This paper reviews current psychological theories of syllogistic inference and establishes that despite their various merits they all contain deficiencies as theories of performance. It presents the results of two experiments, one using syllogisms and the other using three-term series problems, designed to elucidate how the arrangement of terms within the premises affects performance. These data are used in the construction of a theory based on the hypothesis that reasoners construct mental models of the premises, formulate informative conclusions about the (...) relations in the model, and search for alternative models that are counterexamples to these conclusions. This theory, which has been implemented in several computer programs, predicts that two principal factors should affect performance: the figure of the premises, and the number of models that they call for. These predictions were confirmed by a third experiment. Dans cet article sont passées en revue les théories psychologiques du traitement des syllogismes. On établit qu'en dépit de leurs mérites variés toutes sont en défaut en tant que théories de la performance. On présente les résultats de deux expériences, une utilisant des syllogismes et l'autre des problémes avec des séries de trois termes conçues pour élucider comment l'arrangement des termes dans les premises affecte la performance. Ces données sont utilisées pour construire une théorie fondée sur l'hypothése que les gens construisent des modèles mentaux des premises, formulent des conclusions explicites sur les relations dans le modèle et cherchent des modèles qui seraient des contre-exemples pour leurs conclusions. Cette théorie, utilisée dans plusieurs programmes d'ordinateur, prédit que deux principaux facteurs affectent la performance: la figure des premises et le nombre de; modèles qu'ils mettent en jeu. Cette prédiction est confirmée dans les trois expériences. (shrink)

Avec l’article « Aristotle’s natural deduction system », publié en 1974, J. Corcoran a contribué à diffuser une nouvelle perspective sur les écrits logiques d’Aristote et sur la théorie du syllogisme en particulier. Dans cet article, Corcoran affirme que, dans les premiers chapitres des Premiers Analytiques, Aristote ne propose pas un système axiomatique, qui supposerait une logique sous-jacente, ainsi que le pensait Łukasiewicz, mais plutôt un système de déduction naturelle, avec des dimensions métalogiques. Notre propos est ici basé sur une (...) courte monographie de Kurt Ebbinghaus, intitulée Ein formales Model der Syllogistik des Aristoteles (1964), où est fixé le canon de cette nouvelle perspective mentionnée plus haut et qui a été développé dans le cadre conceptuel de la « logique opérative » de Paul Lorenzen. Ebbinghaus développe une reconstruction formelle montrant que l’approche d’Aristote relève de la « théorie de la preuve », non seulement pour ce qui concerne le système d’inférence sous-jacent, mais aussi pour ce qui concerne les éléments métalogiques. C’est notamment à travers ce dernier aspect que se manifeste une différence majeure par rapport à la reconstruction de Corcoran. Alors que celui-ci pose que le système d’inférence d’Aristote est enraciné dans une sémantique de théorie des modèles (élaborée par Corcoran lui-même), Ebbinghaus comprend que la théorie du syllogisme a été développée à partir d’une approche de la signification par des règles (« rule based »), semblables aux règles d’un jeu. En fait, la reconstruction d’Ebbinghaus offre une lecture pragmatiste de la syllogistique d’Aristote, qui, tel est notre propos, paraît non seulement beaucoup plus proche du point de vue d’Aristote que ne l’est la sémantique de théorie des modèles proposée par Corcoran, mais qui, de plus, permet de saisir l’unité systématique de la théorie du syllogisme. (shrink)

Cognitive approaches contribute to our understanding of delusions by providing an explanatory framework that extends beyond the personal level to the sub personal level of information-processing systems. According to one influential cognitive approach, two factors are required to account for the content of a delusion, its initial adoption as a belief, and its persistence. This chapter reviews Bayesian developments of the two-factor framework.

Change, Choice and Inference develops logical theories that are necessary both for the understanding of adaptable human reasoning and for the design of intelligent systems. The book shows that reasoning processes - the drawing on inferences and changing one's beliefs - can be viewed as belonging to the realm of practical reason by embedding logical theories into the broader context of the theory of rational choice. The book unifies lively and significant strands of research in logic, philosophy, economics and artificial (...) intelligence. It elaborates on the relevant theories and provides a mathematically precise foundation for the thesis that large parts of theoretical reason can be subsumed under practical reason. (shrink)

In some previous work, I tried to give a concept-based account of the nature of our entitlement to certain very basic inferences (see the papers in Part III of Boghossian 2008b). In this previous work, I took it for granted, along with many other philosophers, that we understood well enough what it is for a person to infer. In this paper, I turn to thinking about the nature of inference itself. This topic is of great interest in its own right (...) and surprisingly understudied by philosophers. A correct understanding of inference promises to shed light on a number of important topics. In particular, it threatens to undermine the sort of concept-based story about entitlement to which I had previously been attracted. (shrink)

The value-free ideal for science holds that values should not influence the core features of scientific reasoning. We defend the difference-to-inference model of value-permeation, which holds that value-permeation in science is problematic when values make a difference to the inferences made about a hypothesis. This view of value-permeation is superior to existing views, and it suggests a corresponding maxim—namely, that scientists should strive to eliminate differences to inference. This maxim is the basis of a novel value-free ideal for science. -/- (...) . (shrink)

The emerging neurocomputational vision of humans as embodied, ecologically embedded, social agents—who shape and are shaped by their environment—offers a golden opportunity to revisit and revise ideas about the physical and information-theoretic underpinnings of life, mind, and consciousness itself. In particular, the active inference framework makes it possible to bridge connections from computational neuroscience and robotics/AI to ecological psychology and phenomenology, revealing common underpinnings and overcoming key limitations. AIF opposes the mechanistic to the reductive, while staying fully grounded in a (...) naturalistic and information-theoretic foundation, using the principle of free energy minimization. The latter provides a theoretical basis for a unified treatment of particles, organisms, and interactive machines, spanning from the inorganic to organic, non-life to life, and natural to artificial agents. We provide a brief introduction to AIF, then explore its implications for evolutionary theory, ecological psychology, embodied phenomenology, and robotics/AI research. We conclude the paper by considering implications for machine consciousness. (shrink)

Arguing for mathematical realism on the basis of Field’s explanationist version of the Quine–Putnam Indispensability argument, Alan Baker has recently claimed to have found an instance of a genuine mathematical explanation of a physical phenomenon. While I agree that Baker presents a very interesting example in which mathematics plays an essential explanatory role, I show that this example, and the argument built upon it, begs the question against the mathematical nominalist.

The design inference uncovers intelligent causes by isolating their key trademark: specified events of small probability. Just about anything that happens is highly improbable, but when a highly improbable event is also specified undirected natural causes lose their explanatory power. Design inferences can be found in a range of scientific pursuits from forensic science to research into the origins of life to the search for extraterrestrial intelligence. This challenging and provocative 1998 book shows how incomplete undirected causes are for science (...) and breathes new life into classical design arguments. It will be read with particular interest by philosophers of science and religion, other philosophers concerned with epistemology and logic, probability and complexity theorists, and statisticians. (shrink)

Philosophical discussions on causal inference in medicine are stuck in dyadic camps, each defending one kind of evidence or method rather than another as best support for causal hypotheses. Whereas Evidence Based Medicine advocates the use of Randomised Controlled Trials and systematic reviews of RCTs as gold standard, philosophers of science emphasise the importance of mechanisms and their distinctive informational contribution to causal inference and assessment. Some have suggested the adoption of a pluralistic approach to causal inference, and an inductive (...) rather than hypothetico-deductive inferential paradigm. However, these proposals deliver no clear guidelines about how such plurality of evidence sources should jointly justify hypotheses of causal associations. We here develop such guidelines by first giving a philosophical analysis of the underpinnings of Hill’s viewpoints on causality. We then put forward an evidence-amalgamation framework adopting a Bayesian net approach to model causal inference in pharmacology for the assessment of harms. Our framework accommodates a number of intuitions already expressed in the literature concerning the EBM vs. pluralist debate on causal inference, evidence hierarchies, causal holism, relevance, and reliability. (shrink)

We discuss two general issues concerning diverging sets of Bayesian (conditional) probabilities—divergence of “posteriors”—that can result with increasing evidence. Consider a setof probabilities typically, but not always, based on a set of Bayesian “priors.” Incorporating sets of probabilities, rather than relying on a single probability, is a useful way to provide a rigorous mathematical framework for studying sensitivity and robustness in Classical and Bayesian inference. See: Berger (1984, 1985, 1990); Lavine (1991); Huber and Strassen (1973); Walley (1991); and Wasserman and (...) Kadane (1990). Also, sets of probabilities arise in group decision problems. See: Levi (1982); and Seidenfeld, Kadane, and Schervish (1989). Third, sets of probabilities are one consequence of weakening traditional axioms for uncertainty. See: Good (1952); Smith (1961); Kyburg (1961); Levi (1974); Fishburn (1986); Seidenfeld, Schervish, and Kadane (1990); and Walley (1991). (shrink)

Epistemology and Inference was first published in 1983. Minnesota Archive Editions uses digital technology to make long-unavailable books once again accessible, and are published unaltered from the original University of Minnesota Press editions. Henry Kyburg has developed an original and important perspective on probabilistic and statistical inference. Unlike much contemporary writing by philosophers on these topics, Kyburg's work is informed by issues that have arisen in statistical theory and practice as well as issues familiar to professional philosophers. In two major (...) books and many articles, Kyberg has elaborated his technical proposals and explained their ramifications for epistemology, decision-making, and scientific inquiry. In this collection of published and unpublished essays, Kyburg presents his novel ideas and their applications in a manner that makes them accessible to philosophers and provides specialists in probability and induction with a concise exposition of his system. (shrink)

Many philosophers argue that explanatoriness plays no special role in confirmation – that “inference to the best explanation” (IBE) incorrectly demands giving hypotheses extra credit for their potential explanatory qualities beyond the credit they already deserve for their predictive successes. This paper argues against one common strategy for responding to this thought – that is, for trying to fit IBE within a Bayesian framework. That strategy argues that a hypothesis’ explanatory quality (its “loveliness”) contributes either to its prior probability or (...) to its likelihood. This paper argues that this strategy fails because it must give different treatments to two hypotheses that are unlovely for the very same reason. The strategy therefore loses the insight into scientific reasoning that its reconstruction in terms of IBE is supposed to provide. The paper then provides a Bayesian account of the confirmatory role of explanatoriness that represents explanatory quality as having an impact in the same place for two hypotheses that are unlovely for the very same reason. This approach works by “putting explanation back into IBE” – that is, by invoking hypotheses that refer explicitly to scientific explanation and by invoking the agent's background opinions regarding the kind of explanation that the evidence is liable to have. On this approach, there is no list of “explanatory virtues” the possession of which always helps to make an explanation better. Rather, for different facts, there are different characteristics that our background knowledge of other explanations gives us some reason to expect the given fact's explanation to possess. (shrink)

This paper considers the Cartesian theatre as a metaphor for the virtual reality models that the brain uses to make inferences about the world. This treatment derives from our attempts to understand dreaming and waking consciousness in terms of free energy minimization. The idea here is that the Cartesian theatre is not observed by an internal audience but furnishes a theatre in which fictive narratives and fantasies can be rehearsed and tested against sensory evidence. We suppose the brain is driven (...) by the imperative to infer the causes of its sensory samples; in much the same way as scientists are compelled to test hypotheses about experimental data. This recapitulates Helmholtz's notion of unconscious inference and Gregory's treatment of perception as hypothesis testing. However, we take this further and consider the active sampling of the world as the gathering of confirmatory evidence for hypotheses based on our virtual reality. The ensuing picture of consciousness resolves a number of seemingly hard problems in consciousness research and is internally consistent with current thinking in systems neuroscience and theoretical neurobiology. In this formalism, there is a dualism that distinguishes between the process of inference and the process that entails inference. This separation is reflected by the distinction between beliefs and the physical brain states that encode them. This formal approach allows us to appeal to simple but fundamental theorems in information theory and statistical thermodynamics that dissolve some of the mysterious aspects of consciousness. (shrink)

This work addresses the autonomous organization of biological systems. It does so by considering the boundaries of biological systems, from individual cells to Home sapiens, in terms of the presence of Markov blankets under the active inference scheme—a corollary of the free energy principle. A Markov blanket defines the boundaries of a system in a statistical sense. Here we consider how a collective of Markov blankets can self-assemble into a global system that itself has a Markov blanket; thereby providing an (...) illustration of how autonomous systems can be understood as having layers of nested and self-sustaining boundaries. This allows us to show that: (i) any living system is a Markov blanketed system and (ii) the boundaries of such systems need not be co-extensive with the biophysical boundaries of a living organism. In other words, autonomous systems are hierarchically composed of Markov blankets of Markov blankets—all the way down to individual cells, all the way up to you and me, and all the way out to include elements of the local environment. (shrink)

In this paper I defend interaction theory (IT) as an alternative to both theory theory (TT) and simulation theory (ST). IT opposes the basic suppositions that both TT and ST depend upon. I argue that the various capacities for primary and secondary intersubjectivity found in infancy and early childhood should not be thought of as precursors to later developing capacities for using folk psychology or simulation routines. They are not replaced or displaced by such capacities in adulthood, but rather continue (...) to operate as our ordinary and everyday basis for social cognition. I also argue that enactive perception rather than implicit simulation is the best model for explaining these capacities. (shrink)

This paper shows how the availability heuristic can be used to justify inference to the best explanation in such a way that van Fraassen's infamous "best of a bad lot" objection can be adroitly avoided. With this end in mind, a dynamic and contextual version of the erotetic model of explanation sufficient to ground this response is presented and defended.

How is that when scientists need some piece of mathematics through which to frame their theory, it is there to hand? What has been called 'the unreasonable effectiveness of mathematics' sets a challenge for philosophers. Some have responded to that challenge by arguing that mathematics is essentially anthropocentric in character, whereas others have pointed to the range of structures that mathematics offers. Otavio Bueno and Steven French offer a middle way, which focuses on the moves that have to be made (...) in both the mathematics and the relevant physics in order to bring the two into appropriate relation. This relation can be captured via the inferential conception of the applicability of mathematics, which is formulated in terms of immersion, inference, and interpretation. In particular, the roles of idealisations and of surplus structure in science and mathematics respectively are brought to the fore and captured via an approach to models and theories that emphasize the partiality of the available information: the partial structures approach. The discussion as a whole is grounded in a number of case studies drawn from the history of quantum physics, and extended to contest recent claims that the explanatory role of certain mathematical structures in scientific practice supports a realist attitude towards them. The overall conclusion is that the effectiveness of mathematics does not seem unreasonable at all once close attention is paid to how it is actually applied in practice. (shrink)

In a series of papers over the past twenty years, and in a new book, Igor Douven has argued that Bayesians are too quick to reject versions of inference to the best explanation that cannot be accommodated within their framework. In this paper, I survey their worries and attempt to answer them using a series of pragmatic and purely epistemic arguments that I take to show that Bayes’ Rule really is the only rational way to respond to your evidence.

The mainstream science of consciousness offers a few predominate views of how the brain gives rise to awareness. Chief among these are the Higher-Order Thought Theory, Global Neuronal Workspace Theory, Integrated Information Theory, and hybrids thereof. In parallel, rapid development in predictive processing approaches have begun to outline concrete mechanisms by which interoceptive inference shapes selfhood, affect, and exteroceptive perception. Here, we consider these new approaches in terms of what they might offer our empirical, phenomenological, and philosophical understanding of consciousness (...) and its neurobiological roots. (shrink)

The paper addresses the phenomenology of inference. It proposes that the conscious character of conscious inferences is partly constituted by a sense of meaning; specifically, a sense of what Grice called ‘natural meaning’. In consciously drawing the (outright, categorical) conclusion that Q from a presumed fact that P, one senses the presumed fact that P as meaning that Q, where ‘meaning that’ expresses natural meaning. This sense of natural meaning is phenomenologically analogous, I suggest, to our sense of what is (...) said in fluently comprehending everyday utterances in our first language. The proposal that conscious inference involves a sense of natural meaning is compared with views according to which conscious inference involves taking the premises (i) to be good reasons for the conclusion (as defended by Thomson and Grice), (ii) to support it (as argued by Audi and, recently, Boghossian), or (iii) to imply it (as lately contended by Broome). I argue our proposal can explain certain phenomena handled by alternatives (i) and (ii), but that some further phenomena is handled by our account but not these alternatives. In relation to alternative (iii), I argue that, in so far as implicational and natural-meaning relations come apart, the latter are a better fit for what we sense or take to be so in conscious inference. (shrink)

This paper considers an application of work on probabilistic measures of coherence to inference to the best explanation. Rather than considering information reported from different sources, as is usually the case when discussing coherence measures, the approach adopted here is to use a coherence measure to rank competing explanations in terms of their coherence with a piece of evidence. By adopting such an approach IBE can be made more precise and so a major objection to this mode of reasoning can (...) be addressed. Advantages of the coherence - based approach are pointed out by comparing it with several other ways to characterize ‘ best explanation ’ and showing that it takes into account their insights while overcoming some of their problems. The consequences of adopting this approach for IBE are discussed in the context of recent discussions about the relationship between IBE and Bayesianism. (shrink)

Recognizing and aligning individuals’ unique adaptive beliefs or “priors” through cooperative communication is critical to establishing a therapeutic relationship and alliance. Using active inference, we present an empirical integrative account of the biobehavioral mechanisms that underwrite therapeutic relationships. A significant mode of establishing cooperative alliances—and potential synchrony relationships—is through ostensive cues generated by repetitive coupling during dynamic touch. Established models speak to the unique role of affectionate touch in developing communication, interpersonal interactions, and a wide variety of therapeutic benefits for (...) patients of all ages; both neurophysiologically and behaviorally. The purpose of this article is to argue for the importance of therapeutic touch in establishing a therapeutic alliance and, ultimately, synchrony between practitioner and patient. We briefly overview the importance and role of therapeutic alliance in prosocial and clinical interactions. We then discuss how cooperative communication and mental state alignment—in intentional communication—are accomplished using active inference. We argue that alignment through active inference facilitates synchrony and communication. The ensuing account is extended to include the role of tactile afferents in realizing the beneficial effect of therapeutic synchrony. We conclude by proposing a method for synchronizing the effects of touch using the concept of active inference. (shrink)

Why are conditional degrees of belief in an observation E, given a statistical hypothesis H, aligned with the objective probabilities expressed by H? After showing that standard replies are not satisfactory, I develop a suppositional analysis of conditional degree of belief, transferring Ramsey’s classical proposal to statistical inference. The analysis saves the alignment, explains the role of chance-credence coordination, and rebuts the charge of arbitrary assessment of evidence in Bayesian inference. Finally, I explore the implications of this analysis for Bayesian (...) reasoning with idealized models in science. (shrink)

A prevailing view is that while human communication has an ‘ostensive-inferential’ or ‘Gricean’ intentional structure, animal communication does not. This would make the psychological states that support human and animal forms of communication fundamentally different. Against this view, I argue that there are grounds to expect ostensive communication in non-human clades. This is because it is sufficient for ostensive communication that one intentionally address one’s utterance to one’s intended interlocutor – something that is both a functional pre-requisite of successful communication (...) and cognitively undemanding. Furthermore, while ostension is an important feature of intentional communication, the inferences required in Gricean communication may be minimal: ostension and inference may come apart. The grounds for holding that animal communication could not be Gricean are therefore weak. I finish by defending the idea that a ‘minimally Gricean’ model of communication is a valuable tool for characterising the communicative interactions of many animal species. (shrink)

In this paper we compare grammatical inference in the context of simple and of mixed Lambek systems. Simple Lambek systems are obtained by taking the logic of residuation for a family of multiplicative connectives /,,\, together with a package of structural postulates characterizing the resource management properties of the connective.Different choices for Associativity and Commutativity yield the familiar logics NL, L, NLP, LP. Semantically, a simple Lambek system is a unimodal logic: the connectives get a Kripke style interpretation in terms (...) of a single ternary accessibility relation modeling the notion of linguistic composition for each individual system.The simple systems earch have their virtues in linguistic analysis. But none of them in isolation provides a basis for a full theory of grammar. In the second part of the paper, we consider two types of mixed Lambek systems. (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)

This paper illustrates cognitive modeling constructs designed to make learning by instruction more robust, including (1) flexible grounding of language to execution, (2) inference of implicit instruction knowledge, and (3) interactive clarification of instructions during both learning and execution.

The book is a defence of scientific realism. Its primary aim is to argue that it is possible to establish scientific realism without Inference to the Best Explanation. The idea that plays the central role in the book is an "Eddington-inference". Arthur Eddington once considered a hypothetical ichthyologist who concluded from the fact that his net contained no fish smaller than the holes in his net that there were in the sea no fish smaller than the holes in his net. (...) Although Eddington himself defended the inference, the author of the present volume argues on probabilistic grounds that it is likely such an inference is flawed. He generalises the argument to develop a probabilistic justification for scientific realist claims about the existence of unobservable entities. (shrink)

Conceptual change and its connection to the development of new seien tific theories has reeently beeome an intensively discussed topic in philo sophieal literature. Even if the inductive aspects related to conceptual change have already been discussed to some extent, there has so far existed no systematic treatment of inductive change due to conceptual enrichment. This is what we attempt to accomplish in this work, al though most of our technical results are restricted to the framework of monadic languages. We (...) extend Hintikka's system of inductive logic to apply to situations in which new concepts are introduced to the original language. By interpreting them as theoretica1 concepts, it is possible to discuss a number of currently debated philosophical and methodological problems which have previously escaped systematic and exact treatment. For instance, the role which seientific theories employing theoretical con cepts may play within inductive inference can be studied within this framework. From the viewpoint of seientific realism, sueh a study gives outlines for a theory of what we call hypothetico-induetive inference. Some parts of this work which are based on Hintikka's system of in ductive logic are fairly technical. However, no previous knowledge of this system is required, but, in general, acquaintance with the basic ideas of elementary logic and probability theory is suffieient. This work is part of a project, originated by Professors Jaakko Hintikka and Raimo Tuomela, concerning the role of theoretical concepts in science. (shrink)

Modern scientific cosmology pushes the boundaries of knowledge and the knowable. This is prompting questions on the nature of scientific knowledge. A central issue is what defines a 'good' model. When addressing global properties of the Universe or its initial state this becomes a particularly pressing issue. How to assess the probability of the Universe as a whole is empirically ambiguous, since we can examine only part of a single realisation of the system under investigation: at some point, data will (...) run out. We review the basics of applying Bayesian statistical explanation to the Universe as a whole. We argue that a conventional Bayesian approach to model inference generally fails in such circumstances, and cannot resolve, e.g., the so-called 'measure problem' in inflationary cosmology. Implicit and non-empirical valuations inevitably enter model assessment in these cases. This undermines the possibility to perform Bayesian model comparison. One must therefore either stay silent, or pursue a more general form of systematic and rational model assessment. We outline a generalised axiological Bayesian model inference framework, based on mathematical lattices. This extends inference based on empirical data (evidence) to additionally consider the properties of model structure (elegance) and model possibility space (beneficence). We propose this as a natural and theoretically well-motivated framework for introducing an explicit, rational approach to theoretical model prejudice and inference beyond data. (shrink)

In Intention, Anscombe says that practical reasoning is practical, not by virtue of its content, but rather by virtue of its form. But in her later essay ‘Practical Inference’, she seems to take this back, claiming instead that (1) the practicality of practical reasoning (or inference) resides in the distinctive use it makes of the premises, and (2) ‘it is a matter of indifference’ whether we say that it exemplifies a distinctive form. I aim to show that Anscombe is right (...) about (1) but wrong about (2): the distinctive use (or teleology) of practical reasoning explains its distinctive formal features, and when the former is thought through, the latter are revealed to be more numerous and significant than Anscombe seems to recognize. (shrink)

If one investigates a process that has several causes but assumes that it has only one cause, one risks ruling out important causal factors. Three mechanisms account for this mistake: either the significance of the single cause under test is masked by noise contributed by the unsuspected and uncontrolled factors, or the process appears only when two or more causes interact, or the process appears when there are present any of a number of sufficient causes which are not mutally exclusive. (...) In ecology and evolutionary biology, experiments usually test single factor hypotheses, and many scientists apparently believe that hypotheses incorporating several factors are so much more difficult to test that to do so would not be practical. We discuss several areas in ecology and evolutionary biology in which the presupposition of simple causation has apparently impeded progress. We also examine a more mature field, the study of atherosclerosis, in which single factor studies did significantly delay progress towards understanding what now appears to be a multifactor process. The problem has three solutions: either factorial experiments, dynamic models that make quantitative predictions, response-surface methods, or all three. In choosing a definition for ‘cause’, we make a presupposition that profoundly influences subsequent observations and experimental designs. Alternative definitions of causation should be considered as contributing to potential cures for research problems. (shrink)

In their introduction to this volume, Ram and Leake usefully distinguish between task goals and learning goals. Task goals are desired results or states in an external world, while learning goals are desired mental states that a learner seeks to acquire as part of the accomplishment of task goals. We agree with the fundamental claim that learning is an active and strategic process that takes place in the context of tasks and goals (see also Holland, Holyoak, Nisbett, and Thagard, 1986). (...) But there are important questions about the nature of goals that have rarely been addressed. First, how can a cognitive system deal with incompatible task goals? Someone may want both to get lots of research done and to relax and have fun with his or her friends. Learning how to accomplish both these tasks will take place in the context of goals that cannot be fully realized together. Second, how are goals chosen in the first place and why are some goals judged to be more important than others? People do not simply come equipped with goals and priorities: we sometimes have to learn what is important to us by adjusting the importance of goals in the context of other compatible and incompatible goals. This paper presents a theory and a computational model of how goals can be adopted or rejected in the context of decision making. In contrast to classical decision theory, it views decision making as a process not only of choosing actions but also of evaluating goals. Our theory can therefore be construed as concerned with the goal-directed learning of goals. (shrink)

Suppose we think in a language of thought. Then Paul Boghossian' is prepared to argue, first, that there may be ambiguous Mentalese expression types that have unambiguous tokens, and, second, that the way in which this is possible allows for otherwise valid theoretical or practical reasoning to be rendered invalid owing to equivocation of a sort that may be undetectable to the reasoner. Paul sees this as a possible basis from which to launch an argument for what some might call (...) "narrow content", and this is a question I'll take up later. (shrink)

Twenty philosophers offer new essays examining the form of reasoning known as inference to the best explanation - widely used in science and in our everyday lives, yet still controversial. Best Explanations represents the state of the art when it comes to understanding, criticizing, and defending this form of reasoning.

We investigate uncertain reasoning with quantified sentences of the predicate calculus treated as the limiting case of maximum entropy inference applied to finite domains.

Abstract: It is often assumed without argument that fictionalism in the philosophy of science contradicts scientific realism. This paper is a critical analysis of this assumption. The kind of fictionalism that is at present discussed in philosophy of science is characterised, and distinguished from fictionalism in other areas. A distinction is then drawn between forms of fictional representation, and two competing accounts of fiction in science are discussed. I then outline explicitly what I take to be the argument for the (...) incompatibility of scientific realism with fictionalism. I argue that some of its premises are unwarranted, and are moreover questionable from a fictionalist perspective. The conclusion is that fictionalism is neutral in the realism-antirealism debate, pulling neither in favour nor against scientific realism. (shrink)

Three challenges to a unified understanding of delusions emerge from Radden's On Delusion (2011). Here, I propose that in order to respond to these challenges, and to work towards a unifying framework for delusions, we should see delusions as arising in inference under uncertainty. This proposal is based on the observation that delusions in key respects are surprisingly like perceptual illusions, and it is developed further by focusing particularly on individual differences in uncertainty expectations.