What could be more certain than the fact that 2 plus 2 equals 4? Since the time of the ancient Greeks mathematicians have believed there is little---if anything---as unequivocal as a proved theorem. In fact, mathematical statements that can be proved true have often been regarded as a more solid foundation for a system of thought than any maxim about morals or even physical objects. The 17th-century German mathematician and philosopher Gottfried Wilhelm Leibniz even envisioned a ``calculus'' of reasoning (...) such that all disputes could one day be settled with the words ``Gentlemen, let us compute!'' By the beginning of this century symbolic logic had progressed to such an extent that the German mathematician David Hilbert declared that all mathematical questions are in principle decidable, and he confidently set out to codify once and for all the methods of mathematical reasoning. (shrink)

Scientific reasoning is—and ought to be—conducted in accordance with the axioms of probability. This Bayesian view—so called because of the central role it accords to a theorem first proved by Thomas Bayes in the late eighteenth ...

Competence in scientific reasoning is one of the most valued outcomes of secondary and higher education. However, there is a need for a deeper understanding of and further research into the roles of domain-general and domain-specific knowledge in such reasoning. This book explores the functions and limitations of domain-general conceptions of reasoning and argumentation, the substantial differences that exist between the disciplines, and the role of domain-specific knowledge and epistemologies. Featuring chapters and commentaries by widely cited (...) experts in the learning sciences, educational psychology, science education, history education, and cognitive science, Scientific Reasoning and Argumentation presents new perspectives on a decades-long debate about the role of domain-specific knowledge and its contribution to the development of more general reasoning abilities. (shrink)

UNDERSTANDING SCIENTIFIC REASONING develops critical reasoning skills and guides students in the improvement of their scientific and technological literacy. The authors teach students how to understand and critically evaluate the scientific information they encounter in both textbooks and the popular media. With its focus on scientific pedagogy, UNDERSTANDING SCIENTIFIC REASONING helps students learn how to examine scientific reports with a reasonable degree of sophistication. The book also explains how to reason through (...) case studies using the same informal logic skills employed by scientists and to analyze a complex series of propositions and hypotheses using sound scientific reasoning. (shrink)

Whereas an inference (deductive as well as inductive) is usually viewed as being valid in virtue of its argument form, the present paper argues that scientific reasoning is material inference, i.e., justified in virtue of its content. A material inference is licensed by the empirical content embodied in the concepts contained in the premises and conclusion. Understanding scientific reasoning as material inference has the advantage of combining different aspects of scientific reasoning, such as confirmation, (...) discovery, and explanation. This approach explains why these different aspects (including discovery) can be rational without conforming to formal schemes, and why scientific reasoning is local, i.e., justified only in certain domains and contingent on particular empirical facts. The notion of material inference also fruitfully interacts with accounts of conceptual change and psychological theories of concepts. (shrink)

Recent public hearings on misconduct charges belie the conjecture that due process will perforce defeat informed scientific reasoning. One notable case that reviewed an obtuse description of experimental methods displays some of the subtleties of differentiating carelessness from intent to deceive. There the decision of a studious nonscientist panel managed to reach sensible conclusions despite conflicting expert testimony. The significance of such a result may be to suggest that to curtail due process would be both objectionable and unproductive.

This book explores the Bayesian approach to the logic and epistemology of scientific reasoning. Section 1 introduces the probability calculus as an appealing generalization of classical logic for uncertain reasoning. Section 2 explores some of the vast terrain of Bayesian epistemology. Three epistemological postulates suggested by Thomas Bayes in his seminal work guide the exploration. This section discusses modern developments and defenses of these postulates as well as some important criticisms and complications that lie in wait for (...) the Bayesian epistemologist. Section 3 applies the formal tools and principles of the first two sections to a handful of topics in the epistemology of scientific reasoning: confirmation, explanatory reasoning, evidential diversity and robustness analysis, hypothesis competition, and Ockham's Razor. (shrink)

Inconsistent representations of the world have in fact played and should play a role in scientific inquiry. However, it would seem that logical analysis of such representations is blocked by the explosive nature of deductive inference from inconsistent premisses. "Paraconsistent logics" have been suggested as the proper way to remove this impediment and to make explication of the logic of inconsistent scientific theories possible. I argue that installing paraconsistent logic as the underlying logic for scientific inquiry is (...) neither a necessary nor a sufficient condition for giving a philosophical alternative, I suggest that identification of heuristic strategies, based on the network of confirming evidence for inconsistent proposals for reasoning from such proposals to their consistent replacements is the proper way to explicate their function in science. (shrink)

Social scientific reasoning (SSR) is essential to social science education and to a democratic society as a whole. Students are challenged to analyze and reason about social problems such as social inequality, crime, and poverty. However, students experience difficulties with SSR. This study addresses the research question: Which design principles can guide teachers in designing lessons that promote social scientific reasoning? In this design-based research, four social science teachers employed a conceptualization of SSR and its levels (...) together with three initial design principles to develop curriculum materials and activities. These design principles and curriculum materials were piloted in two secondary education classes (9th and 10th grades) and evaluated by four social science teachers, four social science teacher educators and 90 students. The study produced six design principles that can promote students’ SSR. In combination with the curriculum materials, those design principles can help develop teachers’ pedagogical content knowledge and guide the design of tasks and units that develop SSR. (shrink)

The purpose of the two studies reported here was to develop an integrated model of the scientific reasoning process. Subjects were placed in a simulated scientific discovery context by first teaching them how to use an electronic device and then asking them to discover how a hitherto unencountered function worked. To do this task, subjects had to formulate hypotheses based on their prior knowledge, conduct experiments, and evaluate the results of their experiments. In the first study, using (...) 20 adult subjects, we identified two main strategies that subjects used to generate new hypotheses. One strategy was to search memory and the other was to generalize from the results of previous experiments. We described the former group as searching an hypothesis space, and the latter as searching an experiment space. In a second study, with 10 adults, we investigated how subjects search the hypothesis space by instructing them to state all the hypotheses that they could think of prior to conducting any experiments. Following this phase, subjects were then allowed to conduct experiments. Subjects who could not think of the correct rule in the hypothesis generation phase discovered the correct rule only by generalizing from the results of experiments in the experimental phase.Both studies provide support for the view that scientific reasoning can be characterized as search in two problem spaces. By extending Simon and Lea's (1974) Generalized Rule Inducer, we present a general model of Scientific Discovery as Dual Search (SDDS) that shows how search in two problem spaces (an hypothesis space and an experiment space) shapes hypothesis generation, experimental design, and the evaluation of hypotheses. The model also shows how these processes interact with each other. Finally, we interpret earlier findings about the psychology of scientific reasoning in terms of the SDDS model. (shrink)

Logical analyses of scientific representations of the world have usually focused on axiomatized or axiomatizable theories. As practiced, science seldom employs such theories. Rather, we find aggregations of claims, the logical relations of which are not as neat as philosophers of science might like them to be. Indeed, a common feature of such aggregations is the presence of certain “theoretical anomalies,” statements that are in some way incompatible with the remainder of the corpus. Huygens’ description of light as exhibiting (...) an asymmetry with respect to its direction of propagation in polarization phenomena was such an anomaly because of its inconsistency with his account of light as longitudinal waves in a medium. (See Sabra 1981, pp. 226-227.) Although the occurrence of these anomalies is indicative of a problem that must be dealt with, inconsistent representations of the world are not without significant heuristic value. (shrink)

C. S. Peirce argued that inductive reasoning and probability judgments are adequately secure only in the indefinitely long run, and that therefore it is illogical to employ these modes of inference unless one's chief devotion is to the interests of an ideal community of all rational beings, past, present, and future. He thought of this devotion as a "social sentiment", involving self-sacrifice. An examination of his argument shows that the attitude presupposed by his conceptions of induction and probability is (...) in fact not self-sacrificial and is social only in a very special sense. Furthermore, it seems doubtful that this attitude is characteristic of practicing scientists; and this is a reason for questioning Peirce's analysis of induction and probability. (shrink)

The purpose of the two studies reported here was to develop an integrated model of the scientific reasoning process. Subjects were placed in a simulated scientific discovery context by first teaching them how to use an electronic device and then asking them to discover how a hitherto unencountered function worked. To do this task, subjects had to formulate hypotheses based on their prior knowledge, conduct experiments, and evaluate the results of their experiments. In the first study, using (...) 20 adult subjects, we identified two main strategies that subjects used to generate new hypotheses. One strategy was to search memory and the other was to generalize from the results of previous experiments. We described the former group as searching an hypothesis space, and the latter as searching an experiment space. In a second study, with 10 adults, we investigated how subjects search the hypothesis space by instructing them to state all the hypotheses that they could think of prior to conducting any experiments. Following this phase, subjects were then allowed to conduct experiments. Subjects who could not think of the correct rule in the hypothesis generation phase discovered the correct rule only by generalizing from the results of experiments in the experimental phase.Both studies provide support for the view that scientific reasoning can be characterized as search in two problem spaces. By extending Simon and Lea's (1974) Generalized Rule Inducer, we present a general model of Scientific Discovery as Dual Search (SDDS) that shows how search in two problem spaces (an hypothesis space and an experiment space) shapes hypothesis generation, experimental design, and the evaluation of hypotheses. The model also shows how these processes interact with each other. Finally, we interpret earlier findings about the psychology of scientific reasoning in terms of the SDDS model. (shrink)

This chapter examines the extent to which there are continuities between the cognitive processes and epistemic practices engaged in by human hunter-gatherers, on the one hand, and those which are distinctive of science, on the other. It deploys anthropological evidence against any form of 'no-continuity' view, drawing especially on the cognitive skills involved in the art of tracking. It also argues against the 'child-as-scientist' accounts put forward by some developmental psychologists, which imply that scientific thinking is present in early (...) infancy and universal amongst humans who have sufficient time and resources to devote to it. In contrast, a modularist kind of 'continuity' account is proposed, according to which the innately channelled architecture of human cognition provides all the materials necessary for basic forms of scientific reasoning in older children and adults, needing only the appropriate sorts of external support, social context, and background beliefs and skills in order for science to begin its advance. (shrink)

A rationalist and realist model of scientific revolutions will be constructed by reference to two categories of criteria of theory-evaluation, denominated indicators of truth and of beauty. Whereas indicators of truth are formulateda priori and thus unite science in the pursuit of verisimilitude, aesthetic criteria are inductive constructs which lag behind the progression of theories in truthlikeness. Revolutions occur when the evaluative divergence between the two categories of criteria proves too wide to be recomposed or overlooked. This model of (...) revolutions depends upon a substantial new treatment of aesthetic criteria in science with which much of the paper will therefore be occupied. (shrink)

Scientific reasoning represents complex argumentation patterns that eventually lead to scientific discoveries. Social epistemology of science provides a perspective on the scientific community as a whole and on its collective knowledge acquisition. Different techniques have been employed with the goal of maximization of scientific knowledge on the group level. These techniques include formal models and computer simulations of scientific reasoning and interaction. Still, these models have tested mainly abstract hypothetical scenarios. The present thesis (...) instead presents data-driven approaches in social epistemology of science. A data-driven approach requires data collection and curation for its further usage, which can include creating empirically calibrated models and simulations of scientific inquiry, performing statistical analyses, or employing data- mining techniques and other procedures. -/- We present and analyze in detail three co-authored research projects on which the thesis’ author was engaged during her PhD. The first project sought to identify optimal team composition in high energy physics laboratories using data-mining techniques. The results of this project are published in (Perović et al. 2016), and indicate that projects with smaller numbers of teams and team members outperform bigger ones. In the second project, we attempted to determine whether there is an epistemic saturation point in experimentation in high energy physics. The initial results from this project are published in (Sikimić et al. 2018). In the thesis, we expand on this topic by using computer simulations to test for biases that could induce scientists to invest in projects beyond their epistemic saturation point. Finally, in previous examples of data-driven analyses, citations are used as a measure of epistemic efficiency of projects in high energy physics. In order to additionally justify and analyze the usage of this parameter in their data-driven research, in the third project Perović & Sikimić (2019) analyzed and compared inductive patterns in experimental physics and biology with the reliability of citation records in these fields. They conclude that while citations are a relatively reliable measure of efficiency in high energy physics research, the same does not hold for the majority of research in experimental biology. -/- Additionally, contributions of the author that are for the first time published in this theses are: (a) an empirically calibrated model of scientific interaction of research groups in biology, (b) a case study of irregular argumentation patterns in some pathogen discoveries, and (c) an introductory discussion of the benefits and limitations of data- driven approaches to the social epistemology of science. Using computer simulations of an empirically calibrated model, we demonstrate that having several levels of hierarchy and division into smaller research sub-teams is epistemically beneficial for researchers in experimental biology. We also show that argumentation analysis in biology represents a good starting point for further data-driven analyses in the field. Finally, we conclude that a data-driven approach is informative and useful for science policy, but requires careful considerations about data collection, curation, and interpretation. (shrink)

Review of David Faust, The Limits of Scientific Reasoning.

A systematic critique of the notion that natural science is the sovereign domain of truth, Critique of Scientific Reason uses an extensive and detailed investigation of physics—and in particular of Einstein's theory of relativity—to argue that the positivistic notion of rationality is not only wrongheaded but false. Kurt Hübner contends that positivism ignores both the historical dimension of science and the basic structures common to scientific theory, myth, and so-called subjective symbolic systems. Moreover, Hübner argues, positivism has led (...) in our time to a widespread disillusionment with science and technology. (shrink)

We contend that diagrams are tools not only for communication but also for supporting the reasoning of biologists. In the mechanistic research that is characteristic of biology, diagrams delineate the phenomenon to be explained, display explanatory relations, and show the organized parts and operations of the mechanism proposed as responsible for the phenomenon. Both phenomenon diagrams and explanatory relations diagrams, employing graphs or other formats, facilitate applying visual processing to the detection of relevant patterns. Mechanism diagrams guide reasoning (...) about how the parts and operations work together to produce the phenomenon and what experiments need to be done to improve on the existing account. We examine how these functions are served by diagrams in circadian rhythm research. (shrink)

The study of human judgment and its limitations is essential to an understanding of the processes involved in the acquisition of scientific knowledge. With that end in mind, David Faust has made the first comprehensive attempt to apply recent research on.

Teaching children to ask and answer questions is critically important if they are to learn to talk and reason effectively together, particularly during inquiry-based science where they are required to investigate topics, consider alternative propositions and hypotheses, and problem-solve together to propose answers, explanations, and prediction to problems at hand. This study involved 108 students (53 boys and 55 girls) from seven, Year 7 teachers’ classrooms in five primary schools in Brisbane, Australia. Teachers were randomly allocated by school to one (...) of two conditions: the metacognitive questioning condition (Trained condition) or the prescriptive questioning condition (Untrained condition). Data on students’ discourse and reasoning and problem-solving (RP-S) were collected across Times 1 and 2. The results showed that while there were significant differences in the discourse categories of the students in the two conditions at Time 1, the only significant difference was in questioning behaviour at Time 2 with the students in the trained condition continuing to ask more questions than their untrained peers. Given that these students had been taught to specifically ask ‘thinking’ questions that probed and interrogated information, these results are not surprising. A follow-up examination of students’ discourse during their small group discussions illustrated how these students interacted with each other to probe and interrogate information by providing explanations and reasons to make their thinking explicit and by using analogies to verbally represent concepts they were trying to express. Results on the follow-up reasoning and problem-solving (RP-S) tasks indicated that students in the Trained and Untrained conditions improved their scores from Time 1 to Time 2 although the change was not significantly different between conditions. (shrink)

This is a philosophical and historical investigation of the role of inconsistent representations of the same scientific phenomenon. The logical difficulties associated with the simultaneous application of inconsistent models are discussed. Internally inconsistent scientific proposals are characterized as structures whose application is necessarily tied to the confirming evidence that each of its components enjoys and to a vision of the general form of the theory that will resolve the inconsistency. Einstein's derivation of the black body radiation law is (...) used as an example application of such a strategy and is contrasted with planck's derivation. (shrink)

More than a hundred years ago, the American philosopher C. S. Peirce suggested the idea of pragmatism as a logical criterion to analyze what words and concepts express through their practical meaning. Many words have been spent on creative processes and reasoning, especially in the case of scientific practices. In fact, philosophers have usually offered a number of ways of construing hypotheses generation, but all aim at demonstrating that the activity of generating hypotheses is paradoxical, illusory or obscure, (...) and thus not analyzable. The “computational turn” gave us a new way to understand creative processes in a strictly pragmatic sense. Artificial Intelligence and Cognitive Science tools allow us to test concepts and ideas previously conceived in abstract terms. It is in the perspective of these actual models that we find the central role of abduction in the explanation of creative reasoning in science. What I call theoretical abduction certainly illustrates much of what is important in abductive reasoning, especially the objective of selecting and creating a set of hypotheses that are able to dispense good explanations of data, but fails to account for many cases of explanation occurring in science or in everyday reasoning when the exploitation of the environment is crucial. The concept of manipulative abduction is devoted to capture the role of action in many interesting situations: action provides otherwise unavailable information that enables the agent to solve problems by starting and performing a suitable abductive process of generation or selection of hypotheses. Many external things, usually inert from the epistemological point of view, can be transformed into what I call epistemic mediators, which are illustrated in the last part of the paper. (shrink)

It is easy to accept that scientific reasoning cannot determine the characteristics of subjective experiences in cases like Broad’s archangel or Jackson’s Mary. The author questions why this seems to be evident and discusses the differences between these cases and ordinary scientific work, where future states of studied systems can be predicted in phenomenal terms. He concludes that important limitations of scientific reasoning are due to the inadequacy of human sensorial apparatus for representing physical reality. (...) Such inadequacies were more evident in Mary’s case, but are always present, and entail the existence of the explanatory gap. (shrink)

This is the first book to focus on science as a social institution based on a comprehensive analysis of the thought of Foucault and Habermas. A key aspect of this book is its standpoint which critiques science, whilst simultaneously interrogating philosophical critique which must in a certain sense accommodate science, and its effect on modernity.

Although there is increasing interest in philosophy of science in transcendental reasoning, there is hardly any discussion about transcendental arguments. Since this might be related to the dominant understanding of transcendental arguments as a tool to defeat epistemological skepticism, and since the power of transcendental arguments to achieve this goal has convincingly been disputed by Barry Stroud, this contribution proposes, first, a new definition of the transcendental argument which allows its presentation in a simple modus ponens and, second, a (...) pragmatist re-interpretation of this argument form that leaves it to the scientific community to debate, criticize, refine, or reaffirm its core claim: a premise which claims that the truth of a certain assumption is a necessary condition for something that is generally accepted. The proposed “logico-pragmatist interpretation” highlights the role of transcendental arguments as a methodological step to move science forward, just as abduction and inference to the best explanation do. (shrink)

Although there is increasing interest in philosophy of science in transcendental reasoning, there is hardly any discussion about transcendental arguments. Since this might be related to the dominant understanding of transcendental arguments as a tool to defeat epistemological skepticism, and since the power of transcendental arguments to achieve this goal has convincingly been disputed by Barry Stroud, this contribution proposes, first, a new definition of the transcendental argument which allows its presentation in a simple modus ponens and, second, a (...) pragmatist re-interpretation of this argument form that leaves it to the scientific community to debate, criticize, refine, or reaffirm its core claim: a premise which claims that the truth of a certain assumption is a necessary condition for something that is generally accepted. The proposed “logico-pragmatist interpretation” highlights the role of transcendental arguments as a methodological step to move science forward, just as abduction and inference to the best explanation do. (shrink)

Da Costa and French explore the consequences of adopting a 'pragmatic' notion of truth in the philosophy of science. Their framework sheds new light on issues to do with belief, theory acceptance, and the realism-antirealism debate, as well as the nature of scientific models and their heuristic development.

The thesis of underdetermination of theory by evidence has led to an opposition between realism and relationism in philosophy of science. Various forms of the thesis are examined, and it is concluded that it is true in at least a weak form that brings realism into doubt. Realists therefore need, among other things, a theory of degrees of confirmation to support rational theory choice. Recent such theories due to Glymour and Friedman are examined, and it is argued that their criterion (...) of "unification" for good theories is better formulated in Bayesian terms. Bayesian confirmation does, however, have consequences that tell against realism. It is concluded that the prospects are dim for scientific realism as usually understood. (shrink)