important factors in the evolution, development, and popularization of social networks. At present, scholars have proposed many link prediction algorithms based on the similarity of local information and random walks. These algorithms help identify actual missing and false links in various networks. However, the prediction results significantly differ in networks with various structures, (...) and the prediction accuracy is low. This study proposes a method for improving the accuracy of link prediction. Before link prediction, k-shell decomposition method is used to layer the network, and the nodes that are in 1-shell and the nodes that are not linked to the high-shell in the 2-shell are deleted. The experiments on four real network datasets verify the effectiveness of the proposed method. (shrink)

We present a theoretical and an empirical challenge to Cushman's claim that rationalization is adaptive because it allows humans to extract more accurate beliefs from our non-rational motivations for behavior. Rationalization sometimes generates more adaptive decisions by making our beliefs about the world less accurate. We suggest that the most important adaptive advantage of rationalization is instead that it increases our predictability as potential partners in cooperative social interactions.

The Serial Reaction Time Task (SRTT) was designed to measure motor sequence learning and is widely used in many fields in cognitive science and neuroscience. However, the common performance measures derived from SRTT—reaction time (RT) difference scores—may not provide valid measures of sequence learning. This is because RT-difference scores may be subject to floor effects and otherwise not sufficiently reflective of learning. A ratio RT measure might minimize floor effects. Furthermore, measures derived from predictive accuracy may provide a (...) better assessment of sequence learning. Accordingly, we developed a Predictive Sequence Learning Task (PSLT) in which performance can be assessed via both RT and predictive accuracy. We compared performance of N = 99 adults on SRTT and PSLT in a within-subjects design and also measured fluid abilities. The RT-difference scores on both tasks were generally not related to fluid abilities, replicating previous findings. In contrast, a ratio RT measure on SRTT and PSLT and accuracy measures on PSLT were related to fluid abilities. The accuracy measures also indicated an age-related decline in performance on PSLT. The current patterns of results were thus inconsistent across different measures on the same tasks, and we demonstrate that this discrepancy is potentially due to floor effects on the RT difference scores. This may limit the potential of SRTT to measure sequence learning and we argue that PSLT accuracy measures could provide a more accurate reflection of learning ability. (shrink)

This article reports four subliminal perception experiments using the relationship between confidence and accuracy to assess awareness. Subjects discriminated among stimuli and indicated their confidence in each discrimination response. Subjects were classified as being aware of the stimuli if their confidence judgments predicted accuracy and as being unaware if they did not. In the first experiment, confidence predicted accuracy even at stimulus durations so brief that subjects claimed to be performing at chance. This finding indicates that subjects's (...) claims that they are ''just guessing'' should not be accepted as sufficient evidence that they are completely unaware of the stimuli. Experiments 2-4 tested directly for subliminal perception by comparing the minimum exposure duration needed for better than chance discrimination performance against the minimum needed for confidence to predict accuracy. The latter durations were slightly but significantly longer, suggesting that under certain circumstances people can make perceptual discriminations even though the information that was used to make those discriminations is not consciously available. (shrink)

Predictive processing is often presented as a unifying framework for perception, action, and cognition, being able to explain most mental phenomena : with regard to perception, the brain harbours a generative model issuing top-down expectations that are matched against bottom-up sensory feedback. Mismatches lead to error messages and model updates until the brain is 'getting it right'. The core notion of prediction error minimization commits the framework to a specification of accuracy conditions. We therefore turn to issues related (...) to the determination of satisfaction conditions discussed in the philosophy of perception. In particular, we rely on work by Recanati who shows that the accuracy conditions of perceptual content are partly determined by the intentional mode and the perceiver. It is important to notice that the self can enter the specification of accuracy conditions in two ways, namely as subject or as object. Even if we do not perceive ourselves explicitly as an object, we always implicitly represent ourselves as subject. We discuss whether and how these two ways of self-representation can be respected in the predictive processing framework. (shrink)

The use of machine learning instead of traditional models in neuroscience raises significant questions about the epistemic benefits of the newer methods. I draw on the literature on model intelligibility in the philosophy of science to offer some benchmarks for the interpretability of artificial neural networks used as a predictive tool in neuroscience. Following two case studies on the use of ANN’s to model motor cortex and the visual system, I argue that the benefit of providing the scientist with (...) understanding of the brain trades off against the predictive accuracy of the models. This trade-off between prediction and understanding is better explained by a non-factivist account of scientific understanding. (shrink)

Are people are product literate enough to make informed decisions about plant-based and animal-based milk products? In 8 studies, we provide evidence that consumers do not make mistakes indicative of pervasive lack of milk product literacy. People were accurate at identifying plant-based and animal-based milk and cheese products as being plant or animal-based (74% - 84% of the time). In a more difficult task, participants were generally accurate at identifying nutritional differences between plant-based and animal-based milk and cheese products (50%–62% (...) accuracy). We also developed the Milk Literacy Scale, which is a 12-item, validated, knowledge-based instrument that measures knowledge of differences among plant-based and animal-based milk products. The Milk Literacy Scale predicted accuracy in both identification tasks. All results were replicated in a large sample (N = 1054). These results suggest that people are generally product literate about milk products to make informed choices. The studies offer some insights into what kinds of interventions would help make people even more product literate. (shrink)

Spontaneous eye blink rate has been linked to attention and memory, specifically working memory. sEBR is also related to striatal dopamine activity with schizophrenia and Parkinson’s disease showing increases and decreases, respectively, in sEBR. A weakness of past studies of sEBR and WM is that correlations have been reported using blink rates taken at baseline either before or after performance of the tasks used to assess WM. The goal of the present study was to understand how fluctuations in sEBR during (...) different phases of a visual WM task predict task accuracy. In two experiments, with recordings of sEBR collected inside and outside of a magnetic resonance imaging bore, we observed sEBR to be positively correlated with WM task accuracy during the WM delay period. We also found task-related modulation of sEBR, including higher sEBR during the delay period compared to rest, and lower sEBR during task phases that place demands on visual attention. These results provide further evidence that sEBR could be an important predictor of WM task performance with the changes during the delay period suggesting a role in WM maintenance. The relationship of sEBR to DA activity and WM maintenance is discussed. (shrink)

This target article presents a critical survey of the scientific literature dealing with the speed/accuracy trade-offs in rapid-aimed movements. It highlights the numerous mathematical and theoretical interpretations that have been proposed in recent decades. Although the variety of points of view reflects the richness of the field and the high degree of interest that such basic phenomena attract in the understanding of human movements, it calls into question the ability of many models to explain the basic observations consistently reported (...) in the field. This target article summarizes the kinematic theory of rapid human movements, proposed recently by R. Plamondon (1993b; 1993c; 1995a; 1995b), and analyzes its predictions in the context of speed/accuracy trade-offs. Data from human movement literature are reanalyzed and reinterpreted in the context of the new theory. It is shown that the various aspects of speed/accuracy trade-offs can be taken into account by considering the asymptotic behavior of a large number of coupled linear systems, from which a delta-lognormal law can be derived to describe the velocity profile of an end-effector driven by a neuromuscular synergy. This law not only describes velocity profiles almost perfectly, it also predicts the kinematic properties of simple rapid movements and provides a consistent framework for the analysis of different types of speed/accuracy trade-offs using a quadratic (or power) law that emerges from the model. (shrink)

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to (...) predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3%. (shrink)

The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because (...) it helps to maximize patients’ benefits and optimizes limited resources. However, we claim that the opaqueness of the algorithmic black box and its absence of explainability threatens core commitments of procedural fairness such as accountability, avoidance of bias, and transparency. To illustrate this, we discuss liver transplantation as a case of critical medical resources in which the lack of explainability in AI-based allocation algorithms is procedurally unfair. Finally, we provide a number of ethical recommendations for when considering the use of unexplainable algorithms in the distribution of health-related resources. (shrink)

Crop cultivation is one of the oldest activities of civilization. For a long time, crop production was carried out based on knowledge passed from generation to generation. However, due to the rapid growth in the human population of the world, human knowledge-based cultivation is not enough to meet the demanding need. To address this issue, the usage of machine learning-based tools has been studied in this paper. An experiment has been carried out over 0.3 million data. This dataset identifies 46 (...) prominent parameters for cultivation, which is collected from the Department of Agriculture Extension, Bangladesh. Comparison between neural networks and numbers of machine learning algorithms has been carried out in this research. It is observed that the neural network outperforms the other methods by maintaining an average prediction accuracy of 96.06% for six different crops. Other contemporary machine learning algorithms, namely, support vector machine, random forest, and logistic regression, have average prediction accuracy of around 68.9%, 91.2%, and 62.39%, respectively. (shrink)