Robert Batterman examines a form of scientific reasoning called asymptotic reasoning, arguing that it has important consequences for our understanding of the scientific process as a whole. He maintains that asymptotic reasoning is essential for explaining what physicists call universal behavior. With clarity and rigor, he simplifies complex questions about universal behavior, demonstrating a profound understanding of the underlying structures that ground them. This book introduces a valuable new method that is certain to fill explanatory gaps across disciplines.

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)

Starting with some illustrative examples, I develop a systematic account of a specific type of experimentation--an experimentation which is not, as in the "standard view", driven by specific theories. It is typically practiced in periods in which no theory or--even more fundamentally--no conceptual framework is readily available. I call it exploratory experimentation and I explicate its systematic guidelines. From the historical examples I argue furthermore that exploratory experimentation may have an immense, but hitherto widely neglected, epistemic significance.

In seeking general accounts of evidence, confirmation, or inference, philosophers have looked to logical relationships between evidence and hypotheses. Such logics of evidential relationship, whether hypothetico-deductive, Bayesian, or instantiationist fail to capture or be relevant to scientific practice. They require information that scientists do not generally have (e.g., an exhaustive set of hypotheses), while lacking slots within which to include considerations to which scientists regularly appeal (e.g., error probabilities). Building on my co-symposiasts contributions, I suggest some directions in which a (...) new and more adequate philosophy of evidence can move. (shrink)

It is shown how the development of physics has involved making explicit what were homocentric projections which had heretofore been implicit, indeed inexpressible in theory. This is shown to support a particular notion of the invariant as the real. On this basis the divergence in ideals of physical intelligibility between Bohr and Einstein is set out. This in turn leads to divergent, but explicit, conceptions of objectivity and completeness for physical theory. *I am indebted to Dr. G. McLelland. Professor F. (...) Rohrlich and an anonymous referee of this journal for several improvements in the formulation of the paper. (shrink)

This article focuses on the problem of representational content. Accounting for representational content is the central issue in contemporary naturalism: it is the major remaining task facing a naturalistic conception of the world. Representational content is also the central barrier to contemporary cognitive science and artificial intelligence: it is not possible to understand representation in animals nor to construct machines with genuine representation given current (lack of) understanding of what representation is. An elaborated critique is offered to current approaches to (...) representation, arguing that the basic underlying approach is, at root, logically incoherent, and, thus, that standard approaches are doomed to failure. An alternative model of representation - interactivism - is presented that avoids or solves the problems facing standard approaches. Interactivism is framed by a version of functionalism, and a naturalization of that functionalism completes an outline of a naturalization of representation and representational content. (shrink)

No one has a well developed solution to Duhem's problem, the problem of how experimental evidence warrants revision of our theories. Deborah Mayo proposes a solution to Duhem's problem in route to her more ambitious program of providing a philosophical account of inductive inference and experimental knowledge. This paper is a response to Mayo's Error Statistics (ES) program, paying particular attention to her response to Duhem's problem. It turns out that Mayo's purported solution to Duhem's problem is very significant to (...) her project, for the epistemic license claimed by ES and the philosophical underpinnings to her account of experimental knowledge depend on this solution. By introducing the partition problem, I argue that ES fails to solve Duhem's problem and therefore fails to provide an adequate account of experimental knowledge. (shrink)

This paper discusses a series of case studies on observations, experiments, and the theoretical interpretation between 1890 and 1960 of a shift of dark Fraunhofer lines in the solar spectrum. I argue for the use of flow charts to analyze interconnections and to identify sequences of research strategies. Also I advocate using a newly-developed tool called "block diagram" representation of experimental systems as an appropriate method to identify recurrent patterns in the interplay of instrumentation, experiment, and theory in research episodes.

Donald Campbell has long advocated a naturalist epistemology based on a general selection theory, with the scope of knowledge restricted to vicarious adaptive processes. But being a vicariant is problematic because it involves an unexplained epistemic relation. We argue that this relation is to be explicated organizationally in terms of the regulation of behavior and internal state by the vicariant, but that Campbell's selectionist approach can give no satisfactory account of it because it is opaque to organization. We show how (...) organizational constraints and capacities are crucial to understanding both evolution and cognition and conclude with a proposal for an enriched, generalized model of evolutionary epistemology that places high-order regulatory organization at the center. (shrink)

: The purpose of this paper and its sister paper (Farrell and Hooker, b) is to present, evaluate and elaborate a proposed new model for the process of scientific development: self-directed anticipative learning (SDAL). The vehicle for its evaluation is a new analysis of a well-known historical episode: the development of ape-language research. In this first paper we outline five prominent features of SDAL that will need to be realized in applying SDAL to science: 1) interactive exploration of possibility space; (...) 2) self-directedness; 3) localization of success and error; 4) Synergistic increase in learning capacity; and 5) continuity of SDAL process across scientific change. In this paper we examine the first three features of SDAL in relation to the early history of ape-language research. We show that this history is readily explicated as a self-directed, ever-finer, delineation of possibility space that enables the localization of both success and error. Paper II examines the last two features against this history. (shrink)

: The purpose of this paper and its sister paper I (Farrell and Hooker, a) is to present, evaluate and elaborate a proposed new model for the process of scientific development: self-directed anticipative learning. The vehicle for its evaluation is a new analysis of a well-known historical episode: the development of ape language research. Paper I examined the basic features of SDAL in relation to the early history of ape-language research. In this second paper we examine the reconceptualization of ape-language (...) research following what many conceived to be Terrace's refutation of ape-language. We show that the apparent 'revolution' in our understanding of ape linguistic capacities was not based upon 'revolutionary' research different in kind from 'normal' research. The same processes of self-directed interactive exploration of possibility space, that enables a homing-in upon both error and success, is present in all phases of productive science. Moreover, conceiving science as an SDAL process explains how scientists learn how to learn about their research domain. (shrink)

This paper explores how data serve as evidence for phenomena. In contrast to standard philosophical models which invite us to think of evidential relationships as logical relationships, I argue that evidential relationships in the context of data-to-phenomena reasoning are empirical relationships that depend on holding the right sort of pattern of counterfactual dependence between the data and the conclusions investigators reach on the phenomena themselves.

Contrary to the Empiricist model of science, successful sufficiently fundamental theories not only fit and unify their data fields but also prescribe the general terms in which relevantly to describe observation; specify what is and is not observable; specify the conditions under which what is observable, is observable; specify the instrumental means and reliability by which what is measurable is measured; specify what is causally, statistically, and merely accidentally connected. Moreover, such theories typically require all or most of the entire (...) remainder of science to be properly applied in any given situation and theoreticians' models play a crucial role in such applications. (I call these respectively the internal and external globalnesses of theories.) A discussion of the consequences of these global features of theories for philosophy of science is offered in the context of specific examples and a structural model for science. (shrink)

This paper argues, first, that recent studies of experimentation, most notably by Deborah Mayo, provide the conceptual resources to describe scientific discovery's early stages as error-probing processes. Second, it shows that this description yields greater understanding of those early stages, including the challenges that they pose, the research strategies associated with them, and their influence on the rest of the discovery process. Throughout, the paper examines the phenomenon of "chemical hormesis" (i.e., anomalous low-dose effects from toxic chemicals) as a case (...) study that is important not only for the biological sciences but also for contemporary public policy. The resulting analysis is significant for at least two reasons. First, by elucidating the importance of discovery's earliest stages, it expands previous accounts by philosophers such as William Wimsatt and Lindley Darden. Second, it identifies the discovery process as yet another philosophical topic on which the detailed studies of the "new experimentalists" can shed new light. (shrink)

This essay is concerned with the epistemic roles of error in scientific practice. Usually, error is regarded as something negative, as an impediment or obstacle for the advancement of science. However, we also frequently say that we are learning from error. This common expression suggests that the role of error is not—at least not always—negative but that errors can make a fruitful contribution to the scientific enterprise. My paper explores the latter possibility. Can errors play an epistemically productive role in (...) scientific research? The paper begins with a review of several twentieth-century approaches to error and the various agendas behind them. It is shown that only very few scholars have considered whether errors can be productive. The main part of the paper examines a concrete debate in early nineteenth-century microscopy and analyses how the microscopists coped with the problem of error. Drawing on this material, the article offers some terminological clarifications of the common notion ‘learning from error’. The conclusion argues that error can indeed play epistemically productive roles in scientific practice.Keywords: Error; Nineteenth-century microscopic anatomy. (shrink)

All the major inter-theoretic relations of fundamental science are asymptotic ones, e.g. quantum theory as Planck's constant h 0, yielding (roughly) Newtonian mechanics. Thus asymptotics ultimately grounds claims about inter-theoretic explanation, reduction and emergence. This paper examines four recent, central claims by Batterman concerning asymptotics and reduction. While these claims are criticised, the discussion is used to develop an enriched, dynamically-based account of reduction and emergence, to show its capacity to illuminate the complex variety of inter-theory relationships in physics, and (...) to provide a principled resolution to such persistent philosophical problems as multiple realisability and the nature of the special sciences. Introduction Exposition Examination I: Claims (1) and (2), asymptotic explanation and reference Examination II: Claim (3), reduction and singular asymptotics Examination III: Claim (4), emergence and multiple realisability Conclusion. (shrink)

Robert Nola has argued that anti-rationalist interpretations of science fail to adequately explain the process of science, since objective reasons can be causal factors in belief formation. While I agree with Nola that objective reasons can be a cause of belief, in this paper I present a version of the strong programme in the sociology of knowledge, the Interests Thesis, and argue that the Interests Thesis provides a plausible explanation of an episode in the history of ape-language research. Specifically, I (...) examine Terrace, Petitto, Sandess, & Bever illegitimate comparison of the signing of their chimpanzee, Nim, with data from human early childhood language development, and argue that Terrace et al.’s interests played a causal role in determining their sceptical beliefs concerning ape linguistic abilities. However, I go on to argue that Terrace et al.’s interests are not the only causal factors in determining their beliefs: objective reasons, associated with the institution of new methodologies, were also causally determinative of Terrace et al.’s sceptical beliefs. Consequently, I argue that belief formation in science is a multi-factorial affair wherein both interests and objective reasons have causal roles. I finish the paper with two conjectures concerning the proper locus of scientific rationality. (shrink)

The role of interaction in learning is essential and profound: it must provide the means to solve open problems (those only vaguely specified in advance), but cannot be captured using our familiar formal cognitive tools. This presents an impasse to those confined to present formalisms; but interaction is fundamentally dynamical, not formal, and with its importance thus underlined it invites the development of a distinctively interactivist account of life and mind. This account is provided, from its roots in the interactivist (...) biological constitution of life, through the evolution of the dual internal regulatory capacities expressed as intentionality and intelligence, to its expression in self-directed anticipative learning in persons and in science. (shrink)

This paper outlines an original interactivist-constructivist approach to modelling intelligence and learning as a dynamical embodied form of adaptiveness and explores some applications of I-C to understanding the way cognitive learning is realized in the brain. Two key ideas for conceptualizing intelligence within this framework are developed. These are: intelligence is centrally concerned with the capacity for coherent, context-sensitive, self-directed management of interaction; and the primary model for cognitive learning is anticipative skill construction. Self-directedness is a capacity for integrative process (...) modulation which allows a system to "steer" itself through its world by anticipatively matching its own viability requirements to interaction with its environment. Because the adaptive interaction processes required of intelligent systems are too complex for effective action to be prespecified learning is an important component of intelligence. A model of self-directed anticipative learning is formulated based on interactive skill construction, and argued to constitute a central constructivist process involved in cognitive development. SDAL illuminates the capacity of intelligent learners to start with the vague, poorly defined problems typically posed in realistic learning situations and progressively refine them, transforming them into problems with sufficient structure to guide the construction of a solution. Finally, some of the implications of I-C for modelling of the neuronal basis of intelligence and learning are explored; in particular, Quartz and Sejnowski's recent neural constructivism paradigm, enriched by Montague and Sejnowski's dopaminergic model of anticipative-predictive neural learning, is assessed as a promising, but incomplete, contribution to this approach. The paper concludes with a fourfold reflection on the divergence in cognitive modelling philosophy between the I-C and the traditional computational information processing approaches. (shrink)