Several researchers have recently argued that p values lose their meaning in exploratory analyses due to an unknown inflation of the alpha level (e.g., Nosek & Lakens, 2014; Wagenmakers, 2016). For this argument to be tenable, the familywise error rate must be defined in relation to the number of hypotheses that are tested in the same study or article. Under this conceptualization, the familywise error rate is usually unknowable in exploratory analyses because it is (...) usually unclear how many hypotheses have been tested on a spontaneous basis and then omitted from the final research report. In the present article, I argue that it is inappropriate to conceptualize the familywise error rate in relation to the number of hypotheses that are tested. Instead, it is more appropriate to conceptualize familywise error in relation to the number of different tests that are conducted on the same null hypothesis in the same study. Under this conceptualization, alpha level adjustments in exploratory analyses are (a) less necessary and (b) objectively verifiable. As a result, p values do not lose their meaning in exploratory analyses. (shrink)

During multiple testing, researchers often adjust their alpha level to control the familywise error rate for a statistical inference about a joint union alternative hypothesis (e.g., “H1,1 or H1,2”). However, in some cases, they do not make this inference. Instead, they make separate inferences about each of the individual hypotheses that comprise the joint hypothesis (e.g., H1,1 and H1,2). For example, a researcher might use a Bonferroni correction to adjust their alpha level from the conventional level of (...) 0.050 to 0.025 when testing H1,1 and H1,2, find a significant result for H1,1 (p < 0.025) and not for H1,2 (p > .0.025), and so claim support for H1,1 and not for H1,2. However, these separate individual inferences do not require an alpha adjustment. Only a statistical inference about the union alternative hypothesis “H1,1 or H1,2” requires an alpha adjustment because it is based on “at least one” significant result among the two tests, and so it refers to the familywise error rate. Hence, an inconsistent correction occurs when a researcher corrects their alpha level during multiple testing but does not make an inference about a union alternative hypothesis. In the present article, I discuss this inconsistent correction problem, including its reduction in statistical power for tests of individual hypotheses and its potential causes vis-à-vis error rate confusions and the alpha adjustment ritual. I also provide three illustrations of inconsistent corrections from recent psychology studies. I conclude that inconsistent corrections represent a symptom of statisticism, and I call for a more nuanced inference-based approach to multiple testing corrections. (shrink)

Scientists often adjust their significance threshold during null hypothesis significance testing in order to take into account multiple testing and multiple comparisons. This alpha adjustment has become particularly relevant in the context of the replication crisis in science. The present article considers the conditions in which this alpha adjustment is appropriate and the conditions in which it is inappropriate. A distinction is drawn between three types of multiple testing: disjunction testing, conjunction testing, and individual testing. It is argued that alpha (...) adjustment is only appropriate in the case of disjunction testing, in which at least one test result must be significant in order to reject the associated joint null hypothesis. Alpha adjustment is inappropriate in the case of conjunction testing, in which all relevant results must be significant in order to reject the joint null hypothesis. Alpha adjustment is also inappropriate in the case of individual testing, in which each individual result must be significant in order to reject each associated individual null hypothesis. The conditions under which each of these three types of multiple testing is warranted are examined. It is concluded that researchers should not automatically assume that alpha adjustment is necessary during multiple testing. Illustrations are provided in relation to joint studywise hypotheses and joint multiway ANOVAwise hypotheses. (shrink)

In fMRI research, the goal of correcting for multiple comparisons is to identify areas of activity that reflect true effects, and thus would be expected to replicate in future studies. Finding an appropriate balance between trying to minimize false positives (Type I error) while not being too stringent and omitting true effects (Type II error) can be challenging. Furthermore, the advantages and disadvantages of these types of errors may differ for different areas of study. In many areas of (...) social neuroscience that involve complex processes and considerable individual differences, such as the study of moral judgment, effects are typically smaller and statistical power weaker, leading to the suggestion that less stringent corrections that allow for more sensitivity may be beneficial, but also result in more false positives. Using moral judgment fMRI data, we evaluated four commonly used methods for multiple comparison correction implemented in SPM12 by examining which method produced the most precise overlap with results from a meta-analysis of relevant studies and with results from nonparametric permutation analyses. We found that voxel-wise thresholding with family-wise error correction based on Random Field Theory provides a more precise overlap (i.e., without omitting too few regions or encompassing too many additional regions) than either clusterwise thresholding, Bonferroni correction, or false discovery rate correction methods. (shrink)

The inflation of Type I error rates is thought to be one of the causes of the replication crisis. Questionable research practices such as p-hacking are thought to inflate Type I error rates above their nominal level, leading to unexpectedly high levels of false positives in the literature and, consequently, unexpectedly low replication rates. In this article, I offer an alternative view. I argue that questionable and other research practices do not usually inflate relevant Type I error (...) rates. I begin with an introduction to Type I error rates that distinguishes them from theoretical errors. I then illustrate my argument with respect to model misspecification, multiple testing, selective inference, forking paths, exploratory analyses, p-hacking, optional stopping, double dipping, and HARKing. In each case, I demonstrate that relevant Type I error rates are not usually inflated above their nominal level, and in the rare cases that they are, the inflation is easily identified and resolved. I conclude that the replication crisis may be explained, at least in part, by researchers’ misinterpretation of statistical errors and their underestimation of theoretical errors. (shrink)

Many contemporary philosophers rate error theories poorly. We identify the arguments these philosophers invoke, and expose their deficiencies. We thereby show that the prospects for error theory have been systematically underestimated. By undermining general arguments against all error theories, we leave it open whether any more particular arguments against particular error theories are more successful. The merits of error theories need to be settled on a case-by-case basis: there is no good general argument against (...) error theories. (shrink)

The replication crisis has caused researchers to distinguish between exact replications, which duplicate all aspects of a study that could potentially affect the results, and direct replications, which duplicate only those aspects of the study that are thought to be theoretically essential to reproduce the original effect. The replication crisis has also prompted researchers to think more carefully about the possibility of making Type I errors when rejecting null hypotheses. In this context, the present article considers the utility of two (...) types of Type I error probability: the Neyman–Pearson long run Type I error rate and the Fisherian sample-specific Type I error probability. It is argued that the Neyman–Pearson Type I error rate is inapplicable in social science because it refers to a long run of exact replications, and social science deals with irreversible units that make exact replications impossible. Instead, the Fisherian sample-specific Type I error probability is recommended as a more meaningful way to conceptualize false positive results in social science because it can be applied to each sample-specific decision about rejecting the same substantive null hypothesis in a series of direct replications. It is concluded that the replication crisis may be partly due to researchers’ unrealistic expectations about replicability based on their consideration of the Neyman–Pearson Type I error rate across a long run of exact replications. (shrink)

In the context of discovery-oriented hypothesis testing research, behavioral scientists widely accept a convention for false positive (α) and false negative error rates (β) proposed by Jacob Cohen, who deemed the general relative seriousness of the antecedently accepted α = 0.05 to be matched by β = 0.20. Cohen’s convention not only ignores contexts of hypothesis testing where the more serious error is the β-error. Cohen’s convention also implies for discovery-oriented hypothesis testing research that a statistically significant (...) observed effect is four times more probable to be a mistaken discovery than for a statistically significant true observed effect to be independently replicable. In the long run, Cohen’s convention thus is epistemically harmful to the development of a progressive science of human behavior, making its acceptance crucial in explaining the replication crisis in behavioral science. The balance between α- and β-errors generally ought to be struck using both epistemic and practical considerations. Yet epistemic considerations alone imply that making a genuine contribution to the body of knowledge in behavioral science requires error rates that are not only small but also symmetrical. (shrink)

The aim of this article is to address the issue of measuring radio audience from a methodological perspective. Although our approach is contextualized, the case study of radio audience measurement in Romania is relevant to a large number of EU countries, in which scholars and practitioners use Day-After-Recall technique. Considering the size of the radio advertising market, as well as the number of radio listeners (about 90% in EU), it is crucial for both radio broadcasters and advertisers to measure radio (...) audience with high accuracy. First, we briefly review the main definitions of key concepts used in radio audience evaluation: audience, listener, rating, market share, daily-reach, or time spent listening. Next, we discuss two paradigms of audience and two theoretical models of mass communication used as a framework for measuring radio audience. Further, we briefly approach the issue of potential sources of error in rating data, by focusing on response error. Next, we emphasize the advantages and disadvantages of each quantitative method utilized in radio audience measurement, and present the case study of radio audience measurement in Romania. Based on a methodological critical review, we outline challenges and potential solutions for assessing more accurately radio audience. Finally, we reflect on the future of radio in digital era, and refer to new perspectives of upgrading existing research techniques. (shrink)

This paper reveals errors within Norwich et al.’s Entropy Theory of Perception, errors that have broad implications for our understanding of perception. What Norwich and coauthors dubbed their “informational theory of neural coding” is based on cybernetics, that is, control and communication in man and machine. The Entropy Theory uses information theory to interpret human performance in absolute judgments. There, the continuum of the intensity of a sensory stimulus is cut into categories and the subject is shown exemplar stimuli of (...) each category. The subject must then identify individual exemplars by category. The identifications are recorded in the Garner-Hake version of the Shannon “confusion matrix”. The matrix yields “H”, the entropy (degree of uncertainty) about what stimulus was presented. Hypothetically, uncertainty drops as a stimulus lengthens, i.e. a plot of H vs. stimulus duration should fall monotonically. Such “adaptation” is known for both sensation and firing rate. Hence, because “the physiological adaptation curve has the same general shape as the psychophysical adaptation curve”, Norwich et al. assumed that both have the same time course; sensation and firing rate were thus both declared proportional to H. However, a closer look reveals insurmountable contradictions. First, the peripheral neuron hypothetically cannot fire in response to a stimulus of a given intensity until after somehow computing H from its responses to stimuli of various intensities. Thus no sensation occurs until firing rate adapts, i.e. attains its spontaneous rate. But hypothetically, once adaptation is complete, certainty is reached and perception ends. Altogether, then, perception cannot occur until perception is over. Secondly, sensations, firing rates, and H’s are empirically not synchronous, contrary to assumption. In sum, the core concept of the cybernetics-based Entropy Theory of Perception, that is, that uncertainty reduction is the basis for perception, is irrational. (shrink)

Fisher criticised the Neyman-Pearson approach to hypothesis testing by arguing that it relies on the assumption of “repeated sampling from the same population.” The present article considers the responses to this criticism provided by Pearson and Neyman. Pearson interpreted alpha levels in relation to imaginary replications of the original test. This interpretation is appropriate when test users are sure that their replications will be equivalent to one another. However, by definition, scientific researchers do not possess sufficient knowledge about the relevant (...) and irrelevant aspects of their tests and populations to be sure that their replications will be equivalent to one another. Pearson also interpreted the alpha level as a personal rule that guides researchers’ behavior during hypothesis testing. However, this interpretation fails to acknowledge that the same researcher may use different alpha levels in different testing situations. Addressing this problem, Neyman proposed that the average alpha level adopted by a particular researcher can be viewed as an indicator of that researcher’s typical Type I error rate. Researchers’ average alpha levels may be informative from a metascientific perspective. However, they are not useful from a scientific perspective. Scientists are more concerned with the error rates of specific tests of specific hypotheses, rather than the error rates of their colleagues. It is concluded that neither Neyman nor Pearson adequately rebutted Fisher’s “repeated sampling” criticism. Fisher’s significance testing approach is briefly considered as an alternative to the Neyman-Pearson approach. (shrink)

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)

The present study examined the relationship between left–right discrimination (LRD) performance and handedness, sex and cognitive abilities. In total, 31 men and 35 women – with a balanced ratio of left-and right-handers – completed the Bergen Left–Right Discrimination Test. We found an advantage of left-handers in both identifying left hands and in verifying “left” propositions. A sex effect was also found, as women had an overall higher error rate than men, and increasing difficulty impacted their reaction time more (...) than it did for men. Moreover, sex interacted with handedness and manual preference strength. A negative correlation of LRD reaction time with visuo-spatial and verbal long-term memory was found independently of sex, providing new insights into the relationship between cognitive skills and performance on LRD. (shrink)

Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, consisting of three (...) layers (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (shrink)

Abstract: Rice, as a paramount staple crop worldwide, sustains billions of lives. Precise classification of rice types holds immense agricultural, nutritional, and economic significance. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing rice type classification accuracy and efficiency. This research explores rice type classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 18,188 entries and key rice grain attributes, we develop and evaluate a neural network model. Our neural network, featuring a (...) single hidden layer, achieves remarkable results—a staggering 100% accuracy and a minute average error rate of 0.000001. Beyond performance metrics, we delve into the intricacies of rice type classification through feature importance analysis. The most influential features—'id,' 'Area,' 'MajorAxisLength,' 'MinorAxisLength,' 'Eccentricity,' 'ConvexArea,' 'EquivDiameter,' 'Extent,' 'Perimeter,' 'Roundness,' and 'AspectRatio'—uncover the physiological traits underpinning accurate rice classification. This research contributes to advancing rice classification methods and highlights the potential of ANNs in optimizing agricultural practices, ensuring food safety, and bolstering global trade. (shrink)

Over the last decade, philosophers of science have extensively criticized the epistemic superiority of randomized controlled trials for testing safety and effectiveness of new drugs, defending instead various forms of evidential pluralism. We argue that scientific methods in regulatory decision-making cannot be assessed in epistemic terms only: there are costs involved. Drawing on the legal distinction between rules and standards, we show that drug regulation based on evidential pluralism has much higher costs than our current RCT-based system. We analyze these (...) costs and advocate for evaluating any scheme for drug regulatory tests in terms of concrete empirical benchmarks, like the error rates of regulatory decisions. (shrink)

Abstract: In this study, we explore the possibility of classifying the plant species. We collected the plant species from Kaggle website. This dataset encompasses 544 samples, encompassing 136 distinct plant species. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing plant Species classification accuracy and efficiency. This research explores plant Species classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 544 entries, we develop and evaluate a neural network model. Our neural network, (...) featuring a single hidden layer, achieves remarkable results—a staggering 100% accuracy and a minute average error rate of 0.002. Beyond performance metrics, we delve into the intricacies of plant Species classification through feature importance analysis. The most influential features— Vegsout, durflow, semiros, pdias, begflow, wind, leafy, autopoll and insects— uncover the physiological traits underpinning accurate rice classification. This research contributes to advancing rice classification methods and highlights the potential of ANNs in optimizing agricultural practices, ensuring plant safety, and bolstering global trade. (shrink)

This paper presents REAL, a Real-Valued Attribute Classification Tree Learning Algorithm. Several of the algorithm's unique features are explained by úe users' demands for a decision support tool to be used for evaluating financial operations strategies. Compared to competing algorithms, in our applications, REAL presents maj or advantages : (1) The REAL classification trees usually have smaller error rates. (2) A single conviction (or trust) measure at each leaf is more convenient than the traditional (probability, confidence-level) pair. (3) No (...) need for an external pruning criterion. (shrink)

In modern days, person-computer communication systems have gradually penetrated our lives. One of the crucial technologies in person-computer communication systems, Speech Emotion Recognition (SER) technology, permits machines to correctly recognize emotions and greater understand users' intent and human-computer interlinkage. The main objective of the SER is to improve the human-machine interface. It is also used to observe a person's psychological condition by lie detectors. Automatic Speech Emotion Recognition(SER) is vital in the person-computer interface, but SER has challenges for accurate recognition. (...) In this work to resolve the above problem, automatic Speech enhancement shows that deep learning techniques effectively eliminate background noise. Using Deep leaning models for four states were created: happy, sad, angry, and intoxicated. Recurrent Neural Network (RNN) algorithm used to reduce the possibility of over fitting by randomly omitting neurons in the hidden layers. The proposed RNN method could be implemented in personal assistant systems to give better and more appropriate state-based interactions between humans. In the simulation results shows Improving accuracy, Time complexity, Error rate is also reduced to using the proposed method. -/- . (shrink)

DOI: 10.1080/00031305.2018.1564697 When the editors of Basic and Applied Social Psychology effectively banned the use of null hypothesis significance testing (NHST) from articles published in their journal, it set off a fire-storm of discussions both supporting the decision and defending the utility of NHST in scientific research. At the heart of NHST is the p-value which is the probability of obtaining an effect equal to or more extreme than the one observed in the sample data, given the null hypothesis and (...) other model assumptions. Although this is conceptually different from the probability of the null hypothesis being true, given the sample, p-values nonetheless can provide evidential information, toward making an inference about a parameter. Applying a 10,000-case simulation described in this article, the authors found that p-values’ inferential signals to either reject or not reject a null hypothesis about the mean (α = 0.05) were consistent for almost 70% of the cases with the parameter’s true location for the sampled-from population. Success increases if a hybrid decision criterion, minimum effect size plus p-value (MESP), is used. Here, rejecting the null also requires the difference of the observed statistic from the exact null to be meaningfully large or practically significant, in the researcher’s judgment and experience. The simulation compares performances of several methods: from p-value and/or effect size-based, to confidence-interval based, under various conditions of true location of the mean, test power, and comparative sizes of the meaningful distance and population variability. For any inference procedure that outputs a binary indicator, like flagging whether a p-value is significant, the output of one single experiment is not sufficient evidence for a definitive conclusion. Yet, if a tool like MESP generates a relatively reliable signal and is used knowledgeably as part of a research process, it can provide useful information. (shrink)

The phenomenon of medical overtesting in general, and specifically in the emergency room, is well-known and regarded as harmful to both the patient and the healthcare system. Although the implications of this problem raise myriad ethical concerns, this paper explores the extent to which overtesting might mitigate race-based health inequalities. Given that medical malpractice and error greatly increase when the patients belong to a racial minority, it is no surprise that the mortality rate similarly increases in proportion to (...) white patients. For these populations, an environment that emphasizes medical overtesting may well be the desirable medical environment until care evens out among races and ethnicities; additionally, efforts to lower overtesting in conjunction with a high rate of racist medical mythology may cause harm by lower testing when it is actually warranted. Furthermore, medical overtesting may help to assuage racial distrust. This paper ultimately concludes that an environment of medical overtesting may be less pernicious than the alternative. (shrink)

Reprinted with modification and permission from Kennedy Institute of Ethics Journal. The phenomenon of medical overtesting in general, and specifically in the emergency room, is well-known and regarded as harmful to both the patient and the healthcare system. Although the implications of this problem raise myriad ethical concerns, this chapter explores the extent to which overtesting might mitigate race-based health inequalities. Given that medical malpractice and error greatly increase when the patients belong to a racial minority, it is no (...) surprise that the mortality rate similarly increases in proportion to white patients. For these populations, an environment that emphasizes medical overtesting may well be the desirable medical environment until care evens out among races and ethnicities; additionally, efforts to lower overtesting in conjunction with a high rate of racist medical mythology may cause harm by lower testing when it is actually warranted. Furthermore, medical overtesting may help to assuage racial distrust. This paper ultimately concludes that an environment of medical overtesting may be less pernicious than the alternative. (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)

heart attack analysis & prediction dataset is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 304 samples with 11 features, such as age, sex, chest pain type, Trtbps, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and (...) HeartDisease. A neural network model with four layers (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 88% and an average error of 0078296. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

Abstract: This study has effectively tackled the critical challenge of accurate calorie prediction in dishes by employing a robust neural network-based model. With an outstanding accuracy rate of 99.32% and a remarkably low average error of 0.009, our model has showcased its proficiency in delivering precise calorie estimations. This achievement equips individuals, healthcare practitioners, and the food industry with a powerful tool to promote healthier dietary choices and elevate awareness of nutrition. Furthermore, our in-depth feature importance analysis has (...) shed light on the indispensable role played by specific nutritional attributes in calorie estimation. This unveiling of crucial factors provides valuable insights for further research endeavors and practical applications. Notably, this research extends its impact beyond the immediate context by making substantial contributions to the domains of nutrition science and dietary planning. It underscores the transformative potential of artificial intelligence, demonstrating how it can revolutionize our approach to food, nutrition, and health. As the world grapples with the challenges of diet-related health issues and environmental concerns, the accuracy and precision achieved in calorie prediction through neural networks represent a significant stride towards more informed and conscientious dietary practices. In this era of data-driven decision-making, our research paves the way for healthier lifestyles, heightened nutritional awareness, and a more health-conscious society. (shrink)

I review recent empirical findings on knowledge attributions in lottery cases and report a new experiment that advances our understanding of the topic. The main novel finding is that people deny knowledge in lottery cases because of an underlying qualitative difference in how they process probabilistic information. “Outside” information is generic and pertains to a base rate within a population. “Inside” information is specific and pertains to a particular item’s propensity. When an agent receives information that 99% of all (...) lottery tickets lose (outside information), people judge that she does not know that her ticket will lose. By contrast, when an agent receives information that her specific ticket is 99% likely to lose (inside information), people judge that she knows that her ticket will lose. Despite this difference in knowledge judgments, people rate the likelihood of her ticket losing the exact same in both cases (i.e. 99%). The results shed light on other factors affecting knowledge judgments in lottery cases, including formulaic expression and participants’ own estimation of whether it is true that the ticket will lose. The results also undermine previous hypotheses offered for knowledge denial in lottery cases, including the hypotheses that people deny knowledge because they either deny justification or acknowledge a chance for error. (shrink)

The iterated Prisoner’s Dilemma has become the standard model for the evolution of cooperative behavior within a community of egoistic agents, frequently cited for implications in both sociology and biology. Due primarily to the work of Axelrod (1980a, 198Ob, 1984, 1985), a strategy of tit for tat (TFT) has established a reputation as being particularly robust. Nowak and Sigmund (1992) have shown, however, that in a world of stochastic error or imperfect communication, it is not TFT that finally triumphs (...) in an ecological model based on population percentages (Axelrod and Hamilton 1981), but ‘generous tit for tat’ (GTFT), which repays cooperation with a probability of cooperation approaching 1 but forgives defection with a probability of l/3. In this paper, we consider a spatialized instantiation of the stochastic Prisoner’s Dilemma, using two-dimensional cellular automata (Wolfram, 1984, 1986; Gutowitz, 1990) to model the spatial dynamics of populations of competing strategies. The surprising result is that in the spatial model it is not GIFT but still more generous strategies that are favored. The optimal strategy within this spatial ecology appears to be a form of ‘bending over backwards’, which returns cooperation for defection with a probability of 2/3 - a rate twice as generous as GTFT. (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)

Abstract—Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer dataset is used to perform a (...) data mining practical experiment using the Waikato Environment for Knowledge Analysis (WEKA) tool. The WEKA Java application represents a rich resource for conducting performance metrics during the execution of experiments. Pre-processing and feature extraction are used to optimize the data. The classification process used in this study was summarized through thirteen experiments. Additionally, 10 experiments using various different classification algorithms were conducted. The introduced algorithms were: Naïve Bayes, Logistic Regression, Lazy IBK (Instance-Bases learning with parameter K), Lazy Kstar, Lazy Locally Weighted Learner, Rules ZeroR, Decision Stump, Decision Trees J48, Random Forest and Random Trees. The process of producing a predictive model was automated with the use of classification accuracy. Further, several experiments on classification of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer, were conducted to compare the success rates of the different methods. Results conclude that Lazy IBK classifier k-NN can achieve 98% accuracy among other classifiers. The main advantages of the study were the compactness of using 13 different data mining models and 10 different performance measurements, and plotting figures of classifications errors. (shrink)

This article focuses on five flaws of Christian Fuchs’ approach of Web 2.0 economy. Here, Fuchs’ views on immaterial production, productivity of labor, commodification of users’ data, underestimation of financial aspects of digital economy, and the violation of Marx’s laws of value production, rate of exploitation, fall tendency of profit rate, and overproduction crisis are put into question. This article defends the thesis Fuchs fails to apply Marxian political economy to the contemporary phenomena of Web 2.0 economy. It (...) is possible to avoid Fuchs’ errors, and another approach is possible to remake Marxism relevant for an analysis of the new media economy. (shrink)

Dear Colleagues, -/- Recently, for an article submitted to the European Journal of Therapeutics, it was reported that the paper may have been written with artificial intelligence support at a rate of more than 50% in the preliminary examination made with Turnitin. However, the authors did not mention this in the article’s material method or explanations section. Fortunately, the article’s out-of-date content and fundamental errors in its methodology allowed us no difficulty making the desk rejection decision. -/- On the (...) other hand, similar situations that we may encounter later caused us to discuss how we would decide when the artificial intelligence support of the articles was written. The general opinion that we have adopted and currently available in the literature is that if artificial intelligence is used while writing an article, how artificial intelligence is used in the methodology should be written in detail. -/- Moreover, we encountered a much more exciting situation during our evaluation. In a few academic studies, we have seen that artificial intelligence is added as a co-author. On July 06, 2023, in the Web of Science, using the advanced search, we found four articles with the author name ChatGPT [1]. We have determined that ChatGPT is the author in one of these articles [2] and the Group Author in three [3-5]. -/- Lee [6] stated that although artificial intelligence tools are much more advanced than search engines, they cannot be an author regarding research ethics because they cannot take responsibility for what they write. Similarly, Goto and Katanoda [7] stated that it is the author’s responsibility to confirm that the texts written by ChatGPT are correct. However, Pourhoseingholi et al. [8] reported that keeping up with technology is inevitable. Additionally, they said that“this action will be more fruitful and practical in extended dimensions when international institutes like ICMJE or COPE come up with the appropriate adjustments and establish robust criteria to scheme the AI authorship”. -/- Most probably, the use of artificial intelligence applications in scientific articles and whether it can be a co-author in these papers will be discussed soon. -/- We encourage interested authors to submit their ideas to our journal as a letter to the editor. -/- Yours sincerely,. (shrink)

This study assessed external debts and the financing of education in Nigeria using time series data obtained from World Bank, and CBN Statistical Bulletin covering a period of 31 years from 1988 -2018. The model of the study was derived, while the data collected were analysed using the Ordinary Least Squares. Diagnostic tests such as Augmented Dickey- Fuller (ADF) unit root test, Johansen co-integration, Vector Error Correction (VEC) techniques of estimation, and Granger Causality tests were all performed. Findings revealed (...) a significant long-run relationship between external debts and the financing of education; external debts have a significant effect (F=39.07055, p<.05) on the financing of education in Nigeria; external debt stock and external debt service payment have no significant effect on the financing of education; real GDP and Exchange rate have a significant effect on the financing of education in Nigeria respectively. Based on these findings, it was concluded that external debt is a big hindrance to the financing of education and consequently, the economic growth of Nigeria. It was recommended amongst others that the government should use borrowed funds from external sources for productive capital projects or development initiatives such as investment in education and the eradication of illiteracy. (shrink)

Heart failure is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 918 samples with 11 features, such as age, sex, chest pain type, resting blood pressure, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and HeartDisease. A (...) neural network model with four layers (1 input and 1 output) was trained on the dataset and achieved an accuracy of 90% and an average error of 0.009. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

Introduction: The purpose of this study was to assess dental care professionals' drug prescription knowledge, practices, and reporting of adverse drug reactions (ADRs). -/- Methods: A cross-sectional exploratory study was conducted by using a face-to-face survey administered to a sample of dentists from tertiary care hospitals in Adana, Türkiye. A questionnaire consisted of six sections with closed-ended items including sociodemographic characteristics, knowledge about drugs, patient history information, counseling practices during prescribing, source of information and ADR reporting. -/- Results: The study (...) included 180 dentists, with a 95.3% response rate. More than half of the dentists (50.6%) stated their level of knowledge about drug price/cost as low. Most of the dentists (50.6%;n=91) claimed that they never/rarely asked about health insurance during patient history information. Moreover, most of the participants reported that they never/rarely and sometimes counsel the patients regarding drug mechanism of action, side effects of the drug, interaction of prescribed drug with other drugs/nutrients. It was determined that the dentists learned about the drugs from Vademecum (Turkish Medication Guidebook: 70%;n=126) and the internet (55%;n=99). A higher proportion (85.5%) of the dentists indicated that they did not report ADR during their clinical practice. -/- Conclusion: This study showed a general improvement in dentists prescribing knowledge and practices, although they reported some lack of knowledge regarding drug cost, discussion about the possible side effect of a drug/interaction with other drugs/nutrients with patients and under-reporting of ADRs. Periodic education and training for dentists are critical to overcoming any problem related to prescribing errors and potential ADRs. (shrink)

Aim: This research determined the relationship between university teachers’ professional identity and their work performance. -/- Methodology: The design that was used in this study is a Descriptive Comparative- Correlational research design using the quantitative approach. Participants in this study were taken from the 967 university teachers at Guangdong Business and Technology University from the 14 colleges. They were chosen randomly. The researcher used the Qualtrics calculator at a 5% margin of error to arrive at 275 respondents. Data analyses (...) include frequency count and percentage, weighted mean, and descriptive equivalent. A five-point scale was used in this study as a guide to answer the questionnaire. -/- Results: Based on the result, the highest manifestation of teachers’ professional identity was found to be on their professional behavior tendency which was ranked first among the four indicators. It was followed by professional values and professional belonging, while role values tend to be the least manifested professional identity based on the self -assessment of the teachers. The over-all mean value of 3.52 reveals that professional identity was highly manifested among the teachers of Guangdong Business and Technology University. Teacher respondents have manifested an outstanding work performance particularly on work attitude as this was given the highest rating by the teachers themselves. Second in rank was the interpersonal promotion, followed by completion of teaching activities which also shows an outstanding work performance. On the other hand, teachers’ work performance on student achievement was given the lowest assessment by the teachers showing a very satisfactory performance. Generally, the result reveals that teachers exhibited an outstanding work performance as perceived by themselves. Based on the result, there were no significant differences in the assessment of teacher respondents when they are grouped according to gender. This is taken to mean that male and female teachers have relatively the same assessment as regards their work performance in terms of work attitude, interpersonal promotion, completion of teaching activities, and student achievement. Generally, the result reveals that there was a significant relationship between the teachers’ professional identity and their work performance. The result reveals that professional identity of teachers has positive impact on their work performance. -/- Conclusion: Respondents are dominated by female teachers who are mostly in their middle age. A good number of teachers who have earned master’s and doctoral degrees are also evident. They perceived themselves to have a high level of professional identity specifically on professional behavior tendency, professional values, and professional belonging. Teachers have relatively the same assessments on their professional identity regardless of their gender, age, educational attainment, and monthly income. They exhibit an outstanding work performance particularly on work attitude, interpersonal promotion, and completion of teaching activities. However, they feel that they had less contribution to student achievement. Teachers have relatively the same assessments on their work performance regardless of their gender, age, educational attainment, and monthly income. Professional identity of teachers can somehow influence their work performance in general. Specifically, professional values and professional belonging give positive effect to work attitude of teachers, and to their completion of teaching activities. (shrink)

Continuous Stirred Tank Reactor (CSTR) is amajorarea in process, chemical and control engineering. In this paper, PID and MPC controllers are designed for CSTR in order to analyze the output concentration of the system by comparing the two proposed systems using Matlab/Simulink. Comparison have been made using two desired concentration input (Random reference and step) signals with and without input side disturbance (Flow rate error). The simulation result shows that the continuous stirred tank reactor with MPC controller have (...) better response in minimizing the overshoot and tracking the desired concentration for the system without input disturbance and with the effect of the disturbance makes the continuous stirred tank reactor with MPC controller output with small fluctuations and still better than the continuous stirred tank reactor with PID controller. Finally the comparative analysis and simulation results prove the effectiveness of the continuous stirred tank reactor with MPC controller. (shrink)

In this paper, a predictive artificial neural network (ANN) model was developed and validated for the purpose of prediction whether a watermelon is good or bad, the model was developed using JUSTNN software environment. Prediction is done based on some watermelon attributes that are chosen to be input data to the ANN. Attributes like color, density, sugar rate, and some others. The model went through multiple learning-validation cycles until the error is zero, so the model is 100% percent (...) accurate for the purpose of prediction whether good or bad the watermelon is. (shrink)

Abstract: This study tries to establish the microfinance and financial inclusion nexus in Nigeria from 1981 to 2017. The Augmented Dickey-Fuller (ADF) test, co-integration test and Error Correction Model (ECM), as well as diagnostics and stability test were employed in the analysis. The research findings revealed that microfinance has positive significant effect on financial inclusion in Nigeria in the short–run and long–run. This finding is in line CBN objectives for the establishment of microfinance banks. The effect of lending interest (...) rate has a positive but has no significant with financial inclusion in the model one while it is statistically significant with poverty in Nigeria in the mode two. The positive lending interest rate has a statistically significant effect on the level of financial inclusion and national poverty index used as a proxy for poverty rate in the long run in the models. Also, the research also found that microfinance has really a tool to fight against poverty in Nigeria in the short-run, while it’s not really a tool to fight against poverty in the long-run in Nigeria. It was therefore recommended that Government agency and regulatory authority policies and practices need to play a key role in making micro-credit available to the economically-active poor people who are not being served by the formal financial sector. Also, apart from monitoring lending interest rates, the government needs to establish frameworks to prevent undercapitalization, fraudulent practices, and unwarranted interference from bank board members in Nigeria. (shrink)

Horwich gives a fine analysis of Wittgenstein (W) and is a leading W scholar, but in my view, they all fall short of a full appreciation, as I explain at length in this review and many others. If one does not understand W (and preferably Searle also) then I don't see how one could have more than a superficial understanding of philosophy and of higher order thought and thus of all complex behavior (psychology, sociology, anthropology, history, literature, society). In a (...) nutshell, W demonstrated that when you have shown how a sentence is used in the context of interest, there is nothing more to say. I will start with a few notable quotes and then give what I think are the minimum considerations necessary to understand Wittgenstein, philosophy and human behavior. -/- First one might note that putting “meta” in front of any word should be suspect. W remarked e.g., that metamathematics is mathematics like any other. The notion that we can step outside philosophy (i.e., the descriptive psychology of higher order thought) is itself a profound confusion. Another irritation here (and throughout academic writing for the last 4 decades) is the constant reverse linguistic sexism of “her” and “hers” and “she” or “he/she” etc., where “they” and “theirs” and “them” would do nicely. Likewise, the use of the French word 'repertoire' where the English 'repertory' will do quite well. The major deficiency is the complete failure (though very common) to employ what I see as the hugely powerful and intuitive two systems view of HOT and Searle’s framework which I have outlined above. This is especially poignant in the chapter on meaning p111 et seq. (especially in footnotes 2-7), where we swim in very muddy water without the framework of automated true only S1, propositional dispositional S2, COS etc. One can also get a better view of the inner and the outer by reading e.g., Johnston or Budd (see my reviews). Horwich however makes many incisive comments. I especially liked his summary of the import of W’s anti-theoretical stance on p65. He needs to give more emphasis to ‘On Certainty’, recently the subject of much effort by Daniele Moyal- Sharrock, Coliva and others and summarized in my recent articles. -/- Horwich is first rate and his work well worth the effort. One hopes that he (and everyone) will study Searle and some modern psychology as well as Hutto, Read, Hutchinson, Stern, Moyal-Sharrock, Stroll, Hacker and Baker etc. to attain a broad modern view of behavior. Most of their papers are on academia dot edu and philpapers dot org , but for PMS Hacker see his papers on his Oxford page. -/- He gives one of the most beautiful summaries of where an understanding of Wittgenstein leaves us that I have ever seen. -/- “There must be no attempt to explain our linguistic/conceptual activity (PI 126) as in Frege’s reduction of arithmetic to logic; no attempt to give it epistemological foundations (PI 124) as in meaning based accounts of a priori knowledge; no attempt to characterize idealized forms of it (PI 130) as in sense logics; no attempt to reform it (PI 124, 132) as in Mackie’s error theory or Dummett’s intuitionism; no attempt to streamline it (PI 133) as in Quine’s account of existence; no attempt to make it more consistent (PI 132) as in Tarski’s response to the liar paradoxes; and no attempt to make it more complete (PI 133) as in the settling of questions of personal identity for bizarre hypothetical ‘teleportation’ scenarios.” -/- Finally, let me suggest that with the perspective I have encouraged here, W is at the center of contemporary philosophy and psychology and is not obscure, difficult or irrelevant, but scintillating, profound and crystal clear and that to miss him is to miss one of the greatest intellectual adventures possible. -/- Those wishing a comprehensive up to date framework for human behavior from the modern two systems view may consult my book ‘The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle’ 2nd ed (2019). Those interested in more of my writings may see ‘Talking Monkeys--Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet--Articles and Reviews 2006-2019 3rd ed (2019), The Logical Structure of Human Behavior (2019), and Suicidal Utopian Delusions in the 21st Century 4th ed (2019) . (shrink)

In hindsight, it is not surprising that the exegesis of Aristotle’s Sophistici elenchi developed into one of the most substantial parts of the Latin commentary tradition. To make a long story short, in its customary capacity as the art of arts and the science of sciences, medieval logic was primarily concerned with discerning the true from the false in arguments as they occur in natural, ordinary speech as opposed to the more formalised parlance later logicians will resort to. It makes (...) perfect sense, then, that medieval logicians paid special attention to everything that threatens sound reasoning and that prevents us from speaking the truth. Indeed, they were second to none and better than most at exposing and elucidating arguments’ flaws and shortcomings. After all, as John Buridan – faithful to a long and illustrious tradition – aptly put it, «rooting out errors» is logic’s first order of business. As early as the 1140s, Aristotle’s Sophistici elenchi provided the most fertile ground for such keen interest in fallacies; which, in turn, explains etc. This much is uncontroversial or, at any rate, can withstand any amount of scrutiny we care to throw at it. Courtesy – first and foremost – of Sten Ebbesen, whose long-standing interest in medieval writings on bad arguments has turned the Byzantine and Latin aftermath of Aristotle’s Sophistici elenchi into well-charted territory, by all standards. The Anonymus Cantabrigiensis has played no small part in shaping this picture. As a matter of fact, time and again over the last forty years or so, quotes and insights from the anonymous work have kept showing up in Ebbesen’s editions and studies: since he first discovered the commentary in the late 1970s and brought it to the general attention, Ebbesen has routinely drawn on the Anonymus as an early witness of the circulation of Aristotelian logical works and related texts, as a convenient illustration of major trends and distinctive features of the Latin literature on fallacies, and as a sensible interpreter in his own right. (shrink)

Metascientists have studied questionable research practices in science. The present article considers the parallel concept of questionable metascience practices (QMPs). A QMP is a research practice, assumption, or perspective that has been questioned by several commentators as being potentially problematic for metascience and/or the science reform movement. The present article reviews ten QMPs that relate to criticism, replication, bias, generalization, and the characterization of science. Specifically, the following QMPs are considered: (1) rejecting or ignoring self-criticism; (2) a fast ‘n’ bropen (...) scientific criticism style; (3) overplaying the role of replication in science; (4) assuming a replication rate is “too low” without specifying an “acceptable” rate; (5) an unacknowledged metabias towards explaining the replication crisis in terms of researcher bias; (6) assuming that researcher bias can be reduced; (7) devaluing exploratory results as being more “tentative” than confirmatory results; (8) presuming that questionable research practices are problematic research practices; (9) focusing on knowledge accumulation; and (10) focusing on specific scientific methods. It is stressed that only some metascientists engage in some QMPs some of the time, and that these QMPs may not always be problematic. Research is required to estimate the prevalence and impact of QMPs. In the meantime, QMPs should be viewed as invitations to ask questions about how we go about doing better metascience. (shrink)

En Electromecánicas IV, – Despliegue y activación de un espacio-tiempo barroco, Mónica Salinero socióloga y Renzo Filinich artista, se sumergen en el trabajo de análisis de la obra de Raúl Díaz, “Electromecánicas IV, poniendo en valor la diferencia conceptual y la diversidad del universo latinoamericano como espacio de creación situado. Inspirados en las teorías de Bolivar Echevarria, discuten la complejidad simbólica que rodea a esta experiencia estética, deteniéndose en los valores y funciones que se encuentran dentro del espectro cultural andino (...) que la contiene. El relato persigue desentrañar el armazón de pensamiento que alumbra el proceso de creación, cuando la puesta en marcha de dispositivos obsoletos, se vuelve metáfora de intervención local, sobre una reciclada y reconvertida tradición occidental, que representa la idea de Modernidad barroca. (shrink)

Moral error theory of the kind defended by J. L. Mackie and Richard Joyce is premised on two claims: (1) that moral judgements essentially presuppose that moral value has absolute authority, and (2) that this presupposition is false, because nothing has absolute authority. This paper accepts (2) but rejects (1). It is argued first that (1) is not the best explanation of the evidence from moral practice, and second that even if it were, the error theory would still (...) be mistaken, because the assumption does not contaminate the meaning or truth-conditions of moral claims. These are determined by the essential application conditions for moral concepts, which are relational rather than absolute. An analogy is drawn between moral judgements and motion judgements. (shrink)

In this paper I defend what I call the argument from epistemic reasons against the moral error theory. I argue that the moral error theory entails that there are no epistemic reasons for belief and that this is bad news for the moral error theory since, if there are no epistemic reasons for belief, no one knows anything. If no one knows anything, then no one knows that there is thought when they are thinking, and no one (...) knows that they do not know everything. And it could not be the case that we do not know that there is thought when we believe that there is thought and that we do not know that we do not know everything. I address several objections to the claim that the moral error theory entails that there are no epistemic reasons for belief. It might seem that arguing against the error theory on the grounds that it entails that no one knows anything is just providing a Moorean argument against the moral error theory. I show that even if my argument against the error theory is indeed a Moorean one, it avoids Streumer's, McPherson's and Olson's objections to previous Moorean arguments against the error theory and is a more powerful argument against the error theory than Moore's argument against external world skepticism is against external world skepticism. (shrink)

Moral error theories claim that (i) moral utterances express moral beliefs, that (ii) moral beliefs ascribe moral properties, and that (iii) moral properties are not instantiated. Thus, according to these views, there seems to be conclusive evidence against the truth of our ordinary moral beliefs. Furthermore, many error theorists claim that, even if we accepted moral error theory, we could still in principle keep our first-order moral beliefs. This chapter argues that this last claim makes many popular (...) versions of the moral error theory incompatible with the standard philosophical accounts of beliefs. Functionalism, normative theories of beliefs, representationalism, and interpretationalism all entail that being sensitive to thoughts about evidence is a constitutive feature of beliefs. Given that many moral error theorists deny that moral beliefs have this quality, their views are in a direct conflict with the most popular views about the nature of beliefs. (shrink)

Moral error theorists hold that morality is deeply mistaken, thus raising the question of whether and how moral judgments and utterances should continue to be employed. Proposals include simply abolishing morality, adopting some revisionary fictionalist stance toward morality, and conserving moral judgments and utterances unchanged. I defend a fourth proposal, namely revisionary moral expressivism, which recommends replacing cognitivist moral judgments and utterances with non-cognitivist ones. Given that non-cognitivist attitudes are not truth apt, revisionary expressivism does not involve moral (...) class='Hi'>error. Moreover, revisionary expressivism has the theoretical resources to retain many of the useful features of morality, such as moral motivation, moral disagreement, and moral reasoning. Revisionary expressivism fares better than the three major alternatives in both avoiding moral error and preserving these useful features of morality. I also show how this position differs from the “revolutionary expressivism” of Sebastian Köhler and Michael Ridge. (shrink)

Error theories about morality often take as their starting point the supposed queerness of morality, and those resisting these arguments often try to argue by analogy that morality is no more queer than other unproblematic subject matters. Here, error theory (as exemplified primarily by the work of Richard Joyce) is resisted first by arguing that it assumes a common, modern, and peculiarly social conception of morality. Then error theorists point out that the social nature of morality requires (...) one to act against one's self-interest while insisting on the categorical, inescapable, or overriding status of moral considerations: they argue that morality requires magic, then (rightly) claim that there is no such thing as magic. An alternate eudaimonist conception of morality is introduced which itself has an older provenance than the social point of view, dating to the ancient Greeks. Eudaimonism answers to the normative requirements of morality, yet does not require magic. Thus, the initial motivation for error theory is removed. (shrink)

Moral error theory is the doctrine that our first-order moral commitments are pervaded by systematic error. It has been objected that this makes the error theory itself a position in first-order moral theory that should be judged by the standards of competing first-order moral theories :87–139, 1996) and Kramer. Kramer: “the objectivity of ethics is itself an ethical matter that rests primarily on ethical considerations. It is not something that can adequately be contested or confirmed through non-ethical (...) reasoning” [2009, 1]). This paper shows that error theorists can resist this charge if they adopt a particular understanding of the presuppositions of moral discourse. (shrink)

This paper surveys contemporary accounts of error theory and fictionalism. It introduces these categories to those new to metaethics by beginning with moral nihilism, the view that nothing really is right or wrong. One main motivation is that the scientific worldview seems to have no place for rightness or wrongness. Within contemporary metaethics there is a family of theories that makes similar claims. These are the theories that are usually classified as forms of error theory or fictionalism though (...) there are different ways of accepting some form of the view that nothing is really write or wrong. A range of different ways of going in the light of such a realization is also proposed. The resulting taxonomy of positions is quite complicated and sometimes surprising. One surprise will be that some positions plausibly classified as error theories or forms of fictionalism do not quite seem to be forms of nihilism. (shrink)

Åsa Maria Wikforss has proposed a response to Burge's thought-experiments in favour of social externalism, one which allows the individualist to maintain that narrow content is truth-conditional without being idiosyncratic. The narrow aim of this paper is to show that Wikforss' argument against social externalism fails, and hence that the individualist position she endorses is inadequate. The more general aim is to attain clarity on the social externalist thesis. Social externalism need not rest, as is typically thought, on the possibility (...) of incomplete linguistic understanding or conceptual error. I identify the unifying principle that underlies the various externalist thought-experiments. (shrink)