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Neuroscientists routinely use reverse inference (RI) to draw conclusions about cognitive processes from neural activation data. However, despite its widespread use, the methodological status of RI is a matter of ongoing controversy, with some critics arguing that it should be rejected wholesale on the grounds that it instantiates a deductively invalid argument form. In response to these critiques, some have proposed to conceive of RI as a form of abduction or inference to the best explanation (IBE). We side with this response but at the same time argue that a defense of RI requires more than identifying it as a form of IBE. In this paper, we give an analysis of what determines the quality of an RI conceived as an IBE and on that basis argue that whether an RI is warranted needs to be decided on a case-by-case basis. Support for our argument will come from a detailed methodological discussion of RI in cognitive neuroscience in light of what the recent literature on IBE has identified as the main quality indicators for IBEs. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.shpsa.2008.12.013 Byline: Igor Douven, Stefaan E. Cuypers Keywords: Testimony; Justification; Internalism; Social epistemology; Elizabeth Fricker Abstract: Elizabeth Fricker has recently proposed a principle aimed at stating the necessary and sufficient conditions for testimonial justification. Her proposal entails that a hearer is justified in believing a speaker's testimony only if she recognizes the speaker to be trustworthy, which, given Fricker's internalist commitments, requires the hearer to have within her epistemic purview grounds which justify belief in the speaker's trustworthiness. We argue that, as it stands, Fricker's principle is too demanding, and we propose some amendments to it. We further discuss the viability of her internalist approach to testimony. Author Affiliation: Institute of Philosophy, University of Leuven, Kardinaal Mercierplein 2 - bus 3200, B-3000 Leuven, Belgium Article History: Received 7 February 2008; Revised 10 March 2008

The bounded confidence model has become a popular tool for studying communities of epistemically interacting agents. The model makes the idealizing assumption that all agents always have access to all other agents' belief states. We draw on resources from network epistemology to do away with this assumption. In the model to be proposed, we impose an explicit communication network on a community, due to which each agent has access to the beliefs of only a selection of other agents. A much-discussed result from network epistemology shows that densely connected communication networks are not always preferable to sparser networks. The aim of this paper is to investigate whether there are any noteworthy network effects in a version of the bounded confidence model augmented with communication networks, and in particular whether the aforementioned result from network epistemology can be replicated in that version. [ABSTRACT FROM AUTHOR]

There is growing evidence that explanatory considerations influence how people change their degrees of belief in light of new information. Recent studies indicate that this influence is systematic and may result from people's following a probabilistic update rule. While formally very similar to Bayes' rule, the rule or rules people appear to follow are different from, and inconsistent with, that better-known update rule. This raises the question of the normative status of those updating procedures. Is the role explanation plays in people's updating their degrees of belief a bias? Or are people right to update on the basis of explanatory considerations, in that this offers benefits that could not be had otherwise? Various philosophers have argued that any reasoning at deviance with Bayesian principles is to be rejected, and so explanatory reasoning, insofar as it deviates from Bayes' rule, can only be fallacious. We challenge this claim by showing how the kind of explanation-based update rules to which people seem to adhere make it easier to strike the best balance between being fast learners and being accurate learners. Borrowing from the literature on ecological rationality, we argue that what counts as the best balance is intrinsically context-sensitive, and that a main advantage of explanatory update rules is that, unlike Bayes' rule, they have an adjustable parameter which can be fine-tuned per context. The main methodology to be used is agent-based optimization, which also allows us to take an evolutionary perspective on explanatory reasoning. • Uses computer simulations to show the ecological rationality of explanatory reasoning. • Connects computational results on explanatory reasoning with empirical results from psychology. • Shows the standard arguments in favor of Bayesian updating to be flawed. • Links to computer code that readers can easily use to run their own simulations and thereby to make further comparisons between update rules. [ABSTRACT FROM AUTHOR]

That one's degrees of belief at any one time obey the axioms of probability theory is widely regarded as a necessary condition for static rationality. Many theorists hold that it is also a sufficient condition, but according to critics this yields too subjective an account of static rationality. However, there are currently no good proposals as to how to obtain a tenable stronger probabilistic theory of static rationality. In particular, the idea that one might achieve the desired strengthening by adding some symmetry principle to the probability axioms has appeared hard to maintain. Starting from an idea of Carnap and drawing on relatively recent work in cognitive science, this paper argues that conceptual spaces provide the tools to devise an objective probabilistic account of static rationality. Specifically, we propose a principle that derives prior degrees of belief from the geometrical structure of concepts. [ABSTRACT FROM AUTHOR]

That one's degrees of belief at any one time obey the axioms of probability theory is widely regarded as a necessary condition for static rationality. Many theorists hold that it is also a sufficient condition, but according to critics this yields too subjective an account of static rationality. However, there are currently no good proposals as to how to obtain a tenable stronger probabilistic theory of static rationality. In particular, the idea that one might achieve the desired strengthening by adding some symmetry principle to the probability axioms has appeared hard to maintain. Starting from an idea of Carnap and drawing on relatively recent work in cognitive science, this paper argues that conceptual spaces provide the tools to devise an objective probabilistic account of static rationality. Specifically, we propose a principle that derives prior degrees of belief from the geometrical structure of concepts. [ABSTRACT FROM AUTHOR]

Philosophers have argued that people ought to change their graded beliefs via Bayes' rule. Recent work in psychology indicates that people sometimes violate that rule by attending to explanatory factors. Results from computational modeling suggest that such violations may actually be rational. [ABSTRACT FROM AUTHOR]

Bayesians understand the notion of evidential support in terms of probability raising. Little is known about the logic of the evidential support relation, thus understood. We investigate a number of prima facie plausible candidate logical principles for the evidential support relation and show which of these principles the Bayesian evidential support relation does and which it does not obey. We also consider the question which of these principles hold for a stronger notion of evidential support. [ABSTRACT FROM AUTHOR]

• Extends inferentialism and HIT to probabilities of conditionals. • Shows that inference strength accurately predicts the probabilities of conditionals. • Compares HIT with its main rival, the suppositional account. According to the philosophical theory of inferentialism and its psychological counterpart, Hypothetical Inferential Theory (HIT), the meaning of an indicative conditional centrally involves the strength of the inferential connection between its antecedent and its consequent. This paper states, for the first time, the implications of HIT for the probabilities of conditionals. We report two experiments comparing these implications with those of the suppositional account of conditionals, according to which the probability of a conditional equals the corresponding conditional probability. A total of 358 participants were presented with everyday conditionals across three different tasks: judging the probability of the conditionals; judging the corresponding conditional probabilities; and judging the strength of the inference from antecedent to consequent. In both experiments, we found inference strength to be a much stronger predictor of the probability of conditionals than conditional probability, thus supporting HIT. [ABSTRACT FROM AUTHOR]

It has been claimed that epistemic peers, upon discovering that they disagree on some issue, should give up their opposing views and ‘split the difference’. The present paper challenges this claim by showing, with the help of computer simulations, that what the rational response to the discovery of peer disagreement is—whether it is sticking to one’s belief or splitting the difference—depends on factors that are contingent and highly context-sensitive. [Copyright &y& Elsevier]