Many experiments suggest that participants are more critical of arguments that challenge their views or that come from untrustworthy sources. However, other results suggest that this might not be true of demonstrative arguments. A series of four experiments tested whether people are influenced by two factors when they evaluate demonstrative arguments: how confident they are in the answer being challenged by the argument, and how much they trust the source of the argument. Participants were not affected by their confidence in (...) the answer challenged by the argument. By contrast, they were sometimes affected by their trust in the argument’s source. Analyses of reaction times and transfer problems suggest that source trustworthiness did not directly affect argument evaluation, but affected instead the number of times the participants considered the arguments. Our results thus suggest that people can evaluate demonstrative arguments objectively. In conclusion, we defend the hypothesis that people might also be able to evaluate non-demonstrative arguments objectively. These results support the predictions of the argumentative theory of reasoning. (shrink)

Scientific cognition is a hard-won achievement, both from a historical point of view and a developmental point of view. Here, I review seven facets of lay cognition that run counter to, and often impede, scientific cognition: incompatible folk theories, missing ontologies, tolerance for shallow explanations, tolerance for contradictory explanations, privileging explanation over empirical data, privileging testimony over empirical data, and misconceiving the nature of science itself. Most of these facets have been investigated independent of the others, and I propose directions (...) for future research that might integrate them so as to explore potential commonalities among seemingly disparate obstacles to science learning, as well as potential strategies for bridging lay cognition and scientific cognition in the context of science education. (shrink)

Good informal arguments offer justification for their conclusions. They go wrong if the justifications double back, rendering the arguments circular. Circularity, however, is not necessarily a single property of an argument, but may depend on (a) whether the argument repeats an earlier claim, (b) whether the repetition occurs within the same line of justification, and (c) whether the claim is properly grounded in agreed‐upon information. The experiments reported here examine whether people take these factors into account in their judgments of (...) whether arguments are circular and whether they are reasonable. The results suggest that direct judgments of circularity depend heavily on repetition and structural role of claims, but only minimally on grounding. Judgments of reasonableness take repetition and grounding into account, but are relatively insensitive to structural role. (shrink)

Abstract How do people find arguments while engaged in a discussion? Following an analogy with visual search, a mechanism that performs this task is described. It is a metarepresentational device that examines representations in a mostly serial manner until it finds a good enough argument supporting one’s position. It is argued that the mechanism described in dual process theories as ‘system 2’, or analytic reasoning fulfills these requirements. This provides support for the hypothesis that reasoning serves an argumentative function. Content (...) Type Journal Article Pages 1-20 DOI 10.1007/s10503-011-9256-1 Authors Hugo Mercier, Philosophy, Politics and Economics Program, University of Pennsylvania, 313 Cohen Hall, 249 South 36th Street, Philadelphia, PA 19104, USA Journal Argumentation Online ISSN 1572-8374 Print ISSN 0920-427X. (shrink)

Some of the information we receive comes to us in an explicitly conditional form. It is an open question how to model the accommodation of such information in a Bayesian framework. This paper presents data suggesting that there may be no strictly Bayesian account of updating on conditionals. Specifically, the data seem to indicate that such updating at least sometimes proceeds on the basis of explanatory considerations, which famously have no home in standard Bayesian epistemology. The paper also proposes a (...) still broadly Bayesian model of updating on conditionals that explicitly factors in explanation. The model is shown to have clear empirical content and thus to be open to empirical testing. (shrink)

Norms—that is, specifications of what we ought to do—play a critical role in the study of informal argumentation, as they do in studies of judgment, decision-making and reasoning more generally. Specifically, they guide a recurring theme: are people rational? Though rules and standards have been central to the study of reasoning, and behavior more generally, there has been little discussion within psychology about why (or indeed if) they should be considered normative despite the considerable philosophical literature that bears on this (...) topic. In the current paper, we ask what makes something a norm, with consideration both of norms in general and a specific example: norms for informal argumentation. We conclude that it is both possible and desirable to invoke norms for rational argument, and that a Bayesian approach provides solid normative principles with which to do so. (shrink)

This paper brings together results from the philosophy and the psychology of explanation to argue that there are multiple concepts of explanation in human psychology. Specifically, it is shown that pluralism about explanation coheres with the multiplicity of models of explanation available in the philosophy of science, and it is supported by evidence from the psychology of explanatory judgment. Focusing on the case of a norm of explanatory power, the paper concludes by responding to the worry that if there is (...) a plurality of concepts of explanation, one will not be able to normatively evaluate what counts as good explanation. (shrink)

Explanation is a central concept in human psychology. Drawing upon philosophical theories of explanation, psychologists have recently begun to examine the relationship between explanation, probability and causality. Our study advances this growing literature in the intersection of psychology and philosophy of science by systematically investigating how judgments of explanatory power are affected by the prior credibility of a potential explanation, the causal framing used to describe the explanation, the generalizability of the explanation, and its statistical relevance for the evidence. Collectively, (...) the results of our five experiments support the hypothesis that the prior credibility of a causal explanation plays a central role in explanatory reasoning: first, because of the presence of strong main effects on judgments of explanatory power, and second, because of the gate-keeping role it has for other factors. Highly credible explanations were not susceptible to causal framing effects. Instead, highly credible hypotheses were sensitive to the effects of factors which are usually considered relevant from a normative point of view: the generalizability of an explanation, and its statistical relevance for the evidence. These results advance current literature in the philosophy and psychology of explanation in three ways. First, they yield a more nuanced understanding of the determinants of judgments of explanatory power, and the interaction between these factors. Second, they illuminate the close relationship between prior beliefs and explanatory power. Third, they clarify the relationship between abductive and probabilistic reasoning. (shrink)

The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more probable when it was linked to evidence by a causal chain than when both variables shared a common cause; (b) predictive chains in which evidence is a cause of (...) the hypothesis gave rise to higher judgments than diagnostic chains in which evidence is an effect of the hypothesis; and (c) direct chains gave rise to higher judgments than indirect chains. A Bayesian learning model was applied to our data but failed to explain them. An explanation-based hypothesis stating that statistical information will affect judgments only to the extent that it changes beliefs about causal structure is consistent with the results. (shrink)

Most philosophers agree that abduction (in the sense of Inference to the Best Explanation) is a type of inference that is frequently employed, in some form or other, both in everyday and in scientific reasoning. However, the exact form as well as the normative status of abduction are still matters of controversy. This entry contrasts abduction with other types of inference; points at prominent uses of it, both in and outside philosophy; considers various more or less precise statements of it; (...) discusses its normative status; and highlights possible connections between abduction and Bayesian confirmation theory. (shrink)