Search results for 'Confirmability' (try it on Scholar)

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  1. R. G. Swinburne (1974). Meaningfulness Without Confirmability: A Reply. Analysis 35 (1):22 - 27.score: 18.0
    IN THE COURSE OF "CONFIRMABILITY AND FACTUAL MEANINGFULNESS" ("ANALYSIS" VOL. 33) I ARGUED THAT THE CONFIRMATIONIST PRINCIPLE IS FALSE. THIS IS THE PRINCIPLE THAT A STATEMENT IS FACTUALLY MEANINGFUL IF AND ONLY IF IT IS AN OBSERVATION STATEMENT OR CONFIRMABLE BY OBSERVATION STATEMENTS. MY ARGUMENT CONSISTED IN PRODUCING EXAMPLES OF FACTUALLY MEANINGFUL STATEMENTS WHICH FAIL TO SATISFY THE PRINCIPLE. IN "CONFIRMABILITY AND MEANINGFULNESS" ("ANALYSIS" VOL. 34) R I SIKORA ARGUED THAT MY EXAMPLES DO NOT SUPPORT MY CONCLUSION. HERE (...)
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  2. R. G. Swinburne (1973). Confirmability and Factual Meaningfulness. Analysis 33 (3):71 - 76.score: 16.0
    THIS ARTICLE EXAMINES THE CONFIRMATIONIST PRINCIPLE, THAT A STATEMENT IS FACTUALLY MEANINGFUL IF AND ONLY IF IT IS AN OBSERVATION-STATEMENT, OR THERE ARE OBSERVATION STATEMENTS WHICH WOULD CONFIRM OR DISCONFIRM IT. THIS PRINCIPLE IS THE FINAL WEAK CLAIM OF VERIFICATIONISM. EVEN IF TRUE, IT WOULD NOT BE OF GREAT USE IN SORTING OUT THE MEANINGFUL FROM THE MEANINGFULNESS, BUT IT IS SHOWN CONCLUSIVELY TO BE FALSE. A CLAIM THAT THERE IS A DISCREPANCY BETWEEN THE BEST EVIDENCE THAT MEN WILL EVER (...)
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  3. Richard I. Sikora (1974). Confirmability and Meaningfulness. Analysis 34 (4):142 - 144.score: 15.0
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  4. G. Schlesinger (1974). Confirmation and Confirmability. Clarendon Press.score: 15.0
  5. James M. Brown (1976). Confirmation and Confirmability. Philosophical Studies 25:340-342.score: 15.0
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  6. G. Schlesinger (1968). Confirmability and Determinism. Philosophical Quarterly 18 (70):29-39.score: 15.0
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  7. R. I. Sikora (1975). Swinburne on Confirmability. Analysis 35 (6):195 -.score: 15.0
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  8. S. Cannavo (1979). Confirmation and Confirmability. International Studies in Philosophy 11:186-188.score: 15.0
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  9. T. D. P. (1976). Confirmation & Confirmability. Review of Metaphysics 29 (3):560-561.score: 15.0
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  10. D. H. M. Brooks (1980). Confirmability and Meaningfulness. Philosophical Papers 9 (1):41-44.score: 15.0
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  11. R. S. Woolhouse (1975). Confirmation and Confirmability. Philosophical Books 16 (2):27-29.score: 15.0
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  12. Ben Jeffares (2008). Testing Times: Confirmation in the Historical Sciences. Dissertation, Australian National Universityscore: 8.0
    In this thesis, I argue that a good historical science will have the following characteristics: Firstly, it will seek to construct causal histories of the past. Secondly, the construction of these causal histories will utilise well-tested regularities of science. Additionally, well-tested regularities will secure the link between observations of physical traces and the causal events of interest. However, the historical sciences cannot use these regularities in a straightforward manner. The regularities must accommodate the idiosyncrasies of the past, and the degradation (...)
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  13. Darren Bradley & Branden Fitelson (2003). Monty Hall, Doomsday and Confirmation. Analysis 63 (277):23–31.score: 8.0
    We give an analysis of the Monty Hall problem purely in terms of confirmation, without making any lottery assumptions about priors. Along the way, we show the Monty Hall problem is structurally identical to the Doomsday Argument.
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  14. Peter Kung (2010). On Having No Reason: Dogmatism and Bayesian Confirmation. Synthese 177 (1):1 - 17.score: 8.0
    Recently in epistemology a number of authors have mounted Bayesian objections to dogmatism. These objections depend on a Bayesian principle of evidential confirmation: Evidence E confirms hypothesis H just in case Pr(H|E) > Pr(H). I argue using Keynes' and Knight's distinction between risk and uncertainty that the Bayesian principle fails to accommodate the intuitive notion of having no reason to believe. Consider as an example an unfamiliar card game: at first, since you're unfamiliar with the game, you assign credences based (...)
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  15. David Atkinson (2012). Confirmation and Justification. A Commentary on Shogenji's Measure. Synthese 184 (1):49-61.score: 8.0
    So far no known measure of confirmation of a hypothesis by evidence has satisfied a minimal requirement concerning thresholds of acceptance. In contrast, Shogenji’s new measure of justification (Shogenji, Synthese, this number 2009) does the trick. As we show, it is ordinally equivalent to the most general measure which satisfies this requirement. We further demonstrate that this general measure resolves the problem of the irrelevant conjunction. Finally, we spell out some implications of the general measure for the Conjunction Effect; in (...)
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  16. Nevin Climenhaga (2013). A Problem for the Alternative Difference Measure of Confirmation. Philosophical Studies 164 (3):643-651.score: 8.0
    Among Bayesian confirmation theorists, several quantitative measures of the degree to which an evidential proposition E confirms a hypothesis H have been proposed. According to one popular recent measure, s, the degree to which E confirms H is a function of the equation P(H|E) − P(H|~E). A consequence of s is that when we have two evidential propositions, E1 and E2, such that P(H|E1) = P(H|E2), and P(H|~E1) ≠ P(H|~E2), the confirmation afforded to H by E1 does not equal the (...)
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  17. Christopher Clarke (2014). Neuroeconomics and Confirmation Theory. Philosophy of Science 81 (2):195-215.score: 8.0
    Neuroeconomics is a research programme founded on the thesis that cognitive and neurobiological data constitute evidence for answering economic questions. I employ confirmation theory in order to reject arguments both for and against neuroeconomics. I also emphasize that some arguments for neuroeconomics will not convince the skeptics because these arguments make a contentious assumption: economics aims for predictions and deep explanations of choices in general. I then argue for neuroeconomics by appealing to a much more restrictive (and thereby skeptic-friendly) characterization (...)
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  18. Jeanne Peijnenburg (2012). A Case of Confusing Probability and Confirmation. Synthese 184 (1):101-107.score: 8.0
    Tom Stoneham put forward an argument purporting to show that coherentists are, under certain conditions, committed to the conjunction fallacy. Stoneham considers this argument a reductio ad absurdum of any coherence theory of justification. I argue that Stoneham neglects the distinction between degrees of confirmation and degrees of probability. Once the distinction is in place, it becomes clear that no conjunction fallacy has been committed.
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  19. Roberto Festa (2012). “For Unto Every One That Hath Shall Be Given”. Matthew Properties for Incremental Confirmation. Synthese 184 (1):89-100.score: 8.0
    Confirmation of a hypothesis by evidence can be measured by one of the so far known incremental measures of confirmation. As we show, incremental measures can be formally defined as the measures of confirmation satisfying a certain small set of basic conditions. Moreover, several kinds of incremental measure may be characterized on the basis of appropriate structural properties. In particular, we focus on the so-called Matthew properties: we introduce a family of six Matthew properties including the reverse Matthew effect; we (...)
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  20. Patrick Maher (1996). Subjective and Objective Confirmation. Philosophy of Science 63 (2):149-174.score: 8.0
    Confirmation is commonly identified with positive relevance, E being said to confirm H if and only if E increases the probability of H. Today, analyses of this general kind are usually Bayesian ones that take the relevant probabilities to be subjective. I argue that these subjective Bayesian analyses are irremediably flawed. In their place I propose a relevance analysis that makes confirmation objective and which, I show, avoids the flaws of the subjective analyses. What I am proposing is in some (...)
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  21. Brian Laetz (2011). Does the Bayesian Solution to the Paradox of Confirmation Really Support Bayesianism? European Journal for Philosophy of Science 1 (1):39-46.score: 8.0
    Bayesians regard their solution to the paradox of confirmation as grounds for preferring their theory of confirmation to Hempel’s. They point out that, unlike Hempel, they can at least say that a black raven confirms “All ravens are black” more than a white shoe. However, I argue that this alleged advantage is cancelled out by the fact that Bayesians are equally committed to the view that a white shoe confirms “All non-black things are non-ravens” less than a black raven. In (...)
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  22. Branden Fitelson (2007). Likelihoodism, Bayesianism, and Relational Confirmation. Synthese 156 (3):473 - 489.score: 8.0
    Likelihoodists and Bayesians seem to have a fundamental disagreement about the proper probabilistic explication of relational (or contrastive) conceptions of evidential support (or confirmation). In this paper, I will survey some recent arguments and results in this area, with an eye toward pinpointing the nexus of the dispute. This will lead, first, to an important shift in the way the debate has been couched, and, second, to an alternative explication of relational support, which is in some sense a "middle way" (...)
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  23. Gregory Wheeler & Richard Scheines (2013). Coherence and Confirmation Through Causation. Mind 122 (485):135-170.score: 8.0
    Coherentism maintains that coherent beliefs are more likely to be true than incoherent beliefs, and that coherent evidence provides more confirmation of a hypothesis when the evidence is made coherent by the explanation provided by that hypothesis. Although probabilistic models of credence ought to be well-suited to justifying such claims, negative results from Bayesian epistemology have suggested otherwise. In this essay we argue that the connection between coherence and confirmation should be understood as a relation mediated by the causal relationships (...)
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  24. Darrell P. Rowbottom (forthcoming). Information Versus Knowledge in Confirmation Theory. Logique Et Analyse.score: 8.0
    I argue that so-called 'background knowledge' in confirmation theory has little, if anything, to do with 'knowledge' in the sense of mainstream epistemology. I argue that it is better construed as 'background information', which need not be believed in, justified, or true.
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  25. Xinguo Dun (2007). Queries on Hempel's Solution to the Paradoxes of Confirmation. Frontiers of Philosophy in China 2 (1):131-139.score: 8.0
    To solve the highly counterintuitive paradox of confirmation represented by the statement, “A pair of red shoes confirms that all ravens are black,” Hempel employed a strategy that retained the equivalence condition but abandoned Nicod’s irrelevance condition. However, his use of the equivalence condition is fairly ad hoc, raising doubts about its applicability to this problem. Furthermore, applying the irrelevance condition from Nicod’s criterion does not necessarily lead to paradoxes, nor does discarding it prevent the emergence of paradoxes. Hempel’s approach (...)
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  26. Katya Tentori & Vincenzo Crupi (2012). How the Conjunction Fallacy is Tied to Probabilistic Confirmation: Some Remarks on Schupbach (2009). Synthese 184 (1):3-12.score: 8.0
    Crupi et al. (Think Reason 14:182–199, 2008) have recently advocated and partially worked out an account of the conjunction fallacy phenomenon based on the Bayesian notion of confirmation. In response, Schupbach (2009) presented a critical discussion as following from some novel experimental results. After providing a brief restatement and clarification of the meaning and scope of our original proposal, we will outline Schupbach’s results and discuss his interpretation thereof arguing that they do not actually undermine our point of view if (...)
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  27. Jonah N. Schupbach (2012). Is the Conjunction Fallacy Tied to Probabilistic Confirmation? Synthese 184 (1):13-27.score: 8.0
    Crupi et al. (2008) offer a confirmation-theoretic, Bayesian account of the conjunction fallacy—an error in reasoning that occurs when subjects judge that Pr( h 1 & h 2 | e ) > Pr( h 1 | e ). They introduce three formal conditions that are satisfied by classical conjunction fallacy cases, and they show that these same conditions imply that h 1 & h 2 is confirmed by e to a greater extent than is h 1 alone. Consequently, they suggest (...)
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  28. Joseph L. Austerweil & Thomas L. Griffiths (2011). Seeking Confirmation Is Rational for Deterministic Hypotheses. Cognitive Science 35 (3):499-526.score: 8.0
    The tendency to test outcomes that are predicted by our current theory (the confirmation bias) is one of the best-known biases of human decision making. We prove that the confirmation bias is an optimal strategy for testing hypotheses when those hypotheses are deterministic, each making a single prediction about the next event in a sequence. Our proof applies for two normative standards commonly used for evaluating hypothesis testing: maximizing expected information gain and maximizing the probability of falsifying the current hypothesis. (...)
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  29. Luca Moretti (2007). Ways in Which Coherence is Confirmation Conducive. Synthese 157 (3):309 - 319.score: 8.0
    Recent works in epistemology show that the claim that coherence is truth conducive – in the sense that, given suitable ceteris paribus conditions, more coherent sets of statements are always more probable – is dubious and possibly false. From this, it does not follows that coherence is a useless notion in epistemology and philosophy of science. Dietrich and Moretti (Philosophy of science 72(3): 403–424, 2005) have proposed a formal of account of how coherence is confirmation conducive—that is, of how the (...)
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  30. Maximillian Schlosshauer & Gregory Wheeler (2011). Focused Correlation, Confirmation, and the Jigsaw Puzzle of Variable Evidence. Philosophy of Science 78 (3):376-92.score: 8.0
    Focused correlation compares the degree of association within an evidence set to the degree of association in that evidence set given that some hypothesis is true. A difference between the confirmation lent to a hypothesis by one evidence set and the confirmation lent to that hypothesis by another evidence set is robustly tracked by a difference in focused correlations of those evidence sets on that hypothesis, provided that all the individual pieces of evidence are equally, positively relevant to that hypothesis. (...)
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  31. William Roche & Tomoji Shogenji (2013). Confirmation, Transitivity, and Moore: The Screening-Off Approach. Philosophical Studies (3):1-21.score: 8.0
    It is well known that the probabilistic relation of confirmation is not transitive in that even if E confirms H1 and H1 confirms H2, E may not confirm H2. In this paper we distinguish four senses of confirmation and examine additional conditions under which confirmation in different senses becomes transitive. We conduct this examination both in the general case where H1 confirms H2 and in the special case where H1 also logically entails H2. Based on these analyses, we argue that (...)
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  32. Martin Jones & Robert Sugden (2001). Positive Confirmation Bias in the Acquisition of Information. Theory and Decision 50 (1):59-99.score: 8.0
    An experiment is reported which tests for positive confirmation bias in a setting in which individuals choose what information to buy, prior to making a decision. The design – an adaptation of Wason's selection task – reveals the use that subjects make of information after buying it. Strong evidence of positive confirmation bias, in both information acquisition and information use, is found; and this bias is found to be robust to experience. It is suggested that the bias results from a (...)
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  33. Elisabeth A. Lloyd (1987). Confirmation of Ecological and Evolutionary Models. Biology and Philosophy 2 (3):277-293.score: 8.0
    In this paper I distinguish various ways in which empirical claims about evolutionary and ecological models can be supported by data. I describe three basic factors bearing on confirmation of empirical claims: fit of the model to data; independent testing of various aspects of the model, and variety of evident. A brief description of the kinds of confirmation is followed by examples of each kind, drawn from a range of evolutionary and ecological theories. I conclude that the greater complexity and (...)
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  34. Jesús Zamora-Bonilla (2013). Why Are Good Theories Good? Reflections on Epistemic Values, Confirmation, and Formal Epistemology. Synthese 190 (9):1533-1553.score: 8.0
    Franz Huber’s (2008a) attempt to unify inductivist and hypothetico-deductivist intuitions on confirmation by means of a single measure are examined and compared with previous work on the theory of verisimilitude or truthlikeness. The idea of connecting ‘the logic of confirmation’ with ‘the logic of acceptability’ is also critically discussed, and it is argued that ‘acceptability’ takes necessarily into account some pragmatic criteria, and that at least two normative senses of ‘acceptability’ must be distinguished: ‘acceptable’ in the sense of ‘being allowed (...)
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  35. Aysel Dogan (2005). Confirmation of Scientific Hypotheses as Relations. Journal for General Philosophy of Science 36 (2):243 - 259.score: 8.0
    In spite of several attempts to explicate the relationship between a scientific hypothesis and evidence, the issue still cries for a satisfactory solution. Logical approaches to confirmation, such as the hypothetico-deductive method and the positive instance account of confirmation, are problematic because of their neglect of the semantic dimension of hypothesis confirmation. Probabilistic accounts of confirmation are no better than logical approaches in this regard. An outstanding probabilistic account of confirmation, the Bayesian approach, for instance, is found to be defective (...)
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  36. Gregor Betz (2013). Revamping Hypothetico-Deductivism: A Dialectic Account of Confirmation. [REVIEW] Erkenntnis 78 (5):991-1009.score: 8.0
    We use recently developed approaches in argumentation theory in order to revamp the hypothetico-deductive model of confirmation, thus alleviating the well-known paradoxes the H-D account faces. More specifically, we introduce the concept of dialectic confirmation on the background of the so-called theory of dialectical structures (Betz 2010, 2012b). Dialectic confirmation generalises hypothetico-deductive confirmation and mitigates the raven paradox, the grue paradox, the tacking paradox, the paradox from conceptual difference, and the problem of surprising evidence.
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  37. Valeriano Iranzo & Ignacio Martínez de Lejarza (2013). On Ratio Measures of Confirmation. Journal for General Philosophy of Science 44 (1):193-200.score: 8.0
    There are different Bayesian measures to calculate the degree of confirmation of a hypothesis H in respect of a particular piece of evidence E. Zalabardo (Analysis 69:630–635, 2009) is a recent attempt to defend the likelihood-ratio measure (LR) against the probability-ratio measure (PR). The main disagreement between LR and PR concerns their sensitivity to prior probabilities. Zalabardo invokes intuitive plausibility as the appropriate criterion for choosing between them. Furthermore, he claims that it favours the ordering of pairs evidence/hypothesis generated by (...)
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  38. Theo A. F. Kuipers (2012). A Realist Partner for Linda: Confirming a Theoretical Hypothesis More Than its Observational Sub-Hypothesis. Synthese 184 (1):63-71.score: 8.0
    It is argued that the conjunction effect has a disjunctive analog of strong interest for the realism–antirealism debate. It is possible that a proper theory is more confirmed than its (more probable) observational sub-theory and hence than the latter’s disjunctive equivalent, i.e., the disjunction of all proper theories that are empirically equivalent to the given one. This is illustrated by a toy model.
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  39. David H. Glass & Mark McCartney (forthcoming). A New Argument for the Likelihood Ratio Measure of Confirmation. Acta Analytica:1-7.score: 8.0
    This paper presents a new argument for the likelihood ratio measure of confirmation by showing that one of the adequacy criteria used in another argument (Zalabardo Analysis 69: 630–635, 2009) can be replaced by a more plausible and better supported criterion which is a special case of the weak likelihood principle. This new argument is also used to show that the likelihood ratio measure is to be preferred to a measure that has recently received support in the literature.
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  40. Ralf-Thomas Klein (2014). Where There Are Internal Defeaters, There Are “Confirmers”. Synthese 191 (12):2715-2728.score: 8.0
    There is widespread consensus that there are undercutting and rebutting defeaters that diminish or destroy the warrant of a belief B. I argue that there are counterparts of defeaters: the counterparts of undercutting defeaters are “requirement fulfillment beliefs”, the counterparts of rebutting defeaters are “consistency beliefs”. These beliefs confirm the warrant of B, I therefore call them “confirmers”.
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  41. Alexander R. Pruss (2014). Independent Tests and the Log‐Likelihood‐Ratio Measure of Confirmation. Thought: A Journal of Philosophy 3 (2):124-135.score: 8.0
    I shall offer some very plausible assumptions for the measure of confirmation and show that they imply that E confirms H relative to background K to degree f(PK(E|H)/PK(E| ∼ H)), where f is a strictly increasing function. An additional assumption about how measures of confirmation combine then makes f be proportional to a logarithm.
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  42. Harald Dickson (1990). Evidential Support and Undermining: Revision of Håkan Törnebohm's Theory of Confirmation. [REVIEW] Journal for General Philosophy of Science 21 (1):163-182.score: 8.0
    In 1975, 'An Essay on Knowledge Formation' by H. Törnebohm was published in this Journal. Its content in revised form was included in a work in Swedish of 1983 on knowledge development. HT defines his confirmation criterion in terms of a measure of truth degree T, which is based on a measure of matching M, which is also used as a measure of the degree to which proposition p (an hypothesis) is supported or undermined by another proposition q (the evidence (...)
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  43. David J. Buller (1993). Confirmation and the Computational Paradigm (Or: Why Do You Think They Call Itartificial Intelligence?). [REVIEW] Minds and Machines 3 (2):155-181.score: 8.0
    The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition and argue that (...)
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  44. Valeriano Iranzo & Ignacio Martínez de Lejarza (2013). On Ratio Measures of Confirmation. Journal for General Philosophy of Science 44 (1):193-200.score: 8.0
    There are different Bayesian measures to calculate the degree of confirmation of a hypothesis H in respect of a particular piece of evidence E. Zalabardo (Analysis 69:630–635, 2009) is a recent attempt to defend the likelihood-ratio measure (LR) against the probability-ratio measure (PR). The main disagreement between LR and PR concerns their sensitivity to prior probabilities. Zalabardo invokes intuitive plausibility as the appropriate criterion for choosing between them. Furthermore, he claims that it favours the ordering of pairs evidence/hypothesis generated by (...)
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  45. M. Harrell (1996). Confirmation Holism and Semantic Holism. Synthese 109 (1):63-101.score: 7.0
    Fodor and Lepore, in their recent book "Holism," maintain that if an inference from semantic anatomism to semantic holism is allowed, certain fairly deleterious consequences follow. In Section 1 Fodor and Lepore's terminology is construed and amended where necessary with the result that the aforementioned deleterious consequences are neither so apparent nor straightforward as they had suggested. In Section 2 their "Argument A" is considered in some detail. In Section 3 their "argument attributed to Quine" is examined at length and (...)
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  46. Vincenzo Crupi, Roberto Festa & Carlo Buttasi (2010). Toward a Grammar of Bayesian Confirmation. In M. Suàrez, M. Dorato & M. Redéi (eds.), EPSA Epistemology and Methodology of Science: Launch of the a European Philosophy of Science Association. Springer. 73--93.score: 7.0
  47. Franz Huber, Confirmation and Induction. Internet Encyclopedia of Philosophy.score: 7.0
  48. Alan Musgrave (1974). Logical Versus Historical Theories of Confirmation. British Journal for the Philosophy of Science 25 (1):1-23.score: 7.0
  49. Benjamin B. Rubinstein (1980). On the Psychoanalytic Theory of Unconscious Motivation and the Problem of its Confirmation. Noûs 14 (September):427-442.score: 7.0
  50. Gregory Wheeler & Richard Scheines (2011). Causation, Association and Confirmation. In Stephan Hartmann, Marcel Weber, Wenceslao Gonzalez, Dennis Dieks & Thomas Uebe (eds.), Explanation, Prediction, and Confirmation: New Trends and Old Ones Reconsidered. Springer. 37--51.score: 7.0
    Many philosophers of science have argued that a set of evidence that is "coherent" confirms a hypothesis which explains such coherence. In this paper, we examine the relationships between probabilistic models of all three of these concepts: coherence, confirmation, and explanation. For coherence, we consider Shogenji's measure of association (deviation from independence). For confirmation, we consider several measures in the literature, and for explanation, we turn to Causal Bayes Nets and resort to causal structure and its constraint on probability. All (...)
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