The 20th century philosophy of science began on a positivistic note. Its focal point was scientific explanation and the hypothetico-deductive (HD) framework of explanation was proposed as the standard of what is meant by “science.” HD framework, its inductive and statistical variants, and other logic-based approaches to modeling scientific explanation were developed long before the dawn of the information age. Since that time, the volume of observational data and power of high performance computing have increased by several orders of magnitude (...) and reshaped the practice and concept of science, and indeed, the philosophy of science. A new observational-inductive (OI) framework for scientific research is emerging due to recent developments in sensors, data systems, computers, and knowledge discovery techniques. We examine the nature of these changes and their impact on the question of what is meant by “science” after discussing five examples of the OI framework, and conclude that the HD and OI frameworks are complementary and synergistic. (shrink)

This thesis offers a naturalist revision of Alain Badiou’s philosophy. This goal is pursued through an encounter of Badiou’s mathematical ontology and theory of truth with contemporary trends in philosophy of mathematics and philosophy of science. I take issue with Badiou’s inability to elucidate the link between the empirical and the ontological, and his residual reliance on a Heideggerian project of fundamental ontology, which undermines his own immanentist principles. I will argue for both a bottom-up naturalisation of Badiou’s philosophical approach (...) to mathematics and a top-down naturalisation. Articulating my particular understanding of what realism and naturalism should commit us to, I propose a creative fusion of Badiou’s attention to metamathematical results with a structural-informational metaphysics, proposing a ‘matherialism’ uniting the more daring speculative insights of the former with the naturalist and empiricist commitments motivating the latter. (shrink)

In this paper, a case study is conducted to test the capability of the Carneades Argumentation System to model the argumentation in a case where forensic evidence was collected in an investigation triggered by a conflict among art experts on the attribution of a painting to Leonardo da Vinci. A claim that a portrait of a young woman in a Renaissance dress could be attributed to da Vinci was initially dismissed by art experts. Forensic investigations were carried out, and evidence (...) was collected by art history experts and scientific experts. The expert opinions were initially in conflict, but new evidence shifted the burden of proof onto the side of the skeptics. This paper presents an analysis of the structure of the interlocking argumentation in the case using argument mapping tools to track the accumulation of evidence pro and con. (shrink)

A potential explanation of a fact is a hypothesis such that, if it were true, it would explain the fact in question. Let’s suppose that we become aware of a fact and some potential explanations thereof. Let’s also suppose that we would like to believe the truth. Given this aim, we can ask two questions. First, is it likely that one of these hypotheses is true? Second, given an affirmative answer to the first question, which one is it likely to (...) be? Inference to the best explanation (IBE) offers answers to both questions. To the first, it says ‘Yes’—assuming that at least one of the hypotheses would, if true, provide a satisfactory explanation of the fact under consideration. To the second, it says that the hypothesis most likely to be true is the one that scores best on the explanatory virtues: conservatism, modesty, simplicity, generality, and predictive power. Many philosophers have argued against IBE’s answer to the first question. I am interested in an objection to its answer to the second. Many philosophers seem to think that it is unsustainable: they seem to think that even if we assume that one of the competing hypotheses is true, we should not think that IBE will help us to identify it. Or, more carefully, if these philosophers are doing what they appear to be doing—namely, offering critiques of IBE that don’t depend on assumptions about the field of competing hypotheses—then their claim is that IBE will not help us to identify the truth. I believe that this is mistaken: the argument for believing it assumes a model of IBE that we have no reason to accept. (shrink)

This article concerns the structure of defeasible arguments like: 'If Bob has red spots, Bob has the measles; Bob has red spots; therefore Bob has the measles.' The issue is whether such arguments have the form of modus ponens or not. Either way there is a problem. If they don't have the form of modus ponens, the common opinion to the contrary taught in leading logic textbooks is wrong. But if they do have the form of modus ponens, doubts are (...) raised about the conventional dogma that all arguments having the form of modus ponens are deductively valid. By carefully examining arguments on both sides of the issue, reasonable doubts are raised about the view that all arguments having a modus ponens form are valid. (shrink)

Abduction and metaphor are two significant concepts in cognitive science. It is found that the both mental processes are on the basis of certain similarity. The similarity inspires us to seek the answers to the following two questions: Whether there is a common cognitive mechanism behind abduction and metaphor? And if there is, whether this common mechanism could be interpreted within the unified frame of modern intelligence theory? Centering on these two issues, the paper attempts to characterize and interpret the (...) generation and evolution of scientific metaphors from the perspective of the cognitive mechanism of abductive inference. Then it interprets the common cognitive mechanism behind abduction and metaphor within Hawkins’ frame of intelligence theory. The commonality between abduction and metaphor indicates the potential to further explore human intelligence. (shrink)

This study aims to understand scientific inference for the evolutionary procedure of Continental Drift based on abductive inference, which is important for creative inference and scientific discovery during problem solving. We present the following two research problems: (1) we suggest a scientific inference procedure as well as various strategies and a criterion for choosing hypotheses over other competing or previous hypotheses; aspects of this procedure include puzzling observation, abduction, retroduction, updating, deduction, induction, and recycle; and (2) we analyze the “theory (...) of continental drift” discovery, called the Earth science revolution, using our multistage inference procedure. Wegener’s Continental Drift hypothesis had an impact comparable to the revolution caused by Darwin’s theory of evolution in biology. Finally, the suggested inquiry inference model can provide us with a more consistent view of science and promote a deeper understanding of scientific concepts. (shrink)

In multicausal abductive tasks a person must explain some findings by assembling a composite hypothesis that consists of one or more elementary hypotheses. If there are n elementary hypotheses, there can be up to 2n composite hypotheses. To constrain the search for hypotheses to explain a new observation, people sometimes use their current explanation—the previous evidence and their present composite hypothesis of that evidence; however, it is unclear when and how the current explanation is used. In addition, although a person's (...) current explanation can narrow the search for a hypothesis, it can also blind the problem solver to alternative, possibly better, explanations. This paper describes a model of multicausal abductive reasoning that makes two predictions regarding the use of the current explanation. The first prediction is that the current explanation is not used to explain new evidence if there is a simple (i.e., nondisjunctive, concrete) hypothesis to account for that evidence. The second prediction is that the current explanation is used when attempting to discriminate among several alternative hypotheses for new evidence. These hypotheses were tested in three experiments. The results are consistent with the second prediction: the current explanation is used when discriminating among alternative hypotheses. However, the first prediction—that the current explanation is not used when a simple hypothesis can account for new data—received only limited support. Participants used the current explanation to constrain their interpretation of new data in 46.5% of all trials. This suggests that context‐independent strategies compete with context‐dependent ones—an interpretation that is consistent with recent work on strategy selection during problem solving. (shrink)

Collaborative learning with cases characteristically involves discussing and developing shared explanations. We investigated the argumentation scheme which learners use in constructing shared explanations over evidence. We observed medical students attempting to explain how a judge had arrived at his verdict in a case of medical negligence. The students were learning within a virtual learning environment and their communication was computer mediated. We identify the dialogue type that these learners construct and show that their argumentation conforms with an abductive form of (...) argumentation scheme (‘inference to the best explanation’). We also assessed the students’ learning and propose that it is related to particular features of this argumentation scheme. (shrink)

The sciences use a wide range of visual devices, practices, and imaging technologies. This diversity points to an important repertoire of visual methods that scientists use to adapt representations to meet the varied demands that their work places on cognitive processes. This paper identifies key features of the use of visualization in a range of scientific domains and considers the implications of this repertoire for understanding scientists as cognitive agents.

In this article, we argue for the general importance of normative theories of argument strength. We also provide some evidence based on our recent work on the fallacies as to why Bayesian probability might, in fact, be able to supply such an account. In the remainder of the article we discuss the general characteristics that make a specifically Bayesian approach desirable, and critically evaluate putative flaws of Bayesian probability that have been raised in the argumentation literature.

This article presents a formal theory of robot perception as a form of abduction. The theory pins down the process whereby low‐level sensor data is transformed into a symbolic representation of the external world, drawing together aspects such as incompleteness, top‐down information flow, active perception, attention, and sensor fusion in a unifying framework. In addition, a number of themes are identified that are common to both the engineer concerned with developing a rigorous theory of perception, such as the one on (...) offer here, and the philosopher of mind who is exercised by questions relating to mental representation and intentionality. (shrink)

This paper describes an abductive process model of anomalous data integration. The model makes use of the entrenchment of the current explanation and the probability of alternative explanations. It is hypothesised that increasing confirmation of the anom-aly itself increases the probability of alternative explanations. In an experimental study we found that both the entrenchment of an existing explanation and confirmation of the anomaly clearly influence how people resolve anomalous data. These results are in agreement with the predic-tions of the model.

Argumentation schemes are forms of reasoning that are fallible but correctable within a self-correcting framework. Their use provides a basis for taking rational action or for reasonably accepting a conclusion as a tentative hypothesis, but they are not deductively valid. We argue that teleological reasoning can provide the basis for justifying the use of argument schemes both in monological and dialogical reasoning. We consider how such a teleological justification, besides being inspired by the aim of directing a bounded cognizer to (...) true belief and correct choices, needs to take into account the attitudes of dialogue partners as well as normative models of dialogue and communicative activity types, in particular social and cultural settings. (shrink)

This article generalizes the explanationist account of inference to the best explanation. It draws a clear distinction between IBE and abduction and presents abduction as the first step of IBE. The second step amounts to the evaluation of explanatory power, which consist in the degree of explanatory virtues that a hypothesis exhibits. Moreover, even though coherence is the most often cited explanatory virtue, on pain of circularity, it should not be treated as one of the explanatory virtues. Rather, coherence should (...) be equated with explanatory power and considered to be derivable from the other explanatory virtues: unification, explanatory depth and simplicity. (shrink)

Abduction and metaphor are two significant concepts in cognitive science. It is found that the both mental processes are on the basis of certain similarity. The similarity inspires us to seek the answers to the following two questions: (1) Whether there is a common cognitive mechanism behind abduction and metaphor? And (2) if there is, whether this common mechanism could be interpreted within the unified frame of modern intelligence theory? Centering on these two issues, the paper attempts to characterize and (...) interpret the generation and evolution of scientific metaphors from the perspective of the cognitive mechanism of abductive inference. Then it interprets the common cognitive mechanism behind abduction and metaphor within Hawkins’ frame of intelligence theory. The commonality between abduction and metaphor indicates the potential to further explore human intelligence. (shrink)

This book is a sustained defense of the compatibility of the presence of idealizations in the sciences and scientific realism. So, the book is essentially a detailed response to the infamous arguments raised by Nancy Cartwright to the effect that idealization and scientific realism are incompatible.

According to the demand of the Hypothetico-Deductive (H-D) method, a theory is confirmed when the prediction-observation (p-o) gap is small and disconfirmed when the gap is large. A major goal of this paper is to introduce a research domain for which this demand does not hold. In contrast to the H-D method's demand, this research, called the Catch model for reconstructing a face previously seen from memory, requires an increase, within limits, in the p-o gap. The Catch model research substantiates (...) theoretically and empirically a new proposed method that I call the "Deductive-Reconstruction" (D-R) method. This method provides essential conditions whose fulfillment guarantees successful reconstruction of past events (a face previously seen) from memory. It is argued that the D-R method fits the area of research of reconstructing past events from memory better than the H-D method. Application of the H-D method to the Catch model's research domain leads to an internal contradiction and failure to reconstruct past events (a face previously seen) from memory. Finally, the nature of the D-R method along with the Catch model is discussed from three points of view: confirmation, explanation, and generality. (shrink)

This research was carried out to explore some of the cognitive processes involved in scientific anomalies resolution. 40 subjects with a good neuropsychology expertise were asked to explain two (invented) anomalous neuropsychological cases. The subjects' efforts to give a meaningful structure to the data were recorded, and the resulting reasoning blocks were analysed to extract and compute the inferential (deductive, inductive and abductive) and analogical processes used. The processes were intercorrelated to experimentally verify the co-occurrence of different forms of logical (...) thinking. Statistical analysis point out the relevance of abductive inferences, the possible presence of an inferential-style switching process , the high number of external analogies used, the cognitive closeness manifested by expert reasoners. (shrink)

Why do people get sick? I argue that a disease explanation is best thought of as causal network instantiation, where a causal network describes the interrelations among multiple factors, and instantiation consists of observational or hypothetical assignment of factors to the patient whose disease is being explained. This paper first discusses inference from correlation to causation, integrating recent psychological discussions of causal reasoning with epidemiological approaches to understanding disease causation, particularly concerning ulcers and lung cancer. It then shows how causal (...) mechanisms represented by causal networks can contribute to reasoning involving correlation and causation. The understanding of causation and causal mechanisms provides the basis for a presentation of the causal network instantiation model of medical explanation. (shrink)

In his influential paper, 'Why Was the Logic of Discovery Abandoned?', Laudan contends that there has been no philosophical rationale for a logic of discovery since the emergence of consequentialism in the 19th century. It is the purpose of this paper to show that consequentialism does not involve the rejection of all types of logic of discovery. Laudan goes too far in his interpretation of the historical shift from generativism to consequentialism, and his claim that the context of pursuit belongs (...) to neither discovery nor justification is based on narrow interpretations of the contexts of discovery and justification. As a result, Laudan draws unwarranted conclusions concerning both the early and contemporary defenders of a logic of discovery. A methodological logic of discovery - which involves self-corrective methods of hypothesis generation that promote the long-term goals of science and which require consequential support for justification - is a type of logic of discovery that survives the shift to consequentialism. (shrink)

This article describes abductions as special patterns of inference to the best explanation whose structure determines a particularly promising abductive conjecture and thus serves as an abductive search strategy. A classification of different patterns of abduction is provided which intends to be as complete as possible. An important distinction is that between selective abductions, which choose an optimal candidate from given multitude of possible explanations, and creative abductions, which introduce new theoretical models or concepts. While selective abduction has dominated the (...) literature, creative abductions are rarely discussed, although they are essential in science. The article introduces several kinds of creative abductions, such as theoretical model abduction, common cause abduction and statistical factor analysis, and illustrates them by various real case examples. It is suggested to demarcate scientifically fruitful abductions from purely speculative abductions by the criterion of causal unification. (shrink)

This paper reviews the psychological investigation of reasoning with conditionals, putting an emphasis on recent work. In the first part, a few methodological remarks are presented. In the second part, the main theories of deductive reasoning (mental rules, mental models, and the probabilistic approach) are considered in turn; their content is summarised and the semantics they assume for if and the way they explain formal conditional reasoning are discussed, in particular in the light of experimental work on the probability of (...) conditionals. The last part presents the recent shift of interest towards the study of conditional reasoning in context, that is, with large knowledge bases and uncertain premises. (shrink)

Taking as its exegetic point of departure Peirce's outline of a semiotic theory of cognition from the mid 1890s, this paper explores the relevance of this outline to a theory of learning and also to a broader, normative vision of education. Firstly, besides providing for fallibilism in philosophical inquiry Peirce's outline accords with critical strategies of his fellow pragmatists, such as William James's detection of the ‘psychologist's fallacy’ and John Dewey's rejection of the ‘philosophical fallacy’. It is pointed out that (...) this merit of Peirce's outline is due to his abstract and often neglected notion of a prespecialised scientific intelligence. Secondly, the paper shows that Peirce's semiotic approach to prescientific learning processes conditioning all kinds of theoretical activity may indeed complement James's and Dewey's contributions: giving careful attention to the work of linguistic and paralinguistic signs in such learning processes Peirce's analysis captures indexical and iconic aspects, as well as symbolic aspects of linguistic interaction and experientially constrained dialogue. Following Peirce's analysis the paper considers how the iconicity of linguistic structure, ostensive acts and the practical situatedness of language use are pre‐conditions of experiential learning in every day life as well as in scientific learning. It is also shown how this semiotic analysis is connected with Peirce's important notion of abduction. Finally, it is pointed out that a broader vision of education may issue from Peirce's semiotic analysis and that such a vision would emphasise that the life experience of individuals entering educational institutions should be made to bear on the objectives as well as the teaching practices of educational institutions. (shrink)

Peirce wrote that Hume’s argument against miracles (which is generally liked by twentieth century philosophers for its antireligious conclusion) "completely misunderstood the true nature of" ’abduction’. This paper argues that if Hume’s argumentative strategy were seriously used in all situations (not just those in which we seek to "banish superstition"), it would deliver a choking epistemological conservatism. It suggests that some morals for contemporary naturalistic philosophy may be drawn from Peirce’s argument against Hume.

Non-empirical principles have always been a subject of interest of philoso-phers. Authors from different times and traditions agree that principles such as analogy or simplicity are present in the scientific practice. The disagreement comes out when these authors affirm that these principles have an epistemic function, and when they try to present reasons in order to found this state-ment. The first goal of this paper is to describe these principles and to point out their methodological importance. The second goal is (...) to introduce the discussion between the two most important models of philosophical fundamentation – it means, the logicism and the psychologism. The last goal – the main one – is to defend that the non-empirical principals have, legitimately, an epistemic role in the scientific investigation. (shrink)

Abduction is or subsumes a process of inference. It entertains possible hypotheses and it chooses hypotheses for further scrutiny. There is a large literature on various aspects of non-symbolic, subconscious abduction. There is also a very active research community working on the symbolic (logical) characterisation of abduction, which typically treats it as a form of hypothetico-deductive reasoning. In this paper we start to bridge the gap between the symbolic and sub-symbolic approaches to abduction. We are interested in benefiting from developments (...) made by each community. In particular, we are interested in the ability of non-symbolic systems (neural networks) to learn from experience using efficient algorithms and to perform massively parallel computations of alternative abductive explanations. At the same time, we would like to benefit from the rigour and semantic clarity of symbolic logic. We present two approaches to dealing with abduction in neural networks. One of them uses Connectionist Modal Logic and a translation of Horn clauses into modal clauses to come up with a neural network ensemble that computes abductive explanations in a top-down fashion. The other combines neural-symbolic systems and abductive logic programming and proposes a neural architecture which performs a more systematic, bottom-up computation of alternative abductive explanations. Both approaches employ standard neural network architectures which are already known to be highly effective in practical learning applications. Differently from previous work in the area, our aim is to promote the integration of reasoning and learning in a way that the neural network provides the machinery for cognitive computation, inductive learning and hypothetical reasoning, while logic provides the rigour and explanation capability to the systems, facilitating the interaction with the outside world. Although it is left as future work to determine whether the structure of one of the proposed approaches is more amenable to learning than the other, we hope to have contributed to the development of the area by approaching it from the perspective of symbolic and sub-symbolic integration. (shrink)

The question of whether or not to partition data for the purposes of inferring phylogenetic hypotheses remains controversial. Opinions have been especially divided since Kluge's (1989, Systematic Zoology 38, 7–25) claim that data partitioning violates the requirement of total evidence (RTE). Unfortunately, advocacy for or against the RTE has not been based on accurate portrayals of the requirement. The RTE is a basic maxim for non-deductive inference, stipulating that evidence must be considered if it has relevance to an inference. Evidence (...) is relevant if it has a positive or negative effect on a given conclusion. In the case of ℈partitioned’ phylogenetic inferences, the RTE is violated, and the basis for rational belief in any conclusion is compromised, unless it is shown that the partitions are evidentially irrelevant to one another. The goal of phylogenetic systematics is to hypothesize past causal conditions to account for observed shared similarities among two or more species. Such inferences are non-deductive, necessitating consideration of the RTE. Some phylogeneticists claim the parsimony criterion as justification for the RTE. There is no relation between the two – parsimony is a relation between a hypothesis and causal question(s). Parsimony does not dictate the content of premises prior to an inference. ℈Taxonomic congruence,’ ℈supertrees,’ and ℈conditional combination’ methods violate the RTE. Taxonomic congruence and supertree methods also fail to achieve the intended goal of phylogenetic inference, such that ℈consensus trees’ and ℈supertrees’ lack an empirical basis. ℈Conditional combination’ is problematic because hypotheses derived from partitioned data cannot be compared – a causal hypothesis inferred to account for a set of effects only has relevance to those effects, not any comparative relevance to other causal hypotheses. A similar problem arises in the comparisons of hypotheses derived from different causal theories. (shrink)

The aim of this paper is to explore the conditions under which generalisation from single case studies, in the sense of making inferences concerning a wider class of phenomena beyond a case, is reasonable. Two sets of conditions, in particular, provide the basis for our consideration of this issue. The first is an exploration of the impressive amount of empirical knowledge that is contained within the theories that are used to make observations, to classify phenomena, and to understand and interpret (...) cases. The second is recognition of the role of a pattern of inference known as abduction, or inference to the best explanation, in drawing conclusions from case studies. It is argued that these two conditions, when applied to the procedures for conducting case studies, can be used to defend, as reasonable, the making of certain provisional generalisations from single cases. An epistemological procedure for testing and further justifying these generalisations, with a particular emphasis on dealing with the problem of confirmation bias, is then discussed and recommended. (shrink)

Recent developments in the cognitive sciences and artificial intelligence suggest ways of answering the most serious challenge to Peirce's notion of abduction. Either there is no such logical process as abduction or, if abduction is a form of inference, it is essentially unconscious and therefore beyond rational control so that it lacks any normative significance. Peirce himself anticipates and attempts to answer this challenge. Peirce argues that abduction is both a source of creative insight and a form of logical inference (...) subject to a degree of conscious control. In this paper I shall sketch a developing account of abduction that is suggested by the work of Paul Churchland, Paul Thagard, Chris Eliasmith, William Wimsatt, Owen Flanagan, and others. I shall argue that a credible account of abduction will require that we approach the phenomenon from both higher and lower levels as represented by these approaches. (shrink)

In this paper I argue for a place for logic inscientific methodology, at the same level asthat of computational and historicalapproaches. While it is well known that a awhole generation of philosophers dismissedLogical Positivism (not just for the logicthough), there are at least two reasons toreconsider logical approaches in the philosophyof science. On the one hand, the presentsituation in logical research has gone farbeyond the formal developments that deductivelogic reached last century, and new researchincludes the formalization of several othertypes of (...) reasoning, like induction andabduction. On the other hand, we call for abalanced Philosophy of Science, one inwhich both methods, the formal and thehistorical may be complementary, togetherproviding a pluralistic view of science, inwhich no method is the predominant one. (shrink)

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...) psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields. 1. Causal Reasoning: An Introduction Michael R. Waldmann Part I: Theories of Causal Cognition 2. Associative Accounts of Causal Cognition Mike E. Le Pelley, Oren Griffiths, and Tom Beesley 3. Rules of Causal Judgment: Mapping Statistical Information Onto Causal Beliefs José C. Perales, Andrés Catena, Antonio Cándido, and Antonio Maldonado 4. The Inferential Reasoning Theory of Causal Learning: Toward a Multi- Process Propositional Account Yannick Boddez, Jan De Houwer, and Tom Beckers 5. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power Patricia W. Cheng and Hongjing Lu 6. The Acquisition and Use of Causal Structure Knowledge Benjamin Margolin Rottman 7. Formalizing Prior Knowledge in Causal Induction Thomas L. Griffiths 8. Causal Mechanisms Samuel G. B. Johnson and Woo-kyoung Ahn 9. Force Dynamics and Causation Phillip Wolff and Robert Thorstad 10. Mental Models and Causation P. N. Johnson- Laird and Sangeet S. Khemlani 11. Pseudocontingencies Klaus Fiedler and Florian Kutzner 12. Singular Causation David Danks 13. Cognitive Neuroscience of Causal Reasoning Joachim T. Operskalski and Aron K. Barbey Part II: Basic Cognitive Functions 14. Visual Impressions of Causality Peter White 15. Goal-Directed Actions Bernhard Hommel 16. Planning and Control Magda Osman 17. Reinforcement Learning and Causal Models Samuel J. Gershman 18. Causation and the Probability of Causal Conditionals David E. Over 19. Causal Models and Conditional Reasoning Mike Oaksford and Nick Chater 20. Concepts as Causal Models: Categorization Bob Rehder 21. Concepts as Causal Models: Induction Bob Rehder 22. Causal Explanation Tania Lombrozo and Nadya Vasilyeva 23. Diagnostic Reasoning Björn Meder and Ralf Mayrhofer 24. Inferring Causal Relations by Analogy Keith J. Holyoak and Hee-Seung Lee 25. Causal Argument Ulrike Hahn, Roland Bluhm, and Frank Zenker 26. Causality in Decision- Making York Hagmayer and Philip M. Fernbach Part III: Domains of Causal Reasoning 27. Intuitive Theories Tobias Gerstenberg and Joshua B. Tenenbaum 28. Space, Time, and Causality Marc J. Buehner 29. Causation in Legal and Moral Reasoning David A. Lagnado and Tobias Gerstenberg 30. The Role of Causal Knowledge in Reasoning About Mental Disorders Woo-kyoung Ahn, Nancy S. Kim, and Matthew S. Lebowitz 31. Causality and Causal Reasoning in Natural Language Torgrim Solstad and Oliver Bott 32. Social Attribution and Explanation Denis Hilton Part IV: Development, Phylogeny, and Culture 33. The Development of Causal Reasoning Paul Muentener and Elizabeth Bonawitz 34. Causal Reasoning in Non-Human Animals Christian Schloegl and Julia Fischer 35. Causal Cognition and Culture Andrea Bender, Sieghard Beller, and Douglas L. Medin. (shrink)

Taking as its exegetic point of departure Peirce's outline of a semiotic theory of cognition from the mid 1890s, this paper explores the relevance of this outline to a theory of learning and also to a broader, normative vision of education. Firstly, besides providing for fallibilism in philosophical inquiry Peirce's outline accords with critical strategies of his fellow pragmatists, such as William James's detection of the ‘psychologist's fallacy’ and John Dewey's rejection of the ‘philosophical fallacy’. It is pointed out that (...) this merit of Peirce's outline is due to his abstract and often neglected notion of a prespecialised scientific intelligence. Secondly, the paper shows that Peirce's semiotic approach to prescientific learning processes conditioning all kinds of theoretical activity may indeed complement James's and Dewey's contributions: giving careful attention to the work of linguistic and paralinguistic signs in such learning processes Peirce's analysis captures indexical and iconic aspects, as well as symbolic aspects of linguistic interaction and experientially constrained dialogue. Following Peirce's analysis the paper considers how the iconicity of linguistic structure, ostensive acts and the practical situatedness of language use are pre‐conditions of experiential learning in every day life as well as in scientific learning. It is also shown how this semiotic analysis is connected with Peirce's important notion of abduction. Finally, it is pointed out that a broader vision of education may issue from Peirce's semiotic analysis and that such a vision would emphasise that the life experience of individuals entering educational institutions should be made to bear on the objectives as well as the teaching practices of educational institutions. (shrink)

People are habitual explanation generators. At its most mundane, our propensity to explain allows us to infer that we should not drink milk that smells sour; at the other extreme, it allows us to establish facts (e.g., theorems in mathematical logic) whose truth was not even known prior to the existence of the explanation (proof). What do the cognitive operations underlying the inference that the milk is sour have in common with the proof that, say, the square root of two (...) is irrational? Our ability to generate explanations bears striking similarities to our ability to make analogies. Both reflect a capacity to generate inferences and generalizations that go beyond the featural similarities between a novel problem and familiar problems in terms of which the novel problem may be understood. However, a notable difference between analogy-making and explanation-generation is that the former is a process in which a single source situation is used to reason about a single target, whereas the latter often requires the reasoner to integrate multiple sources of knowledge. This seemingly small difference poses a challenge to the task of marshaling our understanding of analogical reasoning to understanding explanation. We describe a model of explanation, derived from a model of analogy, adapted to permit systematic violations of this one-to-one mapping constraint. Simulation results demonstrate that the resulting model can generate explanations for novel explananda and that, like the explanations generated by human reasoners, these explanations vary in their coherence. (shrink)

Whether abduction is treated as an argument or as an inference, the mainstream view presupposes a tight connection between abduction and inference to the best explanation. This paper critically evaluates this link and supports a narrower view on abduction. Our main thesis is that merely the hypothesis-generative aspect, but not the evaluative aspect, is properly abductive in the sense introduced by C. S. Peirce. We show why equating abduction with IBE unnecessarily complicates argument evaluation by levelling the status of abduction (...) as a third reasoning mode. We also propose a scheme for abductive argument along with critical questions, and suggest retaining abduction alongside IBE as related but distinct categories. (shrink)

In this paper it is shown how plausible reasoning of the kind illustrated in the ancient Greek example of the weak and strong man can be analyzed and evaluated using a procedure in which the pro evidence is weighed against the con evidence using formal, computational argumentation tools. It is shown by means of this famous example how plausible reasoning is based on an audience’s recognition of situations of a type they are familiar with as normal and comprehensible in their (...) shared common knowledge. The paper extends previous work on this example by using three new multiagent argumentation schemes closely related to the scheme for argument from negative consequences. (shrink)

The aim of this investigation is to explore the role of argumentation schemes in enthymeme reconstruction. This aim is pursued by studying selected cases of incomplete arguments in natural language discourse to see what the requirements are for filling in the unstated premises and conclusions in some systematic and useful way. Some of these cases are best handled using deductive tools, while others respond best to an analysis based on defeasible argumentations schemes. The approach is also shown to work reasonably (...) well for weak arguments, a class of arguments that has always been difficult to analyze without the principle of charity producing a straw man. (shrink)

Charles S. Peirce (1839-1914) made relevant contributions to deductive logic, but he was primarily interested in the logic of science, and more especially in what he called 'abduction' (as opposed to deduction and induction), which is the process whereby hypotheses are generated in order to explain the surprising facts. Indeed, Peirce considered abduction to be at the heart not only of scientific research, but of all ordinary human activities. Nevertheless, in spite of Peirce's work and writings in the field of (...) methodology of research, scarce attention has been paid to the logic of discovery over the last hundred years, despite an impressive development not only of scientific research but also of logic. -/- Having this in mind, the exposition is divided into five parts: 1) a brief presentation of Peirce, focusing on his work as a professional scientist; 2) an exposition of the classification of inferences by the young Peirce: deduction, induction and hypothesis; 3) a sketch of the notion of abduction in the mature Peirce; 4) an exposition of the logic of surprise; and finally, by way of conclusion, 5) a discussion of this peculiar ability of guessing understood as a rational instinct. -/- . (shrink)

This book examines the philosophical conception of abductive reasoning as developed by Charles S. Peirce, the founder of American pragmatism. It explores the historical and systematic connections of Peirce's original ideas and debates about their interpretations. Abduction is understood in a broad sense which covers the discovery and pursuit of hypotheses and inference to the best explanation. The analysis presents fresh insights into this notion of reasoning, which derives from effects to causes or from surprising observations to explanatory theories. The (...) author outlines some logical and AI approaches to abduction as well as studies various kinds of inverse problems in astronomy, physics, medicine, biology, and human sciences to provide examples of retroductions and abductions. The discussion covers also everyday examples with the implication of this notion in detective stories, one of Peirce’s own favorite themes. The author uses Bayesian probabilities to argue that explanatory abduction is a method of confirmation. He uses his own account of truth approximation to reformulate abduction as inference which leads to the truthlikeness of its conclusion. This allows a powerful abductive defense of scientific realism. This up-to-date survey and defense of the Peircean view of abduction may very well help researchers, students, and philosophers better understand the logic of truth-seeking. (shrink)

The present study has two goals: to explore elementary students’ understanding of evidence and the ways they deploy it to construct arguments, and to examine whether eliciting their concept of evidence during argumentation improves students’ evidence-based reasoning. Individual semi-structured interviews were conducted with 4th and 6th graders in a public school in Mexico. We found significant differences between groups regarding the concept of evidence, with better performance in the older group. A positive correlation between the concept of evidence and the (...) quality of evidence-based reasoning was found. Also, three performance profiles were observed after eliciting the concept of evidence when grade was excluded as a factor. Results suggest that the concept of evidence plays an essential role in developing argumentative competence in pre-adolescence. (shrink)