Two main uses of categories are classification and feature inference, and category labels have been widely shown to play a dominant role in feature inference. However, the nature of this influence remains unclear, and we evaluate two contrasting hypotheses formalized as mathematical models: the label special-mechanism hypothesis and the label super-salience hypothesis. The special-mechanism hypothesis is that category labels, unlike other features, trigger inference decision making in reference to the category prototypes. This results in a (...) tendency for prototype-compatible inferences because the labels trigger a special mechanism rather than because of any influences they have on similarity evaluation. The super-salience hypothesis assumes that the large label influence is due to their high salience and corresponding impact on similarity without any need for a special mechanism. Application of the two models to a feature inference task based on a family resemblance category structure yields strong support for the label super-salience hypothesis and in particular does not support the need for a special mechanism based on prototypes. (shrink)

How do we go about weighing evidence, testing hypotheses, and making inferences? The model of " inference to the best explanation " -- that we infer the hypothesis that would, if correct, provide the best explanation of the available evidence--offers a compelling account of inferences both in science and in ordinary life. Widely cited by epistemologists and philosophers of science, IBE has nonetheless remained little more than a slogan. Now this influential work has been thoroughly revised and updated, and (...) features a new introduction and two new chapters. Inference to the Best Explanation is an unrivaled exposition of a theory of particular interest in the fields both of epistemology and the philosophy of science. (shrink)

Coping with uncertainty is a necessary part of ordinary life and is crucial to an understanding of how the mind works. For example, it is a vital element in developing artificial intelligence that will not be undermined by its own rigidities. There have been many approaches to the problem of uncertain inference, ranging from probability to inductive logic to nonmonotonic logic. Thisbook seeks to provide a clear exposition of these approaches within a unified framework. The principal market for the (...) book will be students and professionals in philosophy, computer science, and AI. Among the special features of the book are a chapter on evidential probability, which has not received a basic exposition before; chapters on nonmonotonic reasoning and theory replacement, matters rarely addressed in standard philosophical texts; and chapters on Mill's methods and statistical inference that cover material sorely lacking in the usual treatments of AI and computer science. (shrink)

The Yogācāra school of logic developed a theory of sign-inferences that has many features of the Stoic and Epicurean logical teachings with small inclusions of Aristotelian ideas. In the Nyāyabindu of Dharmakīrti, we can find the following schemes of formal reasoning: modus Barbara (Figure I) and modus Camenes (Figure IV) of the Aristotelian syllogistic, and all the inference rules of the Stoic logic: modus ponens, modus tollens, modus ponendo tollens, modus tollendo ponens I, modus tollendo ponens II. The three (...) premises of Yogācāra inference (if p, then q; there is an example that p and q occur together; p; then q) corresponds to the Epicurean doctrine of three premises with a necessary exemplification. Thus, the paper is a comparative study of Greek and Buddhist theory of sign-inferences, showing a deep similarity between them. (shrink)

What is an inference? Logicians and philosophers have proposed various conceptions of inference. I shall first highlight seven features that contribute to distinguish these conceptions. I shall then compare three conceptions to see which of them best explains the special force that compels us to accept the conclusion of an inference, if we accept its premises.

Inference to the Best Explanation (IBE) is widely criticized for being an unreliable form of ampliative inference – partly because the explanatory hypotheses we have considered at a given time may all be false, and partly because there is an asymmetry between the comparative judgment on which an IBE is based and the absolute verdict that IBE is meant to license. In this paper, I present a further reason to doubt the epistemic merits of IBE and argue that (...) it motivates moving to an inferential pattern in which IBE emerges as a degenerate limiting case. Since this inferential pattern is structurally similar to an argumentative strategy known as Inferential Robustness Analysis (IRA), it effectively combines the most attractive features of IBE and IRA into a unified approach to non-deductive inference. (shrink)

A surge of empirical research demonstrating flexible cognition in animals and young infants has raised interest in the possibility of rational decision-making in the absence of language. A venerable position, which I here call “Classical Inferentialism”, holds that nonlinguistic agents are incapable of rational inferences. Against this position, I defend a model of nonlinguistic inferences that shows how they could be practically rational. This model vindicates the Lockean idea that we can intuitively grasp rational connections between thoughts by developing the (...) Davidsonian idea that practical inferences are at bottom categorization judgments. From this perspective, we can see how similarity-based categorization processes widely studied in human and animal psychology might count as practically rational. The solution involves a novel hybrid of internalism and externalism: intuitive inferences are psychologically rational (in the explanatory sense) given the intensional sensitivity of the similarity assessment to the internal structure of the agent's reasons for acting, but epistemically rational (in the justificatory sense) given an ecological fit between the features matched by that assessment and the structure of the agent's environment. The essay concludes by exploring empirical results that show how nonlinguistic agents can be sensitive to these similarity assessments in a way that grants them control over their opaque judgments. (shrink)

This article considers the prospects of inference to the best explanation as a method of confirming causal claims vis-à-vis the medical evidence of mechanisms. I show that IBE is actually descriptive of how scientists reason when choosing among hypotheses, that it is amenable to the balance/weight distinction, a pivotal pair of concepts in the philosophy of evidence, and that it can do justice to interesting features of the interplay between mechanistic and population level assessments.

Recent epistemology of modality has seen a growing trend towards metaphysics-first approaches. Contrastingly, this paper offers a more philosophically modest account of justifying modal claims, focusing on the practices of scientific modal inferences. Two ways of making such inferences are identified and analyzed: actualist-manipulationist modality and relative modality. In AM, what is observed to be or not to be the case in actuality or under manipulations, allows us to make modal inferences. AM-based inferences are fallible, but the same holds for (...) practically all empirical inquiry. In RM, modal inferences are evaluated relative to what is kept fixed in a system, like a theory or a model. RM-based inferences are more certain but framework-dependent. While elements from both AM and RM can be found in some existing accounts of modality, it is worth highlighting them in their own right and isolating their features for closer scrutiny. This helps to establish their relevant epistemologies that are free from some strong philosophical assumptions often attached to them in the literature. We close by showing how combining these two routes amounts to a view that accounts for a rich variety of modal inferences in science. (shrink)

This book investigates the role of inference in argumentation, considering how arguments support standpoints on the basis of different loci. The authors propose and illustrate a model for the analysis of the standpoint-argument connection, called Argumentum Model of Topics. A prominent feature of the AMT is that it distinguishes, within each and every single argumentation, between an inferential-procedural component, on which the reasoning process is based; and a material-contextual component, which anchors the argument in the interlocutors’ cultural and (...) factual common ground. The AMT explains how these components differ and how they are intertwined within each single argument. This model is introduced in Part II of the book, following a careful reconstruction of the enormously rich tradition of studies on inference in argumentation, from the antiquity to contemporary authors, without neglecting medieval and post-medieval contributions. The AMT is a contemporary model grounded in a dialogue with such tradition, whose crucial aspects are illuminated in this book. (shrink)

Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a (...) single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models. Key Words: additive clustering; Bayesian inference; categorization; concept learning; contrast model; features; generalization; psychological space; similarity. (shrink)

A surge of empirical research demonstrating flexible cognition in animals and young infants has raised interest in the possibility of rational decision‐making in the absence of language. A venerable position, which I here call “Classical Inferentialism”, holds that nonlinguistic agents are incapable of rational inferences. Against this position, I defend a model of nonlinguistic inferences that shows how they could be practically rational. This model vindicates the Lockean idea that we can intuitively grasp rational connections between thoughts by developing the (...) Davidsonian idea that practical inferences are at bottom categorization judgments. From this perspective, we can see how similarity‐based categorization processes widely studied in human and animal psychology might count as practically rational. The solution involves a novel hybrid of internalism and externalism: intuitive inferences are psychologically rational (in the explanatory sense) given the intensional sensitivity of the similarity assessment to the internal structure of the agent's reasons for acting, but epistemically rational (in the justificatory sense) given an ecological fit between the features matched by that assessment and the structure of the agent's environment. The essay concludes by exploring empirical results that show how nonlinguistic agents can be sensitive to these similarity assessments in a way that grants them control over their opaque judgments. (shrink)

Dharmakīrti argues that there is no pramāṇa (valid means of cognition or source of knowledge) for a thesis that is a self-contradiction (svavacanavirodha). That is, self-contradictions such as ‘everything said is false’ and ‘my mother is barren’ cannot be known to be true or false. The contemporary scholar Tillemans challenges Dharmakīrti by arguing that we can know that self-contradictions are false by means of a formal logical inference. The aims of the paper are to answer Tillemans’ challenge from what (...) we take to be Dharmakīrti’s or Dharmakīrtian (someone who is like Dharmakīrti) perspective and to demonstrate the unique features of Dharmakīrti’s view of inference. By so doing, we show that the epistemology in relation to the formal conception of logic that underlies Tillemans’ challenge is problematic from Dharmakīrti’s or Dharmakīrtian perspective. The paper, thus, presents Dharmakīrti’s view of inference and logical reasoning as well as a Dharmakīrtian challenge to the formal conception of logic that is the dominant contemporary conception. (shrink)

Inferences are familiar movements of thought, but despite important recent work on the topic, we do not yet have a fully satisfying theory of inference. Here I provide a functionalist theory of inference. I argue that the functionalist framework allows us the flexibility to meet various demands on a theory of inference that have been proposed (such as that it must explain inferential Moorean phenomena and epistemological ‘taking’). While also allowing us to compare, contrast, adapt, and combine (...) features of extant theories of inference into one unified theory. In fleshing out the inference role, I also criticize the common assumption that inference requires rule-following. (shrink)

Probabilistic inference from frequencies, such as "Most Quakers are pacifists; Nixon is a Quaker, so probably Nixon is a pacifist" suffer from the problem that an individual is typically a member of many "reference classes" (such as Quakers, Republicans, Californians, etc) in which the frequency of the target attribute varies. How to choose the best class or combine the information? The article argues that the problem can be solved by the feature selection methods used in contemporary Big Data (...) science: the correct reference class is that determined by the features relevant to the target, and relevance is measured by correlation (that is, a feature is relevant if it makes a difference to the frequency of the target). (shrink)

In Lehrer’s case of the superstitious lawyer, a lawyer possesses conclusive evidence for his client’s innocence, and he appreciates that the evidence is conclusive, but the evidence is causally inert with respect to his belief in his client’s innocence. This case has divided epistemologists ever since Lehrer originally proposed it in his argument against causal analyses of knowledge. Some have taken the claim that the lawyer bases his belief on the evidence as a data point for our theories to accommodate, (...) while others have denied that the lawyer has knowledge, or that he bases his belief on the evidence. In this paper, we move the dialectic forward by way of arguing that the superstitious lawyer genuinely infers his client’s innocence from the evidence. To show that the lawyer’s inference is genuine, we argue in defense of a version of a doxastic construal of the ‘taking’ condition on inference. We also provide a pared-down superstitious lawyer-style case, which displays the key features of the original case without including its complicated and distracting features. But interestingly, although we argue that the lawyer’s belief is based on his good evidence, and is also plausibly doxastically justified, we do not argue that the lawyer knows that his client is innocent. (shrink)

Subjective predicates have two interpretive and distributional characteristics that have resisted a comprehensive analysis. First, the use of a subjective predicate to describe an object is in general felicitous only when the speaker has a particular kind of familiarity with relevant features of the object; characterizing an object as _tasty,_ for example, implies that the speaker has experience of its taste. Second, subjective predicates differ from objective predicates in their distribution under certain types of propositional attitude verbs. The goal of (...) this paper is to argue that these features can be explained in a uniform way and within a broadly truth-conditional approach to semantic content, given a view of subjective language as an essentially pragmatic, context-sensitive phenomenon. Specifically, we propose that what renders an issue subjective in discourse is speakers’ awareness of _counterstance contingency_: contingency relative to information states that represent alternative pragmatic stances. (shrink)

Inferentialism is a theory in the philosophy of language which claims that the meanings of expressions are constituted by inferential roles or relations. Instead of a traditional model-theoretic semantics, it naturally lends itself to a proof-theoretic semantics, where meaning is understood in terms of inference rules with a proof system. Most work in proof-theoretic semantics has focused on logical constants, with comparatively little work on the semantics of non-logical vocabulary. Drawing on Robert Brandom’s notion of material inference and (...) Greg Restall’s bilateralist interpretation of the multiple conclusion sequent calculus, I present a proof-theoretic semantics for atomic sentences and their constituent names and predicates. The resulting system has several interesting features: (1) the rules are harmonious and stable; (2) the rules create a structure analogous to familiar model-theoretic semantics; and (3) the semantics is compositional, in that the rules for atomic sentences are determined by those for their constituent names and predicates. (shrink)

Inferentialism is a theory in the philosophy of language which claims that the meanings of expressions are constituted by inferential roles or relations. Instead of a traditional model-theoretic semantics, it naturally lends itself to a proof-theoretic semantics, where meaning is understood in terms of inference rules with a proof system. Most work in proof-theoretic semantics has focused on logical constants, with comparatively little work on the semantics of non-logical vocabulary. Drawing on Robert Brandom’s notion of material inference and (...) Greg Restall’s bilateralist interpretation of the multiple conclusion sequent calculus, I present a proof-theoretic semantics for atomic sentences and their constituent names and predicates. The resulting system has several interesting features: (1) the rules are harmonious and stable; (2) the rules create a structure analogous to familiar model-theoretic semantics; and (3) the semantics is compositional, in that the rules for atomic sentences are determined by those for their constituent names and predicates. (shrink)

This paper addresses two main questions. How does one determine that something has the features it does as a result of design, as opposed to for example chance? How are inferences to design affected when one makes the assumption that the universe is spatially infinite? I will show that arguments for the existence of God based on the improbable development of life don’t go through under the supposition that the universe is spatially infinite. I will also show that the model (...) of design inferences promulgated by William Dembski is flawed, because it has the consequence that one can never infer design in a spatially infinite universe. My model for design inferences has the consequence that there are circumstances where a seeming miracle can count as evidence for the existence of God, even if one would expect that type of event to naturalistically occur in a spatially infinite universe. (shrink)

In "The Comparative Method" Ragin (1987) has outlined a procedure of Boolean causal reasoning operating on pure coincidence data that has meanwhile become widely known as QCA (Qualitative Comparative Analysis) among social scientists. QCA -- also in its recent form as presented in Ragin (2000) -- is designed to analyze causal structures featuring one effect and a possibly complex configuration of mutually independent direct causes of that effect. The paper at hand presents a procedure of causal reasoning that operates on (...) the same type of empirical data as QCA and that implements Boolean techniques related to the ones resorted to by QCA, yet, in contrast to QCA, the procedure introduced here successfully identifies causal structures involving both mutually dependent causes, i.e. causal chains, and multiple effects, i.e. epiphenomena. In this sense, the paper at hand generalizes QCA. (shrink)

This paper develops a means–end analysis of an inductive problem that arises in particle physics: how to infer from observed reactions conservation principles that govern all reactions among elementary particles. I show that there is a reliable inference procedure that is guaranteed to arrive at an empirically adequate set of conservation principles as more and more evidence is obtained. An interesting feature of reliable procedures for finding conservation principles is that in certain precisely defined circumstances they must introduce (...) hidden particles. Among the reliable inductive methods there is a unique procedure that minimizes convergence time as well as the number of times that the method revises its conservation principles. Thus the aims of reliable, fast and steady convergence to an empirically adequate theory single out a unique optimal inference for a given set of observed reactions—including prescriptions for when exactly to introduce hidden particles. (shrink)

This paper assesses Sarkar's ([2003]) deflationary account of genetic information. On Sarkar's account, genes carry information about proteins because protein synthesis exemplifies what Sarkar calls a ‘formal information system’. Furthermore, genes are informationally privileged over non-genetic factors of development because only genes enter into arbitrary relations to their products (in virtue of the alleged arbitrariness of the genetic code). I argue that the deflationary theory does not capture four essential features of the ordinary concept of genetic information: intentionality, exclusiveness, asymmetry, (...) and causal relevance. It is therefore further removed from what is customarily meant by genetic information than Sarkar admits. Moreover, I argue that it is questionable whether the account succeeds in demonstrating that information is theoretically useful in molecular genetics. Introduction Sarkar's Information System The Pre-theoretic Features of Genetic Information 3.1 Intentionality 3.2 Exclusiveness 3.3 Asymmetry 3.4 Causal relevance Theoretical Usefulness Conclusion CiteULike Connotea Del.icio.us What's this? (shrink)

Whenever the use of non-experimental data for discovering causal relations or predicting the outcomes of experiments or interventions is contemplated, two difficulties are routinely faced. One is the problem of latent variables, or confounders: factors influencing two or more measured variables may not themselves have been measured or recorded. The other is the problem of sample selection bias: values of the variables or features under study may themselves influence whether a unit is included in the data sample.

In this chapter we discuss the role of conscious and unconscious inference in theories of delusion formation. Two competing accounts aim to shed light on the formation of delusions: according to explanationism, the delusional belief is offered as an explanation for anomalous experience; according to the endorsement theory, the delusional belief is an acknowledgement that the anomalous experience is veridical. Whereas explanationists argue that the delusional belief is inferred from experience, endorsement theorists argue that there need be no (...) class='Hi'>inference from the experience to the delusional belief. Here we put pressure on the idea that the two accounts can be distinguished on the basis of whether inference is involved in delusion formation and argue that they are both compatible with the increasingly influential idea that Bayesian inference is responsible for the adoption of the delusional belief. One of the models of delusion formation focusing on Bayesian inference is the Coltheart model (Coltheart, Menzies, and Sutton, 2010) which we discuss in detail in the chapter. We show that, given its features, the model does not fit neatly under either explanationism or the endorsement theory. (shrink)

Much evidence has accumulated in favor of such a dual view of reasoning. There is however some vagueness in the way the two systems are characterized. Instead of a principled distinction, we are presented with a bundle of contrasting features - slow/fast, automatic/controlled, explicit/implicit, associationist/rule based, modular/central - that, depending on the specific dual process theory, are attributed more or less exclusively to one of the two systems. As Evans states in a recent review, “it would then be helpful to (...) have some clear basis for this distinction”; he also suggests that “we might be better off talking about type 1 and type 2 processes” rather than systems. We share the intuitions that drove the development of dual system theories. Our goal here is to propose in the same spirit a principled distinction between two types of inferences: ‘intuitive inference’ and ‘reflective inference’. We ground this distinction in a massively modular view of the human mind where metarepresentational modules play an important role in explaining the peculiarities of human psychological evolution. We defend the hypothesis that the main function of reflective inference is to produce and evaluate arguments occurring in interpersonal communication. This function, we claim, helps explain important aspects of reasoning. We review some of the existing evidence and argue that it gives support to this approach. (shrink)

Probabilistic Relational Models (PRMs) are a framework for compactly representing uncertainties (actually probabilities). They result from the combination of Bayesian Networks (BNs), Object-Oriented languages, and relational models. They are specifically designed for their efficient construction, maintenance and exploitation for very large scale problems, where BNs are known to perform poorly. Actually, in large-scale problems, it is often the case that BNs result from the combination of patterns (small BN fragments) repeated many times. PRMs exploit this feature by defining these (...) patterns only once (the so-called PRM’s classes) and using them through multiple instances, as prescribed by the Object-Oriented paradigm. This design induces low construction and maintenance costs. In addition, by exploiting the classes’ structures, PRM’s state-of-the-art inference algorithm “Structured Variable Elimination” (SVE) significantly outperforms BN’s classical inference algorithms (e.g., Variable Elimination, VE; Local Conditioning, LC). SVE is actually an extension of VE that simply exploits classes to avoid redundant computations. In this article, we show that SVE can be enhanced using LC. Although LC is often thought as being outperformed by VE-like algorithms in BNs, we do think that it should play an important role for PRMs because its features are very well suited for best exploiting PRM classes. In this article, relying on Faÿ and Jaffray’s works, we show how LC can be used in conjunction with VE and deduce an extension of SVE that outperforms it for large-scale problems. Numerical experiments highlight the practical efficiency of our algorithm. (shrink)

In a recent book, Anjan Chakravartty builds a case for a particular conception of the relationship of science to metaphysics. The main novel feature in his account of scientific ontology is his construction of a metaphysical distance measure. Some ontological claims are close to the science that informs those claims, and some are further away. The distance is a measure of the epistemic risk one takes in asserting the claim: the further from the empirical base, the greater the risk. (...) But conversely, claims far from the empirical base can serve explanatory and unificatory purposes regarding claims closer in. Since different philosophers have different attitudes towards epistemic risk, Chakravartty endorses a pluralism about scientific ontology. Chakravartty illustrates his arguments concerning scientific ontology by considering what we can infer from the Standard Model of particle physics concerning the ontological status of the structures and entities it describes. I argue that, while this case study initially appears to support Chakravartty’s arguments, on closer inspection it suggests a more deflationary approach to scientific ontology. (shrink)

In this essay, we examine the use of resting state fMRI data for psychological inferences. We argue that resting state studies hold the paired promises of discovering novel functional brain networks, and of avoiding some of the limitations of task-based fMRI. However, we argue that the very features of experimental design that enable resting state fMRI to support exploratory science also generate a novel confound. We argue that seemingly key features of resting state functional connectivity networks may be artefacts resulting (...) from sampling a ‘mixture distribution’ of diverse brain networks active at different times during the scan. We explore the consequences of this ‘mixture view’ for attempts to theorize about the cognitive or psychological functions of resting state networks, as well as the value of exploratory experiments. (shrink)

We incorporate social reasoning about groups of informants into a model of word learning, and show that the model accounts for infant looking behavior in tasks of both word learning and recognition. Simulation 1 models an experiment where 16-month-old infants saw familiar objects labeled either correctly or incorrectly, by either adults or audio talkers. Simulation 2 reinterprets puzzling data from the Switch task, an audiovisual habituation procedure wherein infants are tested on familiarized associations between novel objects and labels. Eight-month-olds outperform (...) 14-month-olds on the Switch task when required to distinguish labels that are minimal pairs, but 14-month-olds' performance is improved by habituation stimuli featuring multiple talkers. Our modeling results support the hypothesis that beliefs about knowledgeability and group membership guide infant looking behavior in both tasks. These results show that social and linguistic development interact in non-trivial ways, and that social categorization findings in developmental psychology could have substantial implications for understanding linguistic development in realistic settings where talkers vary according to observable features correlated with social groupings, including linguistic, ethnic, and gendered groups. (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)

A main message from the causal modelling literature in the last several decades is that under some plausible assumptions, there can be statistically consistent procedures for inferring (features of) the causal structure of a set of random variables from observational data. But whether we can control the error probabilities with a finite sample size depends on the kind of consistency the procedures can achieve. It has been shown that in general, under the standard causal Markov and Faithfulness assumptions, the procedures (...) can only be pointwise but not uniformly consistent without substantial background knowledge. This implies the impossibility of choosing a finite sample size to control the worst case error probabilities. In this paper, I consider the simpler task of inferring causal directions when the skeleton of the causal structure is known, and establish a similarly negative result concerning the possibility of controlling error probabilities. Although the result is negative in form, it has an interesting positive implication for causal discovery methods. (shrink)

The idea that knowledge can be extended by inference from what is known seems highly plausible. Yet, as shown by familiar preface paradox and lottery-type cases, the possibility of aggregating uncertainty casts doubt on its tenability. We show that these considerations go much further than previously recognized and significantly restrict the kinds of closure ordinary theories of knowledge can endorse. Meeting the challenge of uncertainty aggregation requires either the restriction of knowledge-extending inferences to single premises, or eliminating epistemic uncertainty (...) in known premises. The first strategy, while effective, retains little of the original idea—conclusions even of modus ponens inferences from known premises are not always known. We then look at the second strategy, inspecting the most elaborate and promising attempt to secure the epistemic role of basic inferences, namely Timothy Williamson’s safety theory of knowledge. We argue that while it indeed has the merit of allowing basic inferences such as modus ponens to extend knowledge, Williamson’s theory faces formidable difficulties. These difficulties, moreover, arise from the very feature responsible for its virtue- the infallibilism of knowledge. (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)

A feature is central to a concept to the extent that other features depend on it. Four studies tested the hypothesis that people will project a feature from a base concept to a target concept to the extent that they believe the feature is central to the two concepts. This centrality hypothesis implies that feature projection is guided by a principle that aims to maximize the structural commonality between base and target concepts. Participants were told that (...) a category has two or three novel features. One feature was the most central in that more properties depended on it. The extent to which the target shared the feature's dependencies was manipulated by varying the similarity of category pairs. Participants' ratings of the likelihood that each feature would hold in the target category support the centrality hypothesis with both natural kind and artifact categories and with both well-specified and vague dependency structures. (shrink)

The goal of this dissertation is to offer a practice-based account of intentionality. My aim is to examine what sort of practices agents have to engage in so as to count as talking and thinking about the way the world is – that is, what sort of practices count as representational. Representational practices answer to the way the world is: what is correct within such practices depends on the way things are, rather than on the attitudes of agents. An account (...) of representation must explain how such objective standards of correctness are introduced in human practices: one must explain how the world gets to have a say in what is correct in human discursive practices. Roughly, my proposal is that human discursive practices become responsive to the way things are by virtue of involving practical interactions with the world. The outcomes of these interactions depend on the way the world is and the evaluation of such outcomes contributes to determining which moves within the practice count as correct. Due to our practical engagement with the environment, thus, the world gets to constrain discursive practices. In order to flesh out my proposal, I develop a practice-based characterization of intentional or representational content. On this sort of approach, expressing intentional contents is seen as a matter of playing a certain role in relevant practices, rather than as a matter of engaging in some word-world relation. The expression of content, thus, is explained in terms of use. In particular, I adopt an inferentialist perspective, according to which discursive moves express contents because of their role in practices of giving and asking for reasons. I investigate how practical engagement with the environment introduces friction with the world in these practices of giving and asking for reasons. One of the main conclusions reached in the dissertation is that defeasibility is an essential feature of objective representational practices – so that attributions of representational correctness are revisable in an open-ended way. The discussion of defeasible reasoning – and of the way in which defeasibility shapes human representational practices – is a central point of this dissertation. (shrink)

Alcohol addiction can lead to health and social problems. It can also affect people's emotions. Emotion plays a key role in human communications. It is important to recognize the people's emotions at the court and infer the association between the people's emotions and the alcohol addiction. However, it is challenging to recognize people's emotions efficiently in the courtroom. Furthermore, to the best of our knowledge, no existing work is about the association between alcohol addiction and people's emotions at court. In (...) this paper, we propose a deep learning framework for predicting people's emotions based on sound perception, named ResCNN-SER. The proposed model combines several neural network-based components to extract the features of the speech signals and predict the emotions. The evaluation shows that the proposed model performs better than existing methods. By applying ResCNN-SER for emotion recognition based on people's voices at court, we infer the association between alcohol addiction and the defendant's emotion at court. Based on the sound source data from 54 trial records, we found that the defendants with alcohol addiction tend to get angry or fearful more easily at court comparing with defendants without alcohol addiction. (shrink)

This chapter explores the idea that the need to establish common knowledge is one feature that makes social cognition stand apart in important ways from cognition in general. We develop this idea on the background of the claim that social cognition is nothing but a type of causal inference. We focus on autism as our test-case, and propose that a specific type of problem with common knowledge processing is implicated in challenges to social cognition in autism spectrum disorder (...) (ASD). This problem has to do with the individual’s assessment of the reliability of messages that are passed between people as common knowledge emerges. The proposal is developed on the background of our own empirical studies and outlines different ways common knowledge might be comprised. We discuss what these issues may tell us about ASD, about the relation between social and non-social cognition, about social objects, and about the dynamics of social networks. (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)

Twenty-four aphasic and fifteen right brain-damaged subjects were compared on their ability to identify the objects whose use was depicted in a series of twenty videotaped pantomimes. Aphasics were inferior to right brain-damaged patients in inferring object use. Success was correlated with Performance IQ, but not with language measures. Analysis of movement features contributing to subjects' choices reveal speed of movement and object weight to be the most robust and hand shape and size to be the most fragile.

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.

Although Benjamin Schnieder’s theory of the “ordinary conception” of properties successfully handles paradoxical properties—particularly, the property of non-self-instantiation—it fails to account for ordinary, non-pathological cases. The theory allows the inference of ‘a has the property of being F’ only given F(a) and the prior assertibility of ‘the property of being F can exist’. While this allows us to block an inference to a contradiction, it also blocks all of the non-pathological instances of the inference from ‘a is (...) F’ to ‘a has the property of being F’. It thereby fails both as a theory of the ordinary conception and as a replacement of the ordinary notion, assuming the latter is defective. (shrink)

It is possible to learn multiple layers of non-linear features by backpropagating error derivatives through a feedforward neural network. This is a very effective learning procedure when there is a huge amount of labeled training data, but for many learning tasks very few labeled examples are available. In an effort to overcome the need for labeled data, several different generative models were developed that learned interesting features by modeling the higher order statistical structure of a set of input vectors. One (...) of these generative models, the restricted Boltzmann machine (RBM), has no connections between its hidden units and this makes perceptual inference and learning much simpler. More significantly, after a layer of hidden features has been learned, the activities of these features can be used as training data for another RBM. By applying this idea recursively, it is possible to learn a deep hierarchy of progressively more complicated features without requiring any labeled data. This deep hierarchy can then be treated as a feedforward neural network which can be discriminatively fine-tuned using backpropagation. Using a stack of RBMs to initialize the weights of a feedforward neural network allows backpropagation to work effectively in much deeper networks and it leads to much better generalization. A stack of RBMs can also be used to initialize a deep Boltzmann machine that has many hidden layers. Combining this initialization method with a new method for fine-tuning the weights finally leads to the first efficient way of training Boltzmann machines with many hidden layers and millions of weights. (shrink)