Intonation plays an integral role in comprehending spoken language. Listeners can rapidly integrate intonational information to predictively map a given pitch accent onto the speaker's likely referential intentions. We use mouse tracking to investigate two questions: (a) how listeners draw predictive inferences based on information from intonation? and (b) how listeners adapt their online interpretation of intonational cues when these are reliable or unreliable? We formulate a novel Bayesian model of rational predictive cue integration and explore predictions derived under a (...) concrete linking hypothesis relating a quantitative notion of evidential strength of a cue to the moment in time, relative to the unfolding speech signal, at which mouse trajectories turn towards the eventually selected option. In order to capture rational belief updates after concrete observations of a speaker's behavior, we formulate and explore an extension of this model that includes the listener's hierarchical beliefs about the speaker's likely production behavior. Our results are compatible with the assumption that listeners rapidly and rationally integrate all available intonational information, that they expect reliable intonational information initially, and that they adapt these initial expectations gradually during exposition to unreliable input. All materials, data, and scripts can be retrieved here: https://osf.io/dnbuk/. (shrink)

On the fundamental of Hindu philosophy with commentary in verse form.

In order for online communities to assemble and grow, some basic infrastructure is necessary that makes possible the aggregation of the collective action. There is a very intimate and complex relationship between the technological infrastructure and the social character of the community which uses it. Today, most infrastructure is provided by corporations and the contrast between community and corporate dynamics is becoming increasingly pronounced. But rather than address the issues, the corporations are actively obfuscating it. Wikiwashing refers to a strategy (...) of corporate infrastructure providers where practices associated to their role of profit seeking corporations that would be seen as unethical by the communities they enable are concealed by promoting a misleading image of themselves associated with the general values of wikis and Wikipedia . The empirical analysis is based on case studies and triangulation of several methods. (shrink)

I begin with some elementary observations about assertion. In spite of recent criticisms of philosophers who have been too ready to take the subject-predicate indicative sentence as the standard form of assertion, there is no doubt that this form of sentence does represent something very fundamental about assertion. To put the matter in a rough-and-ready way: if we are to assert anything at all, it seems obvious that we must first draw our listener's attention to something that we propose to (...) talk about, and then, when we have secured his attention, go on to say something about that to which we have drawn attention. (shrink)

Keller & Miller's (K&M's) conclusion appears to be correct; namely, that common, harmful, heritable mental disorders are largely maintained at present frequencies by mutation-selection balance at many different loci. However, their “paradox” is questionable. (Published Online November 9 2006).

Discussions of science and values in risk management have largely focused on how values enter into arguments about risks, that is, issues of acceptable risk. Instead this volume concentrates on how values enter into collecting, interpreting, communicating, and evaluating the evidence of risks, that is, issues of the acceptability of evidence of risk. By focusing on acceptable evidence, this volume avoids two barriers to progress. One barrier assumes that evidence of risk is largely a matter of objective scientific data and (...) therefore uncontroversial. The other assumes that evidence of risk, being "just" a matter of values, is not amenable to reasoned critique. Denying both extremes, this volume argues for a more constructive conclusion: understanding the interrelations of scientific and value issues enables a critical scrutiny of risk assessments and better public deliberation about social choices. The contributors, distinguished philosophers, policy analysts, and natural and social scientists, analyze environmental and medical controversies, and assumptions underlying views about risk assessment and the scientific and statistical models used in risk management. (shrink)

: The Centers for Disease Control and Prevention (CDC) has recommended that HIV testing be routinely offered to certain patients in hospitals with a high prevalence of HIV infection and on all pregnant women. The CDC does not, however, offer implementation level guidelines for obtaining informed consent. We provide a moral justification for requiring informed consent for HIV testing and propose guidelines for securing such consent. In particular we argue that genuine informed consent can be secured without elaborate counseling, such (...) as that currently used at Counseling and Testing Sites, provided that sufficient written notice is given to the patients before testing and that they are specifically asked for permission. (shrink)

Once upon a time, logic was the philosopher’s tool for analyzing scientific reasoning. Nowadays, probability and statistics have largely replaced logic, and their most popular application—Bayesianism—has replaced the qualitative deductive relationship between a hypothesis h and evidence e with a quantitative measure of h’s probability in light of e.

There is a wide gap, at any rate in the English-speaking world, between the people whose business it is to talk about the nature of poetry and those who are concerned with the critical analysis of language. Although both subjects are legitimate topics for philosophical discussion, there are few philosophers who combine a deep and effective interest in aesthetics with a concern in the problems of linguistic analysis. The analytical philosopher is only too ready to relegate poetry to the field (...) of “emotive” meaning; and, although “emotive” is a convenient term for marking off aspects of meaning with which the scientist is not concerned, it is also a means for keeping questions closed which ought to be opened. For it conceals the enormous differences which exist between various nonscientific uses of language. It does this behind an implicit suggestion that, since “emotions” are the province of the psychologist, any sort of inquiry into these uses of language will be merely a psychological inquiry. Poetry, of course, falls into this category of non-scientific uses of language, and a few psycho-analysts have accepted the commitment to discuss it; but on the whole its discussion is left to a very small band of aestheticians and a very large fraternity of literary critics. (shrink)

Of the many possible, and no doubt actual, forms of incoherence covered by my title, I shall be concerned with only one, and must begin by dismissing the others. The incoherence I shall speak of is not any alleged inconsistency between deterministic and indeterministic physical theories , such as between classical particle mechanics and quantum theory. It is an inconsistency internal to determinism. Not, that is, internal to any deterministic theory ; but to the general claims put forward by determinists—whether (...) scientists, philosophers, or laymen. Still another qualification—it is ‘hard’ determinism I shall be concerned with—‘hard’ in the sense in which William James distinguished between hard and soft determinism. Soft determinism James himself ridiculed as glaringly incoherent, and in any case I shall not be specially concerned with determinism as regards human conduct—with the problem of free will and responsibility. (shrink)

It is fashionable nowadays to discredit the theory of the general will, and an attempt to rehabilitate it is not likely to receive much sympathy. Nevertheless, I propose to give some reasons for adopting a more lenient attitude towards the theory, and to indicate some possible lines along which a rehabilitation might be conducted.

There is among many moral philosophers today a renewed emphasis on the connection between reason and morality, and an attempt to exhibit moral behaviour as characteristically rational. What is original in Mr. Kneale's extremely interesting paper is the following-up of a suggestion that certain words like “right,” “wrong,” “ought,” are used in the same way both by lawyers and by moralists; this leads to a logical rehabilitation of the somewhat unpopular concept of the moral law, which in turn argues objectivity (...) and rationality in morals, and makes it possible “to distinguish between good and bad moralists in much the same way as we can distinguish between good and bad lawyers.” The legal pattern is essentially the same as the moral pattern. (shrink)

Current scientific research almost always requires collaboration among several (if not several hundred) specialized researchers. When scientists co-author a journal article, who deserves credit for discoveries or blame for errors? How should scientific institutions promote fruitful collaborations among scientists? In this book, leading philosophers of science address these critical questions.

This text provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as an alternative framework for the epistemology of experiment. It seeks to address the needs of researchers who work with statistical analysis.

Over the past two decades, several consistent procedures have been designed to infer causal conclusions from observational data. We prove that if the true causal network might be an arbitrary, linear Gaussian network or a discrete Bayes network, then every unambiguous causal conclusion produced by a consistent method from non-experimental data is subject to reversal as the sample size increases any finite number of times. That result, called the causal flipping theorem, extends prior results to the effect that causal discovery (...) cannot be reliable on a given sample size. We argue that since repeated flipping of causal conclusions is unavoidable in principle for consistent methods, the best possible discovery methods are consistent methods that retract their earlier conclusions no more than necessary. A series of simulations of various methods across a wide range of sample sizes illustrates concretely both the theorem and the principle of comparing methods in terms of retractions. (shrink)

What is science, and what is it not? Is falsifiability the key to drawing this line? How and why does science work? Should we worry whether science is talking about a "real" world? And should we stop thinking there is a single thing we can call "the scientific method"? With Deborah Mayo, Robert Rynasiewicz, and Drew Arrowood.

Suicide is devastating. It is an assault on our ideas of what living is about. In Contemplating Suicide Gavin Fairbairn takes fresh look at suicidal self harm. His view is distinctive in not emphasising external facts: the presence or absence of a corpse, along with evidence that the person who has become a corpse, intended to do so. It emphasises the intentions that the person had in acting, rather than the consequences that follow from those actions. Much of the book (...) is devoted to an attempt to construct a natural history of suicidal self harm and to examine some of the ethical issues that it raises. Fairbairn sets his philosophical reflections against a background of practical experience in the caring professions and uses a storytelling approach in offering a critique of the current language of self harm along with some new ways of thinking. Among other things he offers cogent reasons for abandoning the mindless use of terms such as attempted suicide and parasuicide , and introduces a number of new terms including cosmic roulette , which he uses to describe a family of human acts in which people gamble with their lives. By elaborating a richer model of suicidal self harm than most philosophers and most practitioners of caring professions currently inhabit, Fairbairn has contributed to the development of understanding in this area. Among other things a richer model and vocabulary may reduce the likelihood that those who come into contact with suicidal self harm, will believe that familiarity with the physical facts of the matter - the actions of the suicider and the presence or absence of a corpse - is always sufficient to justify a definite conclusion about the nature of the self harming act. (shrink)

Although physician‐assisted death can be a great benefit both to those who are terminally ill and those who are not, the risks for patients in these two categories are quite different. For now it is reasonable to make the benefit available only for those near death, and to await better evidence about the risks before making it more broadly available.

In the latter half of the twentieth century, philosophers of science have argued (implicitly and explicitly) that epistemically rational individuals might compose epistemically irrational groups and that, conversely, epistemically rational groups might be composed of epistemically irrational individuals. We call the conjunction of these two claims the Independence Thesis, as they together imply that methodological prescriptions for scientific communities and those for individual scientists might be logically independent of one another. We develop a formal model of scientific inquiry, define four (...) criteria for individual and group epistemic rationality, and then prove that the four definitions diverge, in the sense that individuals will be judged rational when groups are not and vice versa. We conclude by explaining implications of the inconsistency thesis for (i) descriptive history and sociology of science and (ii) normative prescriptions for scientific communities. (shrink)

Jim Joyce argues for two amendments to probabilism. The first is the doctrine that credences are rational, or not, in virtue of their accuracy or “closeness to the truth” (1998). The second is a shift from a numerically precise model of belief to an imprecise model represented by a set of probability functions (2010). We argue that both amendments cannot be satisfied simultaneously. To do so, we employ a (slightly-generalized) impossibility theorem of Seidenfeld, Schervish, and Kadane (2012), who show that (...) there is no strictly proper scoring rule for imprecise probabilities. -/- The question then is what should give way. Joyce, who is well aware of this no-go result, thinks that a quantifiability constraint on epistemic accuracy should be relaxed to accommodate imprecision. We argue instead that another Joycean assumption — called strict immodesty— should be rejected, and we prove a representation theorem that characterizes all “mildly” immodest measures of inaccuracy. (shrink)

Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N–P tests stem from unclarity and confusion, even among N–P adherents, as to how a test's (pre-data) error probabilities are to be used for (post-data) inductive inference as opposed to inductive behavior. We argue that the relevance of error probabilities (...) is to ensure that only statistical hypotheses that have passed severe or probative tests are inferred from the data. The severity criterion supplies a meta-statistical principle for evaluating proposed statistical inferences, avoiding classic fallacies from tests that are overly sensitive, as well as those not sensitive enough to particular errors and discrepancies. Introduction and overview 1.1 Behavioristic and inferential rationales for Neyman–Pearson (N–P) tests 1.2 Severity rationale: induction as severe testing 1.3 Severity as a meta-statistical concept: three required restrictions on the N–P paradigm Error statistical tests from the severity perspective 2.1 N–P test T(): type I, II error probabilities and power 2.2 Specifying test T() using p-values Neyman's post-data use of power 3.1 Neyman: does failure to reject H warrant confirming H? Severe testing as a basic concept for an adequate post-data inference 4.1 The severity interpretation of acceptance (SIA) for test T() 4.2 The fallacy of acceptance (i.e., an insignificant difference): Ms Rosy 4.3 Severity and power Fallacy of rejection: statistical vs. substantive significance 5.1 Taking a rejection of H0 as evidence for a substantive claim or theory 5.2 A statistically significant difference from H0 may fail to indicate a substantively important magnitude 5.3 Principle for the severity interpretation of a rejection (SIR) 5.4 Comparing significant results with different sample sizes in T(): large n problem 5.5 General testing rules for T(), using the severe testing concept The severe testing concept and confidence intervals 6.1 Dualities between one and two-sided intervals and tests 6.2 Avoiding shortcomings of confidence intervals Beyond the N–P paradigm: pure significance, and misspecification tests Concluding comments: have we shown severity to be a basic concept in a N–P philosophy of induction? (shrink)

Modeling and computer simulations, we claim, should be considered core philosophical methods. More precisely, we will defend two theses. First, philosophers should use simulations for many of the same reasons we currently use thought experiments. In fact, simulations are superior to thought experiments in achieving some philosophical goals. Second, devising and coding computational models instill good philosophical habits of mind. Throughout the paper, we respond to the often implicit objection that computer modeling is “not philosophical.”.

While many philosophers of science have accorded special evidential significance to tests whose results are "novel facts", there continues to be disagreement over both the definition of novelty and why it should matter. The view of novelty favored by Giere, Lakatos, Worrall and many others is that of use-novelty: An accordance between evidence e and hypothesis h provides a genuine test of h only if e is not used in h's construction. I argue that what lies behind the intuition that (...) novelty matters is the deeper intuition that severe tests matter. I set out a criterion of severity akin to the notion of a test's power in Neyman-Pearson statistics. I argue that tests which are use-novel may fail to be severe, and tests that are severe may fail to be use-novel. I discuss the 1919 eclipse data as a severe test of Einstein's law of gravity. (shrink)

Although both philosophers and scientists are interested in how to obtain reliable knowledge in the face of error, there is a gap between their perspectives that has been an obstacle to progress. By means of a series of exchanges between the editors and leaders from the philosophy of science, statistics and economics, this volume offers a cumulative introduction connecting problems of traditional philosophy of science to problems of inference in statistical and empirical modelling practice. Philosophers of science and scientific practitioners (...) are challenged to reevaluate the assumptions of their own theories - philosophical or methodological. Practitioners may better appreciate the foundational issues around which their questions revolve and thereby become better 'applied philosophers'. Conversely, new avenues emerge for finally solving recalcitrant philosophical problems of induction, explanation and theory testing. (shrink)

Philosophers of science have long been concerned with the question of what a given scientific theory tells us about the contents of the world, but relatively little attention has been paid to how we set out to build theories and to the relevance of pre-theoretical methodology on a theory’s interpretation. In the traditional view, the form and content of a mature theory can be separated from any tentative ontological assumptions that went into its development. For this reason, the target of (...) interpretation is taken to be the mature theory and nothing more. On this view, positions on ontology emerge only once a theory is to hand, not as part of the process of theory building. Serious attention to theory creation suggests this is too simple. In particular, data collection and experimentation are influenced both by theory and by assumptions about the entities thought to be the target of study. Initial reasoning about possible ontologies has an influence on the choice of theoretical variables as well as on the judgments of the methodology appropriate to investigate them. (shrink)