Conceptual spaces and the strength of similarity-based arguments.

Central to the conceptual spaces framework is the thought that concepts can be studied mathematically, by geometrical and topological means. Various applications of the framework have already been subjected to empirical testing, mostly with excellent results, demonstrating the framework's usefulness. So far untested is the suggestion that conceptual spaces may help explain certain inferences people are willing to make. The experiment reported in this paper focused on similarity-based arguments, testing the hypothesis that the strength of such arguments can be predicted from the structure of the conceptual space in which the items being reasoned about are represented. A secondary aim of the experiment concerned a recent inferentialist semantics for indicative conditionals, according to which the truth of a conditional requires the presence of a sufficiently strong inferential connection between its antecedent and consequent. To the extent that the strength of similarity-based inferences can be predicted from the geometry and topology of the relevant conceptual space, such spaces should help predict truth ratings of conditionals embodying a similarity-based inferential link. The results supported both hypotheses.

Abductive conditionals as a test case for inferentialism.

According to inferentialism, for an indicative conditional to be true, there must be a sufficiently strong inferential connection between its antecedent and its consequent. Previous experimental research has found support for inferentialism, but the materials used concerned a fairly abstract context, leaving open the question of how accurately the account can predict semantic judgments about more realistic materials. To address this question, we conducted three experiments using abductive conditionals, which are conditionals featuring an explanatory-inferential connection between their antecedent and consequent (typically, the event cited in the consequent is, or purports to be, the best explanation of the event cited in the antecedent). Two experiments try to predict truth ratings for such conditionals on the basis of judgments of explanatory goodness. Inferentialism predicts about our materials that participants will tend to agree more with a conditional, the better the consequent explains the antecedent and so the stronger the inferential connection between antecedent and consequent is. The first two experiments allow us to contrast inferentialism with a version of the mental models account that aims to explain truth ratings in terms of salient alternatives and disablers. A third experiment looks at abductive conditionals in the context of modus ponens arguments. Inferentialism predicts that endorsement rates for such arguments co-depend on the strength of the inferential connection between the component parts of the major premise and so, again given our materials, on how well that premise's consequent explains its antecedent. The experiment aims to determine whether there is any support for this prediction, and it also contrasts inferentialism with the suppositional account of conditionals as well as with accounts that postulate a more complex probabilistic connection between a conditional's antecedent and consequent. To preview our results, we find strong support for inferentialism across the three experiments.

Best, second-best, and good-enough explanations: How they matter to reasoning.

There is a wealth of evidence that people's reasoning is influenced by explanatory considerations. Little is known, however, about the exact form this influence takes, for instance about whether the influence is unsystematic or because of people's following some rule. Three experiments investigate the descriptive adequacy of a precise proposal to be found in the philosophical literature, to wit, that we should infer to the best explanation, provided certain additional conditions are met. The first experiment studies the relation between the quality of an explanation and people's willingness to infer that explanation when only one candidate explanation is given. The second experiment presents participants always with two explanations and investigates the effect of the presence of an alternative on the participants' willingness to infer the target explanation. Although Experiments 1 and 2 manipulate explanation quality and willingness to infer to the best explanation between participants, Experiment 3 manipulates those measures within participants, thereby allowing to study the influence of explanatory considerations on inference at the individual level. The third experiment also studies the connection between explanation quality, willingness to infer, and metacognitive confidence in the decision to infer. The main conclusions that can be drawn from these experiments are that (a) the quality of an explanation is a good predictor of people's willingness to accept that explanation, and a better predictor than the prior probability of the explanation, and (b) if more than one possible explanation is given, people are the less willing to infer the best explanation the better they deem the second-best explanation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).