This chapter contains sections titled: * The Universe We Know * Theories of Creation * Why This Universe? * The Multiverse and Fine-Tuning * Accounting for the World * God as a Theory * Note * References * Further Reading.

The main aim of this paper is to revisit the curve fitting problem using the reliability of inductive inference as a primary criterion for the ‘fittest' curve. Viewed from this perspective, it is argued that a crucial concern with the current framework for addressing the curve fitting problem is, on the one hand, the undue influence of the mathematical approximation perspective, and on the other, the insufficient attention paid to the statistical modeling aspects of the problem. (...) Using goodness-of-fit as the primary criterion for ‘best', the mathematical approximation perspective undermines the reliability of inference objective by giving rise to selection rules which pay insufficient attention to ‘accounting for the regularities in the data'. A more appropriate framework is offered by the error-statistical approach, where (i) statistical adequacy provides the criterion for assessing when a curve captures the regularities in the data adequately, and (ii) the relevant error probabilities can be used to assess the reliability of inductive inference. Broadly speaking, the fittest curve (statistically adequate) is not determined by the smallness if its residuals, tempered by simplicity or other pragmatic criteria, but by the nonsystematic (e.g. white noise) nature of its residuals. The advocated error-statistical arguments are illustrated by comparing the Kepler and Ptolemaic models on empirical grounds. ‡I am grateful to Deborah Mayo and Clark Glymour for many valuable suggestions and comments on an earlier draft of the paper; estimating the Ptolemaic model was the result of Glymour's prompting and encouragement. †To contact the author, please write to: Department of Economics, Virginia Tech 3019 Pamplin Hall (0316), Blacksburg, VA 24061; e-mail: [email protected]. (shrink)

The concepts of adaptation and fitness have such an appeal that they have been used in other scientific domains, including the social sciences. One particular aspect of this theory transfer concerns the so-called fitness landscape models. At first sight, fitness landscapes visualize how an agent, of any kind, relates to its environment, how its position is conditional because of the mutual interaction with other agents, and the potential routes towards improved fitness. The allure of fitness landscapes is first and (...) foremost that it represents a complex story about adaptation and fitness in one coherent image. Different accounts of fitness landscapes in different domains in the social sciences suggest that the properties and functions of fitness landscapes are attributed rather freely. These differences are testimony of the model’s versatility. At the same time, one will notice that the different approaches can also create ambiguity about the exact meaning and role of fitness landscapes in the social sciences. This article presents an extensive literature survey of the diverging interpretations and uses of fitness landscapes in the social sciences and discusses the implications in terms of how these models inform scientific inquiry. (shrink)

Aris Spanos and Deborah Mayo’s error-statistical approach to statistical modeling and inference adopts the reliability of inductive inference as a primary criterion for statistical model and estimator selection (e.g., curve fitting). In this paper, we expand the error-statistical approach’s adoption of reliable inductive inference by scrutinizing the epistemic legitimacy of contemporary techniques leveraged in data science. We argue that data validation and testing potentially provides a direct, measurable method of evaluating evidence for reliable inductive inferences in cases (...) where the error-statistical approach is not easily applied, and conclude with an exploration of core methodological foils to the reliability of inductive inference revealed by this argument. (shrink)

This article investigates the potential of fMRI to test assumptions about different components in models of complex cognitive tasks. If the components of a model can be associated with specific brain regions, one can make predictions for the temporal course of the BOLD response in these regions. An event‐locked procedure is described for dealing with temporal variability and bringing model runs and individual data trials into alignment. Statistical methods for testing the model are described that deal (...) class='Hi'>with the scan‐to‐scan correlations in the errors of measurement of the BOLD signal. This approach is illustrated using a “sacrificial” ACT‐R model that involves mapping 6 modules onto 6 brain regions in an experiment from concerned with equation solving. The model's visual encoding predicted the BOLD response in the fusiform gyrus, its controlled retrieval predicted the BOLD response in the lateral inferior prefrontal cortex, and its subgoal setting predicted the BOLD response in the anterior cingulate cortex. On the other hand, its motor programming failed to predict anticipatory activation in the motor cortex, its representational changes failed to predicted the pattern of activity in the posterior parietal cortex, and its procedural component failed to predict an initial spike in caudate. The results illustrate the power of such data to direct the development of a theory of complex problem solving, both at the level of a specific task model as well as at the level of the cognitive architecture. (shrink)

Though rapid collection of big data is ubiquitous across domains, from industry settings to academic contexts, the ethics of big data collection and research are contested. A nexus of data ethics issues is the concept of creep, or repurposing of data for other applications or research beyond the conditions of original collection. Data creep has proven controversial and has prompted concerns about the scope of ethical oversight. Institutional review boards offer little guidance regarding big (...) class='Hi'>data, and problematic research can still meet ethical standards. While ethics seem concrete through institutional deployment, I frame ethics as produced. Informed by my ethnographic research at a large public university in the U.S., I explore ethics through two models: ethics as institutional procedures and ethics as acts and intentions. The university where I conducted fieldwork is the development grounds for a predictive model that uses student data to anticipate academic success. While students consent to data collection, the circumstances of consent and the degree to which they are informed are not so apparent, as many data are a product of creep. Drawing from interviews and participant observation with administrators, data scientists, developers, and students, I examine data ethics, from a larger institutional model to everyday enactments related to data creep. After demonstrating the limits of such models, I propose a remodeling of ethics that draws on recent works on data, justice, and refusal to pose generative questions for rethinking ethics in institutional contexts. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Feminist Studies 45, no. 1. © 2019 by Feminist Studies, Inc. 129 Miriam Kienle Dear Data: Feminist Information Design’s Resistance to Self-Quantification Every Sunday for one year, information designers Giorgia Lupi and Stefanie Posavec sent each other a hand-drawn postcard that featured a data visualization of their week as it pertained to a single aspect of their daily lives: doors opened, clocks checks, sounds heard, smells perceived, (...) and so on (figs. 1–4). With this series of postcards exchanged between Brooklyn and London, Lupi and Posavec gained intimate knowledge of one another through their small/slow data, and at the same time, they produced a critical examination of the capture, interpretation, and visualization of their daily data from a uniquely feminist perspective. Although the field of information design prizes clear, efficient, and seamless presentations of quantified activity, Lupi and Posavec’s Dear Data (2015) is comprised of visualizations that underscore complexity over clarity, present questions rather simply display information, and expose the instability of data itself.1 Against the quantification of embodied experience into seemingly objective and all-encompassing datasets, Dear Data instead looks at data from a feminist perspective, as tied to concrete bodies, localities, and temporalities and visualized in a manner 1. The approach to infographics that prioritizes efficiency, accuracy, and elegance, and thus suppresses the constructed and interpretative nature of both data and its visualization, is perhaps most attributed to Edward Tufte. For Tufte’s aims and methods, see Edward R. Tufte, The Visual Display of Quantitative Information (Cheshire, CT: Graphics Press, 2001). 130 Miriam Kienle that is incomplete, contingent, and constructed.2 This paper will analyze Lupi and Posavec’s designs as providing a model for a feminist approach to data visualization—a model imperative in an age in which the quantitative information (much like a binary understanding of gender) is too often treated as a self-evident fact rather than what feminist philosopher Donna Haraway famously called “situated knowledges.”3 Recently acquired by MoMA’s architecture and design collection, the Dear Data project not only stands out in the collection as the product of two female designers working in the male-dominated fields of technology and design, but also as a project that stresses feminist knowledge production by underscoring how data voids shape datasets and debunking the myth that the world is data that can be captured and visualized with neutral, distanced, and all-encompassing technologies of vision. As Catherine D’Ignazio and Lauren Klein describe in Data Feminism, data visualization from a feminist perspective understands that “bar charts might seem neutral and objective, but are in fact the result of very human and necessarily imperfect design processes.”4 Such a perspective is evident across Dear Data. Take, for example, “Week 42: A week of laughter” (fig. 5). On the key in Posavec’s visualization, she notes: “I tried to capture my laughs which was really hard + got in the way of the enjoying life, hence the data voids. / For a card about laughter I am sad about how this card turned out.”5 This willingness to show the gaps in her data, the complexities of her process, and the imperfections of her visualizations contrasts with the seamless and straightforward displays of information 2. For a feminist approach to data visualization, see Catherine D’Ignazio and Lauren Klein, Data Feminism (Cambridge, MA: MIT Press, 2019), ebook; Catherine D’Ignazio, “What Would Feminist Data Visualization Look Like?” Center for Civic Media. Massachusetts Institute of Technology (blog), December 20, 2015, https://civic.mit.edu/feminist-data-visualization; and Johanna Drucker, Graphesis: Visual Forms of Knowledge Production (Cambridge, MA: Harvard University Press, 2014). 3. Donna Haraway, “Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective,” Feminist Studies 14, no. 3 (1988): 575–99. 4. D’Ignazio and Klein, introduction to their Data Feminism. 5. Giorgia Lupi and Stefanie Posavec, Dear Data (New York: Princeton Architectural Press, 2016), 231. Miriam Kienle 131 or what infographic guru Edward Tufte describes as “Designs so good that they are invisible.”6 Counter to the invisible and seemingly objective visualizations of our personal data to which we have become accustomed (on calorie counters, credit card statements, etc... (shrink)

Plamondon's kinematic theory is very powerful from a descriptive point of view. Unfortunately, the fact that it neglects some fundamental features of the motor system, such as nonlinear inertial torque interactions or joint redundancies, limits its explanatory power and biological validity. As a consequence, the data presented by Plamondon & Alimi should be analyzed and interpreted with caution. There appears to be a gap between the observations reported by the authors and some of the conclusions they draw.

A number of recent models of semantics combine linguistic information, derived from text corpora, and visual information, derived from image collections, demonstrating that the resulting multimodal models are better than either of their unimodal counterparts, in accounting for behavioral data. Empirical work on semantic processing has shown that emotion also plays an important role especially in abstract concepts; however, models integrating emotion along with linguistic and visual information are lacking. Here, we first improve on visual and affective representations, (...) derived from state‐of‐the‐art existing models, by choosing models that best fit available human semantic data and extending the number of concepts they cover. Crucially then, we assess whether adding affective representations (obtained from a neural network model designed to predict emojis from co‐occurring text) improves the model's ability to fit semantic similarity/relatedness judgments from a purely linguistic and linguistic–visual model. We find that, given specific weights assigned to the models, adding both visual and affective representations improves performance, with visual representations providing an improvement especially for more concrete words, and affective representations improving especially the fit for more abstract words. (shrink)

Cramer et al.'s account of comorbidity comes with a substantive philosophical view concerning the nature of psychological disorders. Although the network account is responsive to problems with extant approaches, it faces several practical and conceptual challenges of its own, especially in cases where the individual differences in network structures require the analysis of intra-individual time-series data.

This paper examines one of the historical antecedents of Big Data, the social physics movement. Its origins are in the scientific revolution of the 17th century in Western Europe. But it is not named as such until the middle of the 19th century, and not formally institutionalized until another hundred years later when it is associated with work by George Zipf and John Stewart. Social physics is marked by the belief that large-scale statistical measurement of social variables reveals (...) underlying relational patterns that can be explained by theories and laws found in natural science, and physics in particular. This larger epistemological position is known as monism, the idea that there is only one set of principles that applies to the explanation of both natural and social worlds. Social physics entered geography through the work of the mid-20th-century geographer William Warntz, who developed his own spatial version called “macrogeography.” It involved the computation of large data sets, made ever easier with the contemporaneous development of the computer, joined with the gravitational potential model. Our argument is that Warntz's concerns with numeracy, large data sets, machine-based computing power, relatively simple mathematical formulas drawn from natural science, and an isomorphism between natural and social worlds became grounds on which Big Data later staked its claim to knowledge; it is a past that has not yet passed. (shrink)

Order of information plays a crucial role in the process of updating beliefs across time. In fact, the presence of order effects makes a classical or Bayesian approach to inference difficult. As a result, the existing models of inference, such as the belief-adjustment model, merely provide an ad hoc explanation for these effects. We postulate a quantum inference model for order effects based on the axiomatic principles of quantum probability theory. The quantum inference model explains order effects by transforming a (...) state vector with different sequences of operators for different orderings of information. We demonstrate this process by fitting the quantum model to data collected in a medical diagnostic task and a jury decision-making task. To further test the quantum inference model, a new jury decision-making experiment is developed. Using the results of this experiment, we compare the quantum inference model with two versions of the belief-adjustment model, the adding model and the averaging model. We show that both the quantum model and the adding model provide good fits to the data. To distinguish the quantum model from the adding model, we develop a new experiment involving extreme evidence. The results from this new experiment suggest that the adding model faces limitations when accounting for tasks involving extreme evidence, whereas the quantum inference model does not. Ultimately, we argue that the quantum model provides a more coherent account for order effects that was not possible before. (shrink)

This dissertation investigates some philosophical issues using computational models. Chapter 1 presents a Lewis-Skyrms signaling game that can exhibit a type of compositionality novel to the signaling game literature. The structure of the signaling game is motivated by an analogy to the alarm calls of putty-nosed monkeys (Cercopithecus nictitans). Putty-nosed monkeys display a compositional system of alarm calls with a semantics that is sensitive to the ordering of terms. This sensitivity to the ordering of terms has not been previously (...) modeled with a Lewis-Skyrms signaling game literature. Signaling games are valued for showing how communicative systems can arise with minimal learning tools. Simulation results show that basic (Roth-Erev) reinforcement learning is sufficient for the acquisition of a compositional signaling system sensitive to the ordering of terms. Chapter 2 investigates social epistemology in the context of an effect of cognitive biases called the illusory truth effect. The illusory truth effect is exhibited when repeated exposure to a statement increases an individual's credence in that statement. While most investigations of the illusory truth effect focus on individuals' belief formation, humans typically form beliefs within a social structure. This is particularly relevant because various social structures can give rise to repeated exposure to statements; e.g. a popular book might recurringly be discussed in one's social circle, or one or two foundational papers might always be cited by a particular lab. So, how does the illusory truth effect influence learning and belief formation in a group? This chapter uses network models to investigate this question. These models show that the illusory truth effect can be very detrimental to a group's belief formation. The effect causes networks to prematurely settle on a belief, in part, through repeated exposure to data points that are not independently generated. Previous research has indicated that the probability of such failures is near zero when networks are large or scientists are forced to explore unpopular and risky science. However, simulation results show that the harmful consequences of the illusory truth effect are robust even in large networks with mandatory exploration. Finally, Chapter 3 shows how a rudimentary type of abstraction can obtain in Lewis-Skyrms signaling games. Here, abstraction is understood as occurring when different particulars take on the same functional role. Some abstraction may be guided by innate biases, and the chapter develops an analogy of reasoning about strategic thinking in chess to highlight some epistemic concerns that are raised by the presentation of abstraction. Concretely, the signaling game models of this chapter are developed in the context of two tasks that are much simpler than strategizing in chess. The first task is a transitive inference task that has been substantially studied in both humans and non-human animals. General features of the models developed for the transitive inference task are then carried over to a model for the second task of learning rudimentary grammatical structures. This second task is based on studies of human infants and non-human primates' ability to learn ``nonsense grammars". Closing discussion highlights some strengths of the abstraction exhibited in the Lewis-Skyrms signaling game models. (shrink)

Belief polarization is said to occur when two people respond to the same evidence by updating their beliefs in opposite directions. This response is considered to be “irrational” because it involves contrary updating, a form of belief updating that appears to violate normatively optimal responding, as for example dictated by Bayes' theorem. In light of much evidence that people are capable of normatively optimal behavior, belief polarization presents a puzzling exception. We show that Bayesian networks, or Bayes nets, can simulate (...) rational belief updating. When fit to experimental data, Bayes nets can help identify the factors that contribute to polarization. We present a study into belief updating concerning the reality of climate change in response to information about the scientific consensus on anthropogenic global warming. The study used representative samples of Australian and U.S. participants. Among Australians, consensus information partially neutralized the influence of worldview, with free-market supporters showing a greater increase in acceptance of human-caused global warming relative to free-market opponents. In contrast, while consensus information overall had a positive effect on perceived consensus among U.S. participants, there was a reduction in perceived consensus and acceptance of human-caused global warming for strong supporters of unregulated free markets. Fitting a Bayes net model to the data indicated that under a Bayesian framework, free-market support is a significant driver of beliefs about climate change and trust in climate scientists. Further, active distrust of climate scientists among a small number of U.S. conservatives drives contrary updating in response to consensus information among this particular group. (shrink)

The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision-making, and motor control. This article provides an up-to-date introduction to the two most influential theories within this framework: predictive coding and active inference. The first half of the paper (Sections 2–5) reviews the evolution of predictive coding, from early ideas about efficient coding in the visual system (...) to a more general model encompassing perception, cognition, and motor control. The theory is characterized in terms of the claims it makes at Marr's computational, algorithmic, and implementation levels of description, and the conceptual and mathematical connections between predictive coding, Bayesian inference, and variational free energy (a quantity jointly evaluating model accuracy and complexity) are explored. The second half of the paper (Sections 6–8) turns to recent theories of active inference. Like predictive coding, active inference models assume that perceptual and learning processes minimize variational free energy as a means of approximating Bayesian inference in a biologically plausible manner. However, these models focus primarily on planning and decision-making processes that predictive coding models were not developed to address. Under active inference, an agent evaluates potential plans (action sequences) based on their expected free energy (a quantity that combines anticipated reward and information gain). The agent is assumed to represent the world as a partially observable Markov decision process with discrete time and discrete states. Current research applications of active inference models are described, including a range of simulation work, as well as studies fitting models to empirical data. The paper concludes by considering future research directions that will be important for further development of both models. (shrink)

Climate model projections are used to inform policy decisions and constitute a major focus of climate research. Confidence in climate projections relies on the adequacy of climate models for those projections. The question of how to argue for the adequacy of models for climate projections has not gotten sufficient attention in the climate modelling community. The most common way to evaluate a climate model is to assess in a quantitative way degrees of “model fit”; i.e., how well model results fit (...) observation-based data (empirical accuracy) and agree with other models or model versions (robustness). However, such assessments are largely silent about what those degrees of fit imply for a model’s adequacy for projecting future climate. We provide a conceptual framework for discussing the evaluation of the adequacy of models for climate projections. Drawing on literature from philosophy of science and climate science, we discuss the potential and limits of inferences from model fit. We suggest that support of a model by background knowledge is an additional consideration that can be appealed to in arguments for a model’s adequacy for long-term projections, and that this should explicitly be spelled out. Empirical accuracy, robustness and support by background knowledge neither individually nor collectively constitute sufficient conditions in a strict sense for a model’s adequacy for long-term projections. However, they provide reasons that can be strengthened by additional information and thus contribute to a complex non-deductive argument for the adequacy of a climate model or a family of models for long-term climate projections. (shrink)

Purpose Existing algorithms for predicting suicide risk rely solely on data from electronic health records, but such models could be improved through the incorporation of publicly available socioeconomic data – such as financial, legal, life event and sociodemographic data. The purpose of this study is to understand the complex ethical and privacy implications of incorporating sociodemographic data within the health context. This paper presents results from a survey exploring what the general public’s knowledge and concerns are (...) about such publicly available data and the appropriateness of using it in suicide risk prediction algorithms. Design/methodology/approach A survey was developed to measure public opinion about privacy concerns with using socioeconomic data across different contexts. This paper presented respondents with multiple vignettes that described scenarios situated in medical, private business and social media contexts, and asked participants to rate their level of concern over the context and what factor contributed most to their level of concern. Specific to suicide prediction, this paper presented respondents with various data attributes that could potentially be used in the context of a suicide risk algorithm and asked participants to rate how concerned they would be if each attribute was used for this purpose. Findings The authors found considerable concern across the various contexts represented in their vignettes, with greatest concern in vignettes that focused on the use of personal information within the medical context. Specific to the question of incorporating socioeconomic data within suicide risk prediction models, the results of this study show a clear concern from all participants in data attributes related to income, crime and court records, and assets. Data about one’s household were also particularly concerns for the respondents, suggesting that even if one might be comfortable with their own being used for risk modeling, data about other household members is more problematic. Originality/value Previous studies on the privacy concerns that arise when integrating data pertaining to various contexts of people’s lives into algorithmic and related computational models have approached these questions from individual contexts. This study differs in that it captured the variation in privacy concerns across multiple contexts. Also, this study specifically assessed the ethical concerns related to a suicide prediction model and determining people’s awareness of the publicness of select data attributes, as well as which of these data attributes generated the most concern in such a context. To the best of the authors’ knowledge, this is the first study to pursue this question. (shrink)

Advances in science and technology has led to the rise of different issues in relation to human life and security as well as the environment. These issues also paved the way for the field of Bioethics with its principles aiming to uphold moral standards on these issues. This study aimed to test and modify the theoretical models of the factors influencing the conviction schemas of BS Biology Bioethics students of a state university toward bioethical issues. One hundred ten (110) (...) undergraduate students were pre-tested and post-tested for comprehension of Bioethics principles, moral disengagement, and convictions toward bioethical issues. Identified personal factors were also retrieved for testing. Results showed that the Bioethics course has increased the participants’ comprehension of Bioethics principles, moral disengagement, and convictions. Stepwise multiple regression results revealed that anxiety, extraversion, exposure to media, and moral disengagement can predict pre-convictions. On the other hand, only moral disengagement was found to predict post-convictions. Furthermore, moral disengagement can be predicted by convictions before the course while anxiety, openness to change, and convictions were the predictors after the course. The proposed theoretical model was for tested goodness of fit using Structural Equation Modeling (SEM) and acceptance was based on the model fit criteria. Since the proposed model was not found to fit the data, this was revised based on the results of multiple regression and correlation among exogenous variables. Three models were constructed and found to fit the data adequately. These models show the direct and indirect effects of personal factors and moral disengagement on convictions. The mediating effects of moral disengagement and convictions to each other were also shown. Findings further imply that the ability to understand the principles does not influence convictions, but rather moral disengagement has a greater influence on the said factor. Improving moral education by integrating Bioethics to the curriculum is recommended to further strengthen the moral judgement and reasoning of students. (shrink)

Unlike most other sciences, psychology has no true core theory to guide a coherent research programme. It does have James J Gibson’s ecological approach to visual perception, however, which we suggest should serve as an example of the benefits a good theory brings to psychological research. Here we focus on an example of how the ecological approach has served as a guide to discovery, shaping and constraining a recent hypothesis about how humans perform coordinated rhythmic movements (Bingham 2004a, b). Early (...) experiments on this task were framed in a dynamic pattern approach. This phenomenological, behavioural framework (e.g. Jeka & Kelso 1989) classifies the behaviour of complex action systems in terms of the key order parameters, and describes the dynamical stability of the system as it responds to perturbations. Dynamical systems, however, while a valuable toolkit, is not a theory of behaviour, and this style of research is unable to successfully predict data it is not explicitly designed to fit. More recent work by Bingham & colleagues has used dynamical systems to formalise hypotheses derived from Gibson’s ecological approach to perception and action, with a particular emphasis on perceptual information. The resulting model (Bingham 2001, 2004a, b; Snapp-Childs et al. 2011) has had great success with both the phenomena it was designed to explain as well as a wide range of empirical results from a version of the task it is not specifically designed to explain (specifically, learning a novel coordination). This model and the research programme that produced it stand as an example of the value of theory driven research, and we use it to illustrate the contemporary importance the ecological approach has for psychology. (shrink)

Determinist thought with its sui generis view on life, nature and being as a whole is a point of view that could be observed in many different cultures and beliefs. It was thanks to Greek thought that it ceased to be a cultural element and transformed into a systematic cosmology. Schools such as Leucippos, then Democritos and Stoa attempted to integrate the determinist philosophy into ontology and cosmology. In the course of time, physics and metaphysics-based determinism approaches were introduced, (...) and a wide spectrum has emerged from genetics to behaviorism, from culture to psychology, from atomism to divinity. Determinism, whether physical or metaphysical origin, ignores freedom of will and deny agency competence over human behavior. Due to this feature, it is opposed by ethical theories, legal philosophies and religions. When the arguments for determinism are possible to disprove logically and philosophically, this is not so easy when the claims base themselves on scientific base. As the logical consequence of this novel fact, it is important to question the epistemological value of scientific knowledge and critically analyze the data of experiments and clinical studies in terms of methodology when answering any hypothesis such as neuro-biological, neuro-psychological, or neuro-theological argumentations. On the other hand, it is necessary to reach alternative experiments on the same subject or to show that it is possible to interpret them in different ways, as in the example of the Libet experiment. The neurobiological determinism based on biological reductionism, emerged as the result of ignoring the ontological difference of the mind, equating the mind with the brain, or defining mentality as an epiphenomenon of the brain. The main divergence stems from the acceptance of materialist philosophy. Paradigms that reduce existence only to matter do not take the metaphysical possibilities into account. In this case, although the problems called qualia or subjective experience stand there unresolved, the claim is preserved. Although many new developments such as the discovery of the entropy law, the big bang theory, and quantum physics have shaken the materialist philosophy, modern science resists an epistemological revision. However, with the current epistemology, it does not seem possible to grasp both the universe and the being as a whole. This is the most important reason why every attempt to understand the nature and spiritual side of human beings fails despite numerous experiments on the brain. However, the human brain is an organ that seems sophisticated and its secrets cannot be easily solved. Since there is not enough information about the brain, there is not enough evidence to claim that the brain, and therefore the mind, are part of the deterministic universe. Increasing brain researches since the last quarter of the twentieth century has revealed that genes, neurons and particularly brain chemistry claim much greater role than expected in human behavior. These results have been instrumentalized by some researchers for a radical claim that free will is an illusion, while by others it has led to the acceptance of will as less important over actions than was thought. Both ideas adopted a model of biological determinism in which freedom of will was ignored. Hypotheses based on controversial interpretations of various experiments raised concerns about religious, moral, and legal responsibility. Although man cannot be thought of as a non-free being from the point of view of common sense, the smog screen over the truth would not be lifted unless the scientific data in question had a correct interpretation. At first glance, it seems unreasonable to discuss universal determinism in the quantum universe. Nevertheless, the claim that strong causality prevails in the material world, including consciousness, remains in place. Therefore, firstly criticizing determinism and then examining biological determinism will help to fix the conceptual framework. This article aims to explain why genetics and neurobiology, while acknowledging their importance in the realization of behavior, can’t completely abolish moral agency. Some hypotheses attribute consciousness and free will to the physico-shimic structure of the brain, the random activity of neurons, or the ability to automatically transform inputs into outputs with the advantage of genetic information. This is the most sensitive and important point of being human; it attacks free will. Paradoxically, while global civilization of our time promises infinite freedom to human beings the science of the same age tries to prove that it is not free. (shrink)

Every project in digital and computational history of science starts with the collection of data. Depending on the research project, the subject of study, and other factors, data can comprise a variety of different types, including full texts, images, audio, video, and bibliographic metadata. Publications and project reports generally describe their results and the methods and algorithms employed, but few discuss the challenges of the initial data collection process or how it fits into the overall research (...) data life cycle. This essay discusses a concrete research data life cycle and takes a look at the difficulties it involves. It also explores the strategies and challenges of data collection and the question of the comparability of datasets. (shrink)

Deep Neural Networks (DNNs) are becoming increasingly important as scientific tools, as they excel in various scientific applications beyond what was considered possible. Yet from a certain vantage point, they are nothing but parametrized functions fθ(x)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{f}_{\varvec{\theta }}(\varvec{x})$$\end{document} of some data vector x\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{x}$$\end{document}, and their ‘learning’ is nothing but an iterative, algorithmic fitting of the parameters to data. Hence, what could (...) be special about them as a scientific tool or model? I will here suggest an integrated perspective that mediates between extremes, by arguing that what makes DNNs in science special is their ability to develop functional concept proxies (FCPs): Substructures that occasionally provide them with abilities that correspond to those facilitated by concepts in human reasoning. Furthermore, I will argue that this introduces a problem that has so far barely been recognized by practitioners and philosophers alike: That DNNs may succeed on some vast and unwieldy data sets because they develop FCPs for features that are not transparent to human researchers. The resulting breach between scientific success and human understanding I call the ‘Actually Smart Hans Problem’. (shrink)

The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields’ methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future (...) performance based on learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can be enhanced by incorporating insights from a cognitive model of human memory. This was done by exploiting the predictive performance equation's (PPE) narrow but highly specialized domain knowledge with regard to the temporal dynamics of learning and forgetting. Specifically, the PPE was used to engineer timing-related input features for a gradient-boosted decision trees (GBDT) model. The resulting PPE-enhanced GBDT outperformed the default GBDT, especially under conditions in which limited data were available for training. Results suggest that integrating cognitive and ML models could be particularly productive if the available data are too high-dimensional to be explained by a cognitive model but not sufficiently large to effectively train a modern ML algorithm. Here, the cognitive model's insights pertaining to only one aspect of the data were enough to jump-start the ML model's ability to make predictions—a finding that holds promise for future explorations. (shrink)

“The Strategy of Model Building in Population Biology” published by Richard Levins in 1966 has been cited over 2500 times. For a paper concerned with modeling approaches in population biology a surprisingly large part of the attention. The Strategy received comes from history and philosophy of biology, and specifically from accounts on model and model formulation. The Strategy is an unusual paper; it presents neither new data nor a new formal model; at times it reads like a manifesto (...) for some modeling approach, without specifying which broader program that approach intends to support. When these peculiarities of The Strategy are even mentioned, the philosophical literature tends to explain them away by invoking Levins’ Marxist commitments. In contrast, I argue that those peculiarities can be explained by examining the programmatic purpose of the paper; starting from his doctoral work, Levins was trying to establish a research program meant to account for the relations between fitness and environment in different terms than the prevalent lock-and-key view. My paper brings that program back to the discussion, explains its relation to competing approaches and examines Levins’ approach to modeling in light of that context. (shrink)

This paper takes a critical look at theory-free, statistical methodologies for processing and interpreting data taken from respondents answering a set of dichotomous (yes-no) questions. The basic issue concerns to what extent theoretical conclusions based on such analyses are invariant under a class of "informationally equivalent" question transformations. First the notion of Boolean equivalence of two question sets is discussed. Then Lazarsfeld's latent structure analysis is considered in detail. It is discovered that the best fitting latent model depends (...) on which one of the many informationally equivalent question sets is used. This fact raises a number of methodological problems and pitfalls with latent structure analysis. Related problems with other methodologies are briefly discussed. (shrink)

This paper is concerned with the multivariate stochastic volatility modeling of the stock market. We investigate a DGC-t-MSV model to find the historical volatility spillovers between nine markets, including S&P, Nasdaq, SSE, SZSE, HSI, FTSE, CAC, DAX, and Nikkei indices. We use the Bayesian network to analyze the spreading of herd behavior between nine markets. The main results are as follows: the DGC-t-MSV model we considered is a useful way to estimate the parameter and fit the data well (...) in the stock market; our computational analysis shows that the S&P and Nasdaq have higher volatility spillovers to the Shanghai and Shenzhen stock markets; the results also show that there is a strong correlation between stock markets in the same region. (shrink)

Objective: The main purpose of this study was the psychometric assessment of Jacelon’s Attributed Dignity Scale among Iranian older population. Methods: Using a standard “forward-backward” translation procedure, the original English version of Jacelon’s Attributed Dignity Scale was translated into Persian. Internal consistency of the scale was checked by the Cronbach’s α coefficient. Convergent validity of the instrument was appraised by the Social Skills Scale and General Health Questionnaire. Factor structure of the Iranian version of Jacelon’s Attributed Dignity Scale and possible (...) interplay between its subscales were checked through recruiting a convenient sample of 300 Iranian older people and performing the confirmatory factor analysis. Findings: The estimated Cronbach’s α and intraclass correlation coefficients for the Iranian version of Jacelon’s Attributed Dignity Scale were in the vicinity of acceptable range, that is, 0.87 and 0.93, respectively. The output of confirmatory factor analysis revealed that a four-factor model best fitted the study data (χ2 = 323.49; df = 129; p < 0.001; comparative fit index = 0.913; Tucker–Lewis index = 0.901; root mean square error approximation = 0.074; standardized root mean square residual = 0.078). Rasch estimates of item difficulty ranged from –1.28 (less difficult) to 1.33 (more difficult). No significant cross-gender differences were observed regarding the Iranian version of Jacelon’s Attributed Dignity Scale’s items indicating its invariant psychometric properties for use in the Iranian men and women subgroups. Ethical Considertaion: This study was approved by the Ethics Committee at the Tabriz university of medical science. Informed consent, information confidentiality, and voluntary participation were guaranteed. Conclusion: The study findings were indicative of applicability of the Iranian version of Jacelon’s Attributed Dignity Scale as a reliable tool in measurement of the perceived social dignity among Iranian and probably other Persian-speaking older populations. (shrink)

Table of contentsI1 Proceedings of the 4th World Conference on Research IntegrityConcurrent Sessions:1. Countries' systems and policies to foster research integrityCS01.1 Second time around: Implementing and embedding a review of responsible conduct of research policy and practice in an Australian research-intensive universitySusan Patricia O'BrienCS01.2 Measures to promote research integrity in a university: the case of an Asian universityDanny Chan, Frederick Leung2. Examples of research integrity education programmes in different countriesCS02.1 Development of a state-run “cyber education program of research ethics” in (...) KoreaEun Jung Ko, Jin Sun Kwak, TaeHwan Gwon, Ji Min Lee, Min-Ho LeeCS02.3 Responsible conduct of research teachers’ training courses in Germany: keeping on drilling through hard boards for more RCR teachersHelga Nolte, Michael Gommel, Gerlinde Sponholz3. The research environment and policies to encourage research integrityCS03.1 Challenges and best practices in research integrity: bridging the gap between policy and practiceYordanka Krastev, Yamini Sandiran, Julia Connell, Nicky SolomonCS03.2 The Slovenian initiative for better research: from national activities to global reflectionsUrsa Opara Krasovec, Renata SribarCS03.3 Organizational climate assessments to support research integrity: background of the Survey of Organizational Research Climate and the experience with its use at Michigan State UniversityBrian C. Martinson, Carol R. Thrush, C.K. Gunsalus4. Expressions of concern and retractionsCS04.1 Proposed guidelines for retraction notices and their disseminationIvan Oransky, Adam MarcusCS04.2 Watching retractions: analysis of process and practice, with data from the Wiley retraction archivesChris Graf, Verity Warne, Edward Wates, Sue JoshuaCS04.3 An exploratory content analysis of Expressions of ConcernMiguel RoigCS04.4 An ethics researcher in the retraction processMichael Mumford5. Funders' role in fostering research integrityCS05.1 The Fonds de Recherche du Québec’s institutional rules on the responsible conduct of research: introspection in the funding agency activitiesMylène Deschênes, Catherine Olivier, Raphaëlle Dupras-LeducCS05.2 U.S. Public Health Service funds in an international setting: research integrity and complianceZoë Hammatt, Raju Tamot, Robin Parker, Cynthia Ricard, Loc Nguyen-Khoa, Sandra TitusCS05.3 Analyzing decision making of funders of public research as a case of information asymmetryKarsten Klint JensenCS05.4 Research integrity management: Empirical investigation of academia versus industrySimon Godecharle, Ben Nemery, Kris Dierickx5A: Education: For whom, how, and what?CS05A.1 Research integrity or responsible conduct of research? What do we aim for?Mickey Gjerris, Maud Marion Laird Eriksen, Jeppe Berggren HoejCS05A.2 Teaching and learning about RCR at the same time: a report on Epigeum’s RCR poll questions and other assessment activitiesNicholas H. SteneckCS05A.4 Minding the gap in research ethics education: strategies to assess and improve research competencies in community health workers/promoteresCamille Nebeker, Michael Kalichman, Elizabeth Mejia Booen, Blanca Azucena Pacheco, Rebeca Espinosa Giacinto, Sheila Castaneda6. Country examples of research reward systems and integrityCS06.1 Improving systems to promote responsible research in the Chinese Academy of SciencesDing Li, Qiong Chen, Guoli Zhu, Zhonghe SunCS06.4 Exploring the perception of research integrity amongst public health researchers in IndiaParthasarathi Ganguly, Barna Ganguly7. Education and guidance on research integrity: country differencesCS07.1 From integrity to unity: how research integrity guidance differs across universities in Europe.Noémie Aubert Bonn, Kris Dierickx, Simon GodecharleCS07.2 Can education and training develop research integrity? The spirit of the UNESCO 1974 recommendation and its updatingDaniele Bourcier, Jacques Bordé, Michèle LeducCS07.3 The education and implementation mechanisms of research ethics in Taiwan's higher education: an experience in Chinese web-based curriculum development for responsible conduct of researchChien Chou, Sophia Jui-An PanCS07.4 Educating principal investigators in Swiss research institutions: present and future perspectivesLouis Xaver Tiefenauer8. Measuring and rewarding research productivityCS08.1 Altimpact: how research integrity underpins research impactDaniel Barr, Paul TaylorCS08.2 Publication incentives: just reward or misdirection of funds?Lyn Margaret HornCS08.3 Why Socrates never charged a fee: factors contributing to challenges for research integrity and publication ethicsDeborah Poff9. Plagiarism and falsification: Behaviour and detectionCS09.1 Personality traits predict attitude towards plagiarism of self and others in biomedicine: plagiarism, yes we can?Martina Mavrinac, Gordana Brumini, Mladen PetrovečkiCS09.2 Investigating the concept of and attitudes toward plagiarism for science teachers in Brazil: any challenges for research integrity and policy?Christiane Coelho Santos, Sonia VasconcelosCS09.3 What have we learnt?: The CrossCheck Service from CrossRefRachael LammeyCS09.4 High p-values as a sign of data fabrication/falsificationChris Hartgerink, Marcel van Assen, Jelte Wicherts10. Codes for research integrity and collaborationsCS10.1 Research integrity in cross-border cooperation: a Nordic exampleHanne Silje HaugeCS10.3 Research integrity, research misconduct, and the National Science Foundation's requirement for the responsible conduct of researchAaron MankaCS10.4 A code of conduct for international scientific cooperation: human rights and research integrity in scientific collaborations with international academic and industry partnersRaffael Iturrizaga11. Countries' efforts to establish mentoring and networksCS11.1 ENRIO : a network facilitating common approaches on research integrity in EuropeNicole FoegerCS11.2 Helping junior investigators develop in a resource-limited country: a mentoring program in PeruA. Roxana Lescano, Claudio Lanata, Gissella Vasquez, Leguia Mariana, Marita Silva, Mathew Kasper, Claudia Montero, Daniel Bausch, Andres G LescanoCS11.3 Netherlands Research Integrity Network: the first six monthsFenneke Blom, Lex BouterCS11.4 A South African framework for research ethics and integrity for researchers, postgraduate students, research managers and administratorsLaetus OK Lategan12. Training and education in research integrity at an early career stageCS12.1 Research integrity in curricula for medical studentsGustavo Fitas ManaiaCS12.2 Team-based learning for training in the responsible conduct of research supports ethical decision-makingWayne T. McCormack, William L. Allen, Shane Connelly, Joshua Crites, Jeffrey Engler, Victoria Freedman, Cynthia W. Garvan, Paul Haidet, Joel Hockensmith, William McElroy, Erik Sander, Rebecca Volpe, Michael F. VerderameCS12.4 Research integrity and career prospects of junior researchersSnezana Krstic13. Systems and research environments in institutionsCS13.1 Implementing systems in research institutions to improve quality and reduce riskLouise HandyCS13.2 Creating an institutional environment that supports research integrityDebra Schaller-DemersCS13.3 Ethics and Integrity Development Grants: a mechanism to foster cultures of ethics and integrityPaul Taylor, Daniel BarrCS13.4 A culture of integrity at KU LeuvenInge Lerouge, Gerard Cielen, Liliane Schoofs14. Peer review and its role in research integrityCS14.1 Peer review research across disciplines: transdomain action in the European Cooperation in Science and Technology “New Frontiers of Peer Review ”Ana Marusic, Flaminio SquazzoniCS14.2 Using blinding to reduce bias in peer reviewDavid VauxCS14.3 How to intensify the role of reviewers to promote research integrityKhalid Al-Wazzan, Ibrahim AlorainyCS14.4 Credit where credit’s due: professionalizing and rewarding the role of peer reviewerChris Graf, Verity Warne15. Research ethics and oversight for research integrity: Does it work?CS15.1 The psychology of decision-making in research ethics governance structures: a theory of bounded rationalityNolan O'Brien, Suzanne Guerin, Philip DoddCS15.2 Investigator irregularities: iniquity, ignorance or incompetence?Frank Wells, Catherine BlewettCS15.3 Academic plagiarismFredric M. Litto16. Research integrity in EuropeCS16.1 Whose responsibility is it anyway?: A comparative analysis of core concepts and practice at European research-intensive universities to identify and develop good practices in research integrityItziar De Lecuona, Erika Löfstrom, Katrien MaesCS16.2 Research integrity guidance in European research universitiesKris Dierickx, Noémie Bonn, Simon GodecharleCS16.3 Research Integrity: processes and initiatives in Science Europe member organisationsTony Peatfield, Olivier Boehme, Science Europe Working Group on Research IntegrityCS16.4 Promoting research integrity in Italy: the experience of the Research Ethics and Bioethics Advisory Committee of the Consiglio Nazionale delle Ricerche Cinzia Caporale, Daniele Fanelli17. Training programs for research integrity at different levels of experience and seniorityCS17.1 Meaningful ways to incorporate research integrity and the responsible conduct of research into undergraduate, graduate, postdoctoral and faculty training programsJohn Carfora, Eric Strauss, William LynnCS17.2 "Recognize, respond, champion": Developing a one-day interactive workshop to increase confidence in research integrity issuesDieter De Bruyn, Bracke Nele, Katrien De Gelder, Stefanie Van der BurghtCS17.4 “Train the trainer” on cultural challenges imposed by international research integrity conversations: lessons from a projectJosé Roberto Lapa e Silva, Sonia M. R. Vasconcelos18. Research and societal responsibilityCS18.1 Promoting the societal responsibility of research as an integral part of research integrityHelene IngierdCS18.2 Social responsibility as an ethical imperative for scientists: research, education and service to societyMark FrankelCS18.3 The intertwined nature of social responsibility and hope in scienceDaniel Vasgird, Stephanie BirdCS18.4 Common barriers that impede our ability to create a culture of trustworthiness in the research communityMark Yarborough19. Publication ethicsCS19.1 The authors' forum: A proposed tool to improve practices of journal editors and promote a responsible research environmentIbrahim Alorainy, Khalid Al-WazzanCS19.2 Quantifying research integrity and its impact with text analyticsHarold GarnerCS19.3 A closer look at authorship and publication ethics of multi- and interdisciplinary teamsLisa Campo-Engelstein, Zubin Master, Elise Smith, David Resnik, Bryn Williams-JonesCS19.4 Invisibility of duplicate publications in biomedicineMario Malicki, Ana Utrobicic, Ana Marusic20. The causes of bad and wasteful research: What can we do?CS20.1 From countries to individuals: unravelling the causes of bias and misconduct with multilevel meta-meta-analysisDaniele Fanelli, John PA IoannidisCS20.2 Reducing research waste by integrating systems of oversight and regulationGerben ter Riet, Tom Walley, Lex Marius BouterCS20.3 What are the determinants of selective reporting?: The example of palliative care for non-cancer conditionsJenny van der Steen, Lex BouterCS20.4 Perceptions of plagiarism, self-plagiarism and redundancy in research: preliminary results from a national survey of Brazilian PhDsSonia Vasconcelos, Martha Sorenson, Francisco Prosdocimi, Hatisaburo Masuda, Edson Watanabe, José Carlos Pinto, Marisa Palácios, José Lapa e Silva, Jacqueline Leta, Adalberto Vieyra, André Pinto, Mauricio Sant’Ana, Rosemary Shinkai21. Are there country-specific elements of misconduct?CS21.1 The battle with plagiarism in Russian science: latest developmentsBoris YudinCS21.2 Researchers between ethics and misconduct: A French survey on social representations of misconduct and ethical standards within the scientific communityEtienne Vergès, Anne-Sophie Brun-Wauthier, Géraldine VialCS21.3 Experience from different ways of dealing with research misconduct and promoting research integrity in some Nordic countriesTorkild VintherCS21.4 Are there specifics in German research misconduct and the ways to cope with it?Volker Bähr, Charité22. Research integrity teaching programmes and their challengesCS22.1 Faculty mentors and research integrityMichael Kalichman, Dena PlemmonsCS22.2 Training the next generation of scientists to use principles of research quality assurance to improve data integrity and reliabilityRebecca Lynn Davies, Katrina LaubeCS22.3 Fostering research integrity in a culturally-diverse environmentCynthia Scheopner, John GallandCS22.4 Towards a standard retraction formHervé Maisonneuve, Evelyne Decullier23. Commercial research and integrityCS23.1 The will to commercialize: matters of concern in the cultural economy of return-on-investment researchBrian NobleCS23.2 Quality in drug discovery data reporting: a mission impossible?Anja Gilis, David J. Gallacher, Tom Lavrijssen, Malwitz David, Malini Dasgupta, Hans MolsCS23.3 Instituting a research integrity policy in the context of semi-private-sector funding: an example in the field of occupational health and safetyPaul-Emile Boileau24. The interface of publication ethics and institutional policiesCS24.1 The open access ethical paradox in an open government effortTony SavardCS24.2 How journals and institutions can work together to promote responsible conductEric MahCS24.3 Improving cooperation between journals and research institutions in research integrity casesElizabeth Wager, Sabine Kleinert25. Reproducibility of research and retractionsCS25.1 Promoting transparency in publications to reduce irreproducibilityVeronique Kiermer, Andrew Hufton, Melanie ClyneCS25.2 Retraction notices issued for publications by Latin American authors: what lessons can we learn?Sonia Vasconcelos, Renan Moritz Almeida, Aldo Fontes-Pereira, Fernanda Catelani, Karina RochaCS25.3 A preliminary report of the findings from the Reproducibility Project: Cancer biologyElizabeth Iorns, William Gunn26. Research integrity and specific country initiativesCS26.1 Promoting research integrity at CNRS, FranceMichèle Leduc, Lucienne LetellierCS26.2 In pursuit of compliance: is the tail wagging the dog?Cornelia MalherbeCS26.3 Newly established research integrity policies and practices: oversight systems of Japanese research universitiesTakehito Kamata27. Responsible conduct of research and country guidelinesCS27.1 Incentives or guidelines? Promoting responsible research communication through economic incentives or ethical guidelines?Vidar EnebakkCS27.3 Responsible conduct of research: a view from CanadaLynn PenrodCS27.4 The Danish Code of Conduct for Research Integrity: a national initiative to promote research integrity in DenmarkThomas Nørgaard, Charlotte Elverdam28. Behaviour, trust and honestyCS28.1 The reasons behind non-ethical behaviour in academiaYves FassinCS28.2 The psychological profile of the dishonest scholarCynthia FekkenCS28.3 Considering the implications of Dan Ariely’s keynote speech at the 3rd World Conference on Research Integrity in MontréalJamal Adam, Melissa S. AndersonCS28.4 Two large surveys on psychologists’ views on peer review and replicationJelte WichertsBrett Buttliere29. Reporting and publication bias and how to overcome itCS29.1 Data sharing: Experience at two open-access general medical journalsTrish GrovesCS29.2 Overcoming publication bias and selective reporting: completing the published recordDaniel ShanahanCS29.3 The EQUATOR Network: promoting responsible reporting of health research studiesIveta Simera, Shona Kirtley, Eleana Villanueva, Caroline Struthers, Angela MacCarthy, Douglas Altman30. The research environment and its implications for integrityCS30.1 Ranking of scientists: the Russian experienceElena GrebenshchikovaCS30.4 From cradle to grave: research integrity, research misconduct and cultural shiftsBronwyn Greene, Ted RohrPARTNER SYMPOSIAPartner Symposium AOrganized by EQUATOR Network, Enhancing the Quality and Transparency of Health ResearchP1 Can we trust the medical research literature?: Poor reporting and its consequencesIveta SimeraP2 What can BioMed Central do to improve published research?Daniel Shanahan, Stephanie HarrimanP3 What can a "traditional" journal do to improve published research?Trish GrovesP4 Promoting good reporting practice for reliable and usable research papers: EQUATOR Network, reporting guidelines and other initiativesCaroline StruthersPartner Symposium COrganized by ENRIO, the European Network of Research Integrity OfficersP5 Transparency and independence in research integrity investigations in EuropeKrista Varantola, Helga Nolte, Ursa Opara, Torkild Vinther, Elizabeth Wager, Thomas NørgaardPartner Symposium DOrganized by IEEE, the Institute of Electrical and Electronics EngineersRe-educating our author community: IEEE's approach to bibliometric manipulation, plagiarism, and other inappropriate practicesP6 Dealing with plagiarism in the connected world: An Institute of Electrical and Electronics Engineers perspectiveJon RokneP7 Should evaluation of raises, promotion, and research proposals be tied to bibliometric indictors? What the Institute of Electrical and Electronics Engineers is doing to answer this questionGianluca SettiP8 Recommended practices to ensure conference content qualityGordon MacPhersonPartner Symposium EOrganized by the Committee on Freedom and Responsibility in the Conduct of Science of ICSU, the International Council for ScienceResearch assessment and quality in science: perspectives from international science and policy organisationsP9 Challenges for science and the problems of assessing researchEllen HazelkornP10 Research assessment and science policy developmentCarthage SmithP11 Research integrity in South Africa: the value of procedures and processes to global positioningRobert H. McLaughlinP12 Rewards, careers and integrity: perspectives of young scientists from around the worldTatiana Duque MartinsPartner Symposium FOrganized by the Online Resource Center for Ethics Education in Engineering and Science / Center for Engineering, Ethics, and Society of the National Academy of EngineeringP13 Research misconduct: conceptions and policy solutionsTetsuya Tanimoto, Nicholas Steneck, Daniele Fanelli, Ragnvald Kalleberg, Tajammul HusseinPartner Symposium HOrganized by ORI, the Office of Research Integrity; Universitas 21; and the Asia Pacific Research Integrity NetworkP14 International integrity networks: working together to ensure research integrityPing Sun, Ovid Tzeng, Krista Varantola, Susan ZimmermanPartner Symposium IOrganized by COPE, the Committee on Publication EthicsPublication without borders: Ethical challenges in a globalized worldP15 Authorship: credit and responsibility, including issues in large and interdisciplinary studiesRosemary ShinkaiPartner Symposium JOrganized by CITI, the Cooperative Institutional Training InitiativeExperiences on research integrity educational programs in Colombia, Costa Rica and PeruP16 Experiences in PeruRoxana LescanoP17 Experiences in Costa RicaElizabeth HeitmanP18 Experiences in ColumbiaMaria Andrea Rocio del Pilar Contreras NietoPoster Session B: Education, training, promotion and policyPT.01 The missing role of journal editors in promoting responsible researchIbrahim Alorainy, Khalid Al-WazzanPT.02 Honorary authorship in Taiwan: why and who should be in charge?Chien Chou, Sophia Jui-An PanPT.03 Authorship and citation manipulation in academic researchEric Fong, Al WilhitePT.04 Open peer review of research submission at medical journals: experience at BMJ Open and The BMJTrish GrovesPT.05 Exercising authorship: claiming rewards, practicing integrityDésirée Motta-RothPT.07 Medical scientists' views on publication culture: a focus group studyJoeri Tijdink, Yvo SmuldersPoster Session B: Education, training, promotion and policyPT.09 Ethical challenges in post-graduate supervisionLaetus OK LateganPT.10 The effects of viable ethics instruction on international studentsMichael Mumford, Logan Steele, Logan Watts, James Johnson, Shane Connelly, Lee WilliamsPT.11 Does language reflect the quality of research?Gerben ter Riet, Sufia Amini, Lotty Hooft, Halil KilicogluPT.12 Integrity complaints as a strategic tool in policy decision conflictsJanneke van Seters, Herman Eijsackers, Fons Voragen, Akke van der Zijpp and Frans BromPoster Session C: Ethics and integrity intersectionsPT.14 Regulations of informed consent: university-supported research processes and pitfalls in implementationBadaruddin Abbasi, Naif Nasser AlmasoudPT.15 A review of equipoise as a requirement in clinical trialsAdri LabuschagnePT.16 The Research Ethics Library: online resource for research ethics educationJohanne Severinsen, Espen EnghPT.17 Research integrity: the view from King Abdulaziz City for Science and TechnologyDaham Ismail AlaniPT. 18 Meeting global challenges in high-impact publications and research integrity: the case of the Malaysian Palm Oil BoardHJ. Kamaruzaman JusoffPT.19 University faculty perceptions of research practices and misconductAnita Gordon, Helen C. HartonPoster Session D: International perspectivesPT.21 The Commission for Scientific Integrity as a response to research fraudDieter De Bruyn, Stefanie Van der BurghtPT. 22 Are notions of the responsible conduct of research associated with compliance with requirements for research on humans in different disciplinary traditions in Brazil?Karina de Albuquerque Rocha, Sonia Maria Ramos de VasconcelosPT.23 Creating an environment that promotes research integrity: an institutional model of Malawi Liverpool Welcome TrustLimbanazo MatandikaPT.24 How do science policies in Brazil influence user-engaged ecological research?Aline Carolina de Oliveira Machado Prata, Mark William NeffPoster Session E: Perspectives on misconductPT.26 What “causes” scientific misconduct?: Testing major hypotheses by comparing corrected and retracted papersDaniele Fanelli, Rodrigo Costas, Vincent LarivièrePT.27 Perception of academic plagiarism among dentistry studentsDouglas Leonardo Gomes Filho, Diego Oliveira GuedesPT. 28 a few bad apples?: Prevalence, patterns and attitudes towards scientific misconduct among doctoral students at a German university hospitalVolker Bähr, Niklas Keller, Markus Feufel, Nikolas OffenhauserPT. 29 Analysis of retraction notices published by BioMed CentralMaria K. Kowalczuk, Elizabeth C. MoylanPT.31 "He did it" doesn't work: data security, incidents and partnersKatie SpeanburgPoster Session F: Views from the disciplinesPT.32 Robust procedures: a key to generating quality results in drug discoveryMalini Dasgupta, Mariusz Lubomirski, Tom Lavrijssen, David Malwitz, David Gallacher, Anja GillisPT.33 Health promotion: criteria for the design and the integrity of a research projectMaria Betânia de Freitas Marques, Laressa Lima Amâncio, Raphaela Dias Fernandes, Oliveira Patrocínio, and Cláudia Maria Correia Borges RechPT.34 Integrity of academic work from the perspective of students graduating in pharmacy: a brief research studyMaria Betânia de Freitas Marques, Cláudia Maria Correia Borges Rech, Adriana Nascimento SousaPT.35 Research integrity promotion in the Epidemiology and Health Services, the journal of the Brazilian Unified Health SystemLeila Posenato GarciaPT.36 When are clinical trials registered? An analysis of prospective versus retrospective registration of clinical trials published in the BioMed Central series, UKStephanie Harriman, Jigisha PatelPT.37 Maximizing welfare while promoting innovation in drug developmentFarida LadaOther posters that will be displayed but not presented orally:PT.38 Geoethics and the debate on research integrity in geosciencesGiuseppe Di Capua, Silvia PeppoloniPT.39 Introducing the Professionalism and Integrity in Research Program James M. DuBois, John Chibnall, Jillon Van der WallPT.40 Validation of the professional decision-making in research measureJames M. DuBois, John Chibnall, Jillon Van der Wall, Raymond TaitPT.41 General guidelines for research ethicsJacob HolenPT. 42 A national forum for research ethicsAdele Flakke Johannessen, Torunn EllefsenPT.43 Evaluation of integrity in coursework: an approach from the perspective of the higher education professorClaudia Rech, Adriana Sousa, Maria Betânia de Freitas MarquesPT.44 Principles of geoethics and research integrity applied to the European Multidisciplinary Seafloor and Water Column Observatory, a large-scale European environmental research infrastructureSilvia Peppoloni, Giuseppe Di Capua, Laura BeranzoliF1 Focus track on improving research systems: the role of fundersPaulo S.L. Beirão, Susan ZimmermanF2 Focus track on improving research systems: the role of countriesSabine Kleinert, Ana MarusicF3 Focus track on improving research systems: the role of institutionsMelissa S. Anderson, Lex Bouter. (shrink)

Network neuroscience represents the brain as a collection of regions and inter-regional connections. Given its ability to formalize systems-level models, network neuroscience has generated unique explanations of neural function and behavior. The mechanistic status of these explanations and how they can contribute to and fit within the field of neuroscience as a whole has received careful treatment from philosophers. However, these philosophical contributions have not yet reached many neuroscientists. Here we complement formal philosophical efforts by providing an applied perspective from (...) and for neuroscientists. We discuss the mechanistic status of the explanations offered by network neuroscience and how they contribute to, enhance, and interdigitate with other types of explanations in neuroscience. In doing so, we rely on philosophical work concerning the role of causality, scale, and mechanisms in scientific explanations. In particular, we make the distinction between an explanation and the evidence supporting that explanation, and we argue for a scale-free nature of mechanistic explanations. In the course of these discussions, we hope to provide a useful applied framework in which network neuroscience explanations can be exercised across scales and combined with other fields of neuroscience to gain deeper insights into the brain and behavior. (shrink)

With the development of network technology, enterprises face the explosive growth of data every day. Therefore, to fully mine the value of massive data, big data analysis technology has become the key to developing the core competitiveness of enterprises. However, few empirical studies have investigated the influencing mechanism of the BDA capability of an enterprise on its operational performance. To fill this gap, this study explores how BDA technology capability influences enterprise operation performance, based on dynamic (...) capabilities theory and resource-based theory. It proposes the key variables, including the connectivity, compatibility, and modularization of big data analysis technical capability, enterprise's operational performance, and the fit between data and tools, to establish a model and study the correlation between the variables. The results highlight the mediating role of data-tool fit in the relationships between BDA capability and the enterprise's operational performance, which is a major finding that has not been underlined in the extant literature. This study provides valuable insight for operational managers to help them in mobilizing BDA capability for enterprises' operational management and improving operational performance. (shrink)

We applaud Dienes & Perner's efforts while raising some concerns regarding their assimilation of diverse data into a unifying framework. Some of the findings need not fit the framework they suggest. It is also not always clear what, above logico-semantic consistency, assimilation adds to the data that do fit their framework. These concerns are highlighted with reference to their arguments regarding the developmental data and the neuropsychological data, respectively.

Children learn their native language by exposure to their linguistic and communicative environment, but apparently without requiring that their mistakes be corrected. Such learning from “positive evidence” has been viewed as raising “logical” problems for language acquisition. In particular, without correction, how is the child to recover from conjecturing an over-general grammar, which will be consistent with any sentence that the child hears? There have been many proposals concerning how this “logical problem” can be dissolved. In this study, we (...) review recent formal results showing that the learner has sufficient data to learn successfully from positive evidence, if it favors the simplest encoding of the linguistic input. Results include the learnability of linguistic prediction, grammaticality judgments, language production, and form-meaning mappings. The simplicity approach can also be “scaled down” to analyze the learnability of specific linguistic constructions, and it is amenable to empirical testing as a framework for describing human language acquisition. (shrink)

Prelog’s model was one of the first empirical models to explain the stereoselectivity of the Grignard reactions of 2-oxocarboxylic acid esters bearing a chiral alcohol. Prelog constructed his model based on some assumptions regarding the conformation of chiral 2-oxocarboxylic acid esters to explain the relationship in configuration between the chiral alcohol starting materials and the 2-hydroxycarboxylic acid products. Construction of the model involves four steps: (1) mentally analyzing the reactants to identify the basic stereochemical structures, (2) assuming the conformations of (...) the chiral alcohol moiety as the main reasons for stereoselectivity, (3) modeling the transition states to explain the relationship in configuration between the chiral alcohols and 2-hydroxycarboxylic acids, and (4) validating the evidence to revise the model. The process of constructing this historically pivotal model may have implications for science education, especially for developing theories from extensive data. The four steps from the Prelog’s model were compared with the models for boiling points of hydrocarbons and solubility of amino acids. (shrink)

The multi-purpose of publicizing a scientific consensus includes a communicative strategy by which scientific institutions accommodate the weighty social and economic demands to demonstrate they are collaborating and cooperating with non-scientific sectors of society, relying on a wide range of spokespeople and representatives to carry out the delivery of their consensus in formal, institutionally arranged, professional and impersonal public settings. I investigate the conditions and presuppositions that make it possible for a research consortium to contribute indirectly to public discourse (...) beyond the presentation of empirical data and theoretical speculations routinely associated with knowledge-producing collectives. The baptismal action of researchers in selecting a designative name to announce a new discovery of virus species and species variants does not follow the rigorous regulations that standardize all names of taxonomic categories in other biological sciences as well as higher order taxa in virology. It is argued here that the lack of clarity in the denotation of the term ‘Ebola’ (in epidemiological reports from West Africa throughout 2014–2016) serves the purpose of shifting a receptor’s understanding of a statement from its explicit assertive point to an implicit declarative, commissive, directive or expressive, covertly delivered point. Specialists of nomenclature concerned with quality assurance regard this lapse in standardization as a consequence of human fallibility demanding urgent intervention. Here it is proposed instead that the occlusive effect of a technical descriptive name may serve an important communicative function. (shrink)

Recent evidence suggests that experienced events are often mapped to too many episodic states, including those that are logically or experimentally incompatible with one another. For example, episodic over-distribution patterns show that the probability of accepting an item under different mutually exclusive conditions violates the disjunction rule. A related example, called subadditivity, occurs when the probability of accepting an item under mutually exclusive and exhaustive instruction conditions sums to a number >1. Both the over-distribution effect and subadditivity have been (...) widely observed in item and source-memory paradigms. These phenomena are difficult to explain using standard memory frameworks, such as signal-detection theory. A dual-trace model called the over-distribution model can explain the episodic over-distribution effect, but not subadditivity. Our goal is to develop a model that can explain both effects. In this paper, we propose the Generalized Quantum Episodic Memory model, which extends the Quantum Episodic Memory model developed by Brainerd, Wang, and Reyna. We test GQEM by comparing it to the OD model using data from a novel item-memory experiment and a previously published source-memory experiment examining the over-distribution effect. Using the best-fit parameters from the over-distribution experiments, we conclude by showing that the GQEM model can also account for subadditivity. Overall these results add to a growing body of evidence suggesting that quantum probability theory is a valuable tool in modeling recognition memory. (shrink)

Conceptual features differ in how mentally tranformable they are. A robin that does not eat is harder to imagine than a robin that does not chirp. We argue that features are immutable to the extent that they are central in a network of dependency relations. The immutability of a feature reflects how much the internal structure of a concept depends on that feature; i.e., how much the feature contributes to the concept's coherence. Complementarily, mutability reflects the aspects in which a (...) concept is flexible. We show that features can be reliably ordered according to their mutability using tasks that require people to conceive of objects missing a feature, and that mutability (conceptual centrality) can be distinguished from category centrality and from diagnosticity and salience. We test a model of mutability based on asymmetric, unlabeled, pairwise dependency relations. With no free parameters, the model provides reasonable fits to data. Qualitative tests of the model show that mutability judgments are unaffected by the type of dependency relation and that dependency structure influences judgments of variability. (shrink)

This paper discusses some philosophical aspects related to the recent publication of the experimental results of the 2017 black hole experiment, namely the first image of the supermassive black hole at the center of galaxy M87. In this paper I present a philosophical analysis of the 2017 Event Horizon Telescope (EHT) black hole experiment. I first present Hacking’s philosophy of experimentation. Hacking gives his taxonomy of elements of laboratory science and distinguishes a list of elements. I show that the EHT (...) experiment conforms to major elements from Hacking’s list. I then describe with the help of Galison’s Philosophy of the Shadow how the EHT Collaboration created the famous black hole image. Galison outlines three stages for the reconstruction of the black hole image: Socio-Epistemology, Mechanical Objectivity, after which there is an additional SocioEpistemology stage. I subsequently present my own interpretation of the reconstruction of the black hole image and I discuss model fitting to data. I suggest that the main method used by the EHT Collaboration to assure trust in the results of the EHT experiment is what philosophers call the Argument from Coincidence. I show that using this method for the above purpose is problematic. I present two versions of the Argument from Coincidence: Hacking’s Coincidence and Cartwright’s Reproducibility by which I analyse the EHT experiment. The same estimation of the mass of the black hole is reproduced in four different procedures. The EHT Collaboration concludes: the value we have converged upon is robust. I analyse the mass measurements of the black hole with the help of Cartwright’s notion of robustness. I show that the EHT Collaboration construe Coincidence/Reproducibility as Technological Agnosticism and I contrast this interpretation with van Fraassen’s scientific agnosticism. (shrink)

Over the last 40 years or so, economics has become a modelling science: a science in which models have become one of the main epistemological tools both for theoretical and applied work. But providing an account of how models work and what they do for the economist is not easy. For the philosopher of economics like me, struggling with this question, John Sutton's views on the nature and design of economic models and how they work is indeed thought provoking. (...) Because of my own interests, my review of Sutton's book: Marshall's Tendencies: What Can Economists Know? will focus on three related issues that I found especially intriguing in his treatment of the role of models in modern economics. The first is the way that Sutton's account fits with my own reading of the history of twentieth-century economics, namely that the focus of economic explanation has moved during the last century from and to and . The second is to understand the epistemological connotations of Sutton's view. The third is to explore what Sutton means when he says a model . These three questions roughly coincide with the material presented in Chapters 1, 3 and 2 respectively of the book. As we shall see, Sutton gives us a practitioner account of applied economics which can fit within the standard terms used by philosophers of economics on theory-testing, but which reveals a number of novel elements for philosophical analysis. (shrink)

This article uses simulation as an empirical method for identifying process models of strategy effects in a category-learning task. A general set of learning assumptions defined a symbolic learning framework in which alternative simulation models were defined and tested. The goal was to identify process models that could account for previously reported data on the interaction between how a learner encounters category variance across a series of training samples and whether the task instructions suggested an active, hypothesis-testing approach, or (...) a more passive learning mode. Descriptive characterizations of active and passive learning were mapped into complementary settings of parameters operating with the general learning framework. Alternative models, defined by different configurations of these parameters, were evaluated on their goodness of fit to the observed data. The signature differences between models that best fit the passive learning data and models that best fit the active learning data concerned a delayed versus immediate learning parameter and a degree-of-match parameter that determined which patterns were retrieved to make category decisions. A functional account of these parameters is given by considering the learning task as a search process and the role of these parameters in localizing the impact of learning mechanisms in certain areas of the search space. Issues related to simulation as an empirical method for identifying candidate process models are discussed. (shrink)

Data science, and the related field of big data, is an emerging discipline involving the analysis of data to solve problems and develop insights. This rapidly growing domain promises many benefits to both consumers and businesses. However, the use of big data analytics can also introduce many ethical concerns, stemming from, for example, the possible loss of privacy or the harming of a sub-category of the population via a classification algorithm. To help address these potential ethical (...) challenges, this paper maps and describes the main ethical themes that were identified via systematic literature review. It then identifies a possible structure to integrate these themes within a data science project, thus helping to provide some structure in the on-going debate with respect to the possible ethical situations that can arise when using data science analytics. (shrink)

At some point during the 1950s, mainstream American philosophy of science began increasingly to avoid questions about the role of non-cognitive values in science and, accordingly, increasingly to avoid active engagement with social, political and moral concerns. Such questions and engagement eventually ceased to be part of the mainstream. Here we show that the eventual dominance of 'value-free' philosophy of science can be attributed, at least in part, to the policies of the U.S. National Science Foundation's "History and Philosophy (...) of Science" sub-program. In turn, the sub-program's policies were set by logical empiricists who espoused value-free philosophy of science; these philosophers' actions, we also point out, fit a broad pattern, one in which analytic philosophers used institutional control to marginalize rival approaches to philosophy. We go on to draw on existing knowledge of this pattern to suggest two further, similar, contributors to the withdrawal from value-laden philosophy of science, namely decisions by the editors of Philosophy of Science and by the editors of The Journal of Philosophy. Political climate was, we argue, at most an indirect contributor to the withdrawal and was neither a factor that decided whether it occurred nor one that was sufficient to bring it about. Moreover, we argue that the actions at the National Science Foundation went beyond what was required by its senior administrators and are better viewed as part of what drove, rather than as what was being driven by, the adoption of logical empiricism by the philosophy of science community. (shrink)

Mental model construction is supposed to be a useful cognitive devise for learning. Beyond human capacity of constructing mental models, scientists construct complex explanations about phenomena, named scientific or theoretical models. In this work we revisit three vissions: the first one concern about the polisemic term “model”. Our proposal is to discriminate between “mental models” and “explicit models”, being the former those “imaginistic” ideas constructed in scientists’—o teachers—minds, and the latter those teaching devices expressed in different languages that tend (...) to communicate any “scientific model”. From this point of view, the class is considered a place where teachers’ mental models should be learned by novice students by decoding their teaching devices which are expressed in different languages. Other proposal of this work claims to distinguish the term “representation” with respect to its artistical or instrumental origin, highlighting that they are types of teaching devices and that artistical representations are always analogies. Finally, data about the construction of freshmen’s wrong mental models related to the use of the analogy between the chemical combustion and the global process of cellular respiration from glucose is presented to reinforce previous epistemological reflections. (shrink)

This is a largely expository paper in which the following simple idea is pursued. Take the truth value of a formula to be the set of agents that accept the formula as true. This means we work with an arbitrary Boolean algebra as the truth value space. When this is properly formalized, complete modal tableau systems exist, and there are natural versions of bisimulations that behave well from an algebraic point of view. There remain significant problems concerning the proper (...) formalization, in this context, of natural language statements, particularly those involving negative knowledge and common knowledge. A case study is presented which brings these problems to the fore. None of the basic material presented here is new to this paper—all has appeared in several papers over many years, by the present author and by others. Much of the development in the literature is more general than here—we have confined things to the Boolean case for simplicity and clarity. Most proofs are omitted, but several of the examples are new. The main virtue of the present paper is its coherent presentation of a systematic point of view—identify the truth value of a formula with the set of those who say the formula is true. (shrink)

Distributional semantic models (DSMs) are a primary method for distilling semantic information from corpora. However, a key question remains: What types of semantic relations among words do DSMs detect? Prior work typically has addressed this question using limited human data that are restricted to semantic similarity and/or general semantic relatedness. We tested eight DSMs that are popular in current cognitive and psycholinguistic research (positive pointwise mutual information; global vectors; and three variations each of Skip-gram and continuous bag of words (...) (CBOW) using word, context, and mean embeddings) on a theoretically motivated, rich set of semantic relations involving words from multiple syntactic classes and spanning the abstract–concrete continuum (19 sets of ratings). We found that, overall, the DSMs are best at capturing overall semantic similarity and also can capture verb–noun thematic role relations and noun–noun event-based relations that play important roles in sentence comprehension. Interestingly, Skip-gram and CBOW performed the best in terms of capturing similarity, whereas GloVe dominated the thematic role and event-based relations. We discuss the theoretical and practical implications of our results, make recommendations for users of these models, and demonstrate significant differences in model performance on event-based relations. (shrink)

The quality of early childhood education and care (ECEC) is important for children’s development. One instrument that was developed to assess an aspect of ECEC quality is the Classroom Assessment Scoring System for pre-kindergarten children (CLASS Pre-K). We examined the factorial validity of the instrument using data from 177 German preschool classrooms. The three-factor teaching through interaction model (Hamre et al., 2013) was contrasted to a one-factor, a two-factor, and a bifactor model as proposed by Hamre et al. (2014). (...) Our results indicated that the three-factor structure with the domains of emotional support, classroom organization, and instructional support fit the data best. The fit of the teaching through interaction model was satisfying after adding a cross-loading of the dimension language modeling on emotional support, and two correlated residuals. Evidence of convergent and discriminant validity are provided. In terms of factor structure and pattern score comparisons, the results were similar to previous U.S. and German studies. The discussion concerns the justifiability of the factor model revisions and draws directions for further research. We concluded that our study offers further evidence of the applicability of the CLASS Pre-K for the assessment of teacher-child interaction quality in the German context. (shrink)

Consider the concept combination ‘pet human’. In word association experiments, human subjects produce the associate ‘slave’ in relation to this combination. The striking aspect of this associate is that it is not produced as an associate of ‘pet’, or ‘human’ in isolation. In other words, the associate ‘slave’ seems to be emergent. Such emergent associations sometimes have a creative character and cognitive science is largely silent about how we produce them. Departing from a dimensional model of human conceptual space, this (...) article will explore concept combinations, and will argue that emergent associations are a result of abductive reasoning within conceptual space, that is, below the symbolic level of cognition. A tensor-based approach is used to model concept combinations allowing such combinations to be formalized as interacting quantum systems. Free association norm data is used to motivate the underlying basis of the conceptual space. It is shown by analogy how some concept combinations may behave like quantum-entangled particles. Two methods of analysis were presented for empirically validating the presence of non-separable concept combinations in human cognition. One method is based on quantum theory and another based on comparing a joint probability distribution with another distribution based on a separability assumption using a chi-square goodness-of-fit test. Although these methods were inconclusive in relation to an empirical study of bi-ambiguous concept combinations, avenues for further refinement of these methods are identified. (shrink)

The chapter discusses the principle of conservatism and traces how the general principle is related to the specific one. This tracing suggests that the principle of conservatism needs to be refined. Connecting the principle in cognitive science to more general questions about scientific inference also allows us to revisit the question of realism versus instrumentalism. The framework deployed in model selection theory is very general; it is not specific to the subject matter of science. The chapter outlines some non-Bayesian ideas (...) that have been developed in model selection theory. The principle of conservatism, like C. Lloyd Morgan's canon, describes a preference concerning kinds of parameters. It says that a model that postulates only lower-level intentionality is preferable to one that postulates higher-level intentionality if both fit the data equally well. The model selection approach to parsimony helps explain why unification is a theoretical virtue. (shrink)

This is a welcome book, for it challenges intelligently the conventional view that Proust's novel is strictly a narrative of metaphor and memory. Nicola Luckhurst's project here is to demonstrate that, with its profoundly legislative impulses and knowledge‐seeking, theory‐laden tone, A la recherche du temps perdu is as much about hypothesis, experiment, and scientific law making as about nostalgia and reminiscence.The entry point for Luckhurst's discussion is a perceptive chapter on Proust's maxims, those abundant sententiae that on the one (...) hand link him to the moralistic tradition of La Bruyère and La Rochefoucauld and on the other demonstrate his need to generate laws that explain behavior and experience. She offers chapters on the novelist's use of hypothesis, modeling, and retroduction; the last notion is especially revelatory in a Proustian context. Borrowing from Aristotle, the philosopher Charles Sanders Pierce coined the term “retroduction” to describe a type of inference that contains within itself the nascent theory, the hypothesis, and the data being examined. Later Norwood Hanson spoke of the simultaneous “dawning of an aspect and a dawning of an explanation [that] both suggest what to look for next” . Applied to A la recherche, retroduction aptly describes the main structure of cognition in Proust: intuitions about a singulative event and hypotheses about that event's relationship to a broader law or theory are always co‐present, with Proust's narrator representing both the datum being examined and the voice speculating on the resulting science.Chapters 5, 6, and 7 focus in large part on the narrative's fascination with male and female homosexuality and assess the models and theories applied to each. The botanical analogy for male homosexuality has of course been widely discussed. But with the help of Antoine Compagnon's notes to Sodome et Gomorrhe , Luckhurst provides a convincing account of the impact made on Proust by the reading of Darwin, Maurice Maeterlinck's L'intelligence des fleurs, various essays by Jules Michelet, the zoologist/embryologist Elie Metchnikoff's Etudes sur la nature humaine: Essai de philosophie optimiste, and Remy de Gourmont's Physique de l'amour. However, as she notes, lesbianism in Proust's novel is an epistemological dead end. No model can account for it, and Proust's narrative, in Albertine disparue, becomes bogged down and “embalmed.” Here, Luckhurst's own narrative loses momentum because of the space it devotes to this nonsignificance.But the central, and persuasive, argument of this study concerns the converging attempts within the scientific and literary communities of the early twentieth century to narrate and conceptualize creativity. The tendency in literature toward what David S. Luft has called “essayism”—so clear cut in Proust—affected a generation of writers convinced that theory and story must converge, that “the tasks of philosophy, literature and cultural criticism” had come together . In parallel, within the scientific community, intuition, analogy, and a kind of aesthetic sense were becoming acknowledged methodological tools, especially in the work of the French mathematician Henri Poincaré, whose speculations on the role of the unconscious in cognition closely parallel those of Proust . Luckhurst's study demonstrates above all that underlying Proust's adamant distinctions between the particular and the general, between the intuitive/instinctive and the intellectual, is a deep esteem for science and an attraction to its laws. Metaphor itself, the Proustian cognitive device par excellence, must in his own words be exact enough to create a relationship “analogous in the artistic world to the unique relationship of the law of causality in the scientific world.” It is the empowerment offered Proust by scientific truth that this study brings so nicely into focus. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:360 HUME'S NATURALI Z EP PHILOSOPHY In "Epistemology Naturalized," Quine claimed that the failure of reductive-foundationalist attempts in epistemology, after the model of Carnap' s Aufbau, must lead to a redefinition of epistemology's task. Instead of setting out to reconstruct the whole fabric of our knowledge from absolute data through deductive operations, we should investigate how human subjects derive their knowledge of nature from sensory inputs. Thus epistemology (...) is to be conceived as a branch of natural science, cognate with psychology, not as the study of the foundations of science any more. In the present paper, I will first outline Quine's position and his related interpretation of Hume's philosophical enterprise. Then, I will try to assess the relevance and fairness of Quine's account and consider in what measure it allows us to understand the novelty of Hume's conception of philosophy, since Hume also advocated a nonfoundationalist conception of philosophy which bears some striking analogy to naturalized epistemology. Finally, I will suggest which consequences are to be drawn from this unusual view of Hume, especially as regards the self-reference of philosophy. Quine's position in the matter of epistemology is well known. For him, under its traditional form and program, epistemology used to deal with the foundations of science, be it mathematics or the science of nature. This foundationalist approach had two connected aspects, one which Quine calls conceptual; the other he calls doctrinal. As Quine 361 puts it "the conceptual studies are concerned with clarifying concepts by defining them, some in terms of others" (Quine, p. 69). In the same manner, the doctrinal studies attempt to establish laws "by proving them, some on the basis of others" (Quine, p. 69). Derivative concepts will be defined in terms of the clearer ones and complex laws will be proved from primitive and obvious ones. Thus the traditional or classical conception of epistemology involves simultaneously a theory of concepts or meanings and a theory of truth. These two branches (Quine speaks of a duality of structure or bifurcation) converge so as to provide an analysis of the way our knowledge of nature -- and our knowledge in general — stems from and is built upon our sensory experience which validates it. In so characterizing epistemology, Quine simply restates in his own terms the f oundationalist and reductivist program of positivist epistemology, which itself tied in with the logicist program in mathematics. Then Quine suggests that Hume dealt with both sides of this program, the conceptual and the doctrinal. To begin with, Hume developed what might be called a theory of concepts or meanings in terms of our sensory data. Such is the point of his theory of ideas which consists in tracing them back to the original impressions they are derived from. In spite of its limitations, which hinge on the lack of an approach in terms of statements, Hume's phenomenalism does the job of a theory of meaning and concepts. Hume also attempted to give an answer to the question of the truth of our knowledge. In this case again, his phenomenalist stand allows him to account for the truth of our singular statements concerning our perceptions, even though this result is achieved at the cost of identifying objects and sensory data. 362 However, when Hume comes to general statements and singular statements concerning future events, his attempt fails. This is not surprising. According to Quine, if it were possible to improve on Hume's theory of meaning and concepts, especially through the use of contextual definition techniques, by contrast, all the attempts to logically derive the truth of our knowledge from true premises have failed. In matter of induction, we are not one step further than Hume was. "The most modest of generalizations about observable traits will cover more cases than its utterer can have had occasions actually to observe. The hopelessness of grounding natural science upon immediate experience in a firmly logical way was acknowledged" (Quine, p. 74). Or "the Humean predicament is the human predicament." For Quine, foundationalist attempts of the sort Hume engaged in involve two cardinal principles of empiricism which remain unchallenged. One of these principles is to the effect... (shrink)

Expiratory flow limitation (EFL) can occur in mechanically ventilated patients with chronic obstructive pulmonary disease and other disorders. It leads to dynamic hyperinflation with ensuing deleterious consequences. Detecting EFL is thus clinically relevant. Easily applicable methods however lack this detection being routinely made in intensive care. Using a simple mathematical model, we propose a new method to detect EFL that does not require any intervention or modification of the ongoing therapeutic. The model consists in a monoalveolar representation of (...) the respiratory system, including a collapsible airway that is submitted to periodic changes in pressure at the airway opening: EFL provokes a sharp expiratory increase in the resistance Rc of the collapsible airway. The model parameters were identified via the Levenberg-Marquardt method by fitting simulated data on the airway pressure and the flow signals recorded in 10 mechanically ventilated patients. A sensitivity study demonstrated that only 8/11 parameters needed to be identified, the remaining three being given reasonable physiological values. Flow-volume curves built at different levels of positive expiratory pressure, PEEP, during PEEP trials (stepwise increases in positive end-expiratory pressure to optimize ventilator settings) have shown evidence of EFL in three cases. This was concordant with parameter identification (high Rc during expiration for EFL patients). We conclude from these preliminary results that our model is a potential tool for the non-invasive detection of EFL in mechanically ventilated patients. (shrink)