Williamson on conditionals and testimony.

In Suppose and Tell, Williamson makes a new case for the material conditional account. He tries to explain away apparently countervailing data by arguing that these have been misinterpreted because researchers have overlooked the role of heuristics in the processing of conditionals. Cases involving the receipt of apparently conflicting conditionals play an important dialectical role in Williamson's book: they are supposed to provide evidence for the material conditional account as well as for the defeasibility of a key procedure underlying our everyday assessments of conditionals. We argue that they can serve neither of these purposes and that Williamson overestimates the reach of heuristics. We specifically challenge Williamson's assumption that, in the kind of cases centrally at issue in his book, the recipient of conflicting conditionals will typically accept those at face value, even granting Williamson that conditionals can be freely passed among speakers under normal conditions of testimony.

Network effects in a bounded confidence model.

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.

The ecological rationality of explanatory reasoning.

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.

Conditionals and inferential connections: A hypothetical inferential theory.

Intuition suggests that for a conditional to be evaluated as true, there must be some kind of connection between its component clauses. In this paper, we formulate and test a new psychological theory to account for this intuition. We combined previous semantic and psychological theorizing to propose that the key to the intuition is a relevance-driven, satisficing-bounded inferential connection between antecedent and consequent. To test our theory, we created a novel experimental paradigm in which participants were presented with a soritical series of objects, notably colored patches (Experiments 1 and 4) and spheres (Experiment 2), or both (Experiment 3), and were asked to evaluate related conditionals embodying non-causal inferential connections (such as "If patch number 5 is blue, then so is patch number 4"). All four experiments displayed a unique response pattern, in which (largely determinate) responses were sensitive to parameters determining inference strength, as well as to consequent position in the series, in a way analogous to belief bias. Experiment 3 showed that this guaranteed relevance can be suppressed, with participants reverting to the defective conditional. Experiment 4 showed that this pattern can be partly explained by a measure of inference strength. This pattern supports our theory's "principle of relevant inference" and "principle of bounded inference," highlighting the dual processing characteristics of the inferential connection.

How group members contribute to group performance: Evidence from agent-based simulations.

The authors argue that group performance depends on the degree to which group members identify with the group as well as on their degree of differentiation. In this commentary, I discuss results from agent-based simulations, suggesting that group performance depends, at least in part, on features orthogonal to agents' caring about group performance or about how they are perceived by other group members.

Similarity-based reasoning in conceptual spaces.

Whereas the validity of deductive inferences can be characterized in terms of their logical form, this is not true for all inferences that appear pre-theoretically valid. Nonetheless, philosophers have argued that at least some of those inferences-sometimes called "similarity-based inferences" -can be given a formal treatment with the help of similarity spaces, which are mathematical spaces purporting to represent human similarity judgments. In these inferences, we conclude that a given property pertains to a category of items on the grounds that the same property pertains to a similar category of items. We look at a specific proposal according to which the strength of such inferences is a function of the distance, as measured in the appropriate similarity space, between the category referenced in the premise and the category referenced in the conclusion. We report the outcomes of three studies that all support the said proposal.

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.

How explanation guides belief change.

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.

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.

Putting prototypes in place.

It has recently been proposed that natural concepts are those represented by the cells of an optimally partitioned similarity space. In this proposal, optimal partitioning has been defined in terms of rational design criteria, criteria that a good engineer would adopt if asked to develop a conceptual system. It has been argued, for instance, that convexity should rank high among such criteria. Other criteria concern the possibility of placing prototypes such that they are both similar to the items they represent-each prototype ought to be representative-and dissimilar to each other: the prototypes ought to be contrastive. Parts of this design proposal are already supported by evidence. This paper reports results of a new study meant to address parts still lacking in empirical support. In particular, it presents data concerning color similarity space which indicate that color prototypes are indeed located such that they trade off optimally between being representative and being contrastive.

A Bayesian perspective on Likert scales and central tendency.

The central tendency bias is a robust finding in data from experiments using Likert scales to elicit responses. The present paper offers a Bayesian perspective on this bias, explaining it as a natural outcome of how participants provide point estimates of probability distributions over the items on a Likert scale. Two studies are reported that support this Bayesian explanation.

Delving Deeper Into Color Space.

So far, color-naming studies have relied on a rather limited set of color stimuli. Most importantly, stimuli have been largely limited to highly saturated colors. Because of this, little is known about how people categorize less saturated colors and, more generally, about the structure of color categories as they extend across all dimensions of color space. This article presents the results from a large Internet-based color-naming study that involved color stimuli ranging across all available chroma levels in Munsell space. These results help answer such questions as how English speakers name a more complex color set, whether English speakers use so-called basic color terms (BCTs) more frequently for more saturated colors, how they use non-BCTs in comparison with BCTs, whether non-BCTs are highly consensual in less saturated parts of the solid, how deep inside color space basic color categories extend, or how they behave on the chroma dimension.

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).

Does optimal partitioning of color space account for universal color categorization?

A 2007 study by Regier, Kay, and Khetarpal purports to show that universal categories emerge as a result of optimal partitioning of color space. Regier, Kay, and Khetarpal only consider color categorizations of up to six categories. However, in most industrialized societies eleven color categories are observed. This paper shows that when applied to the case of eleven categories, Regier, Kay, and Khetarpal's optimality criterion yields unsatisfactory results. Applications of the criterion to the intermediate cases of seven, eight, nine, and ten color categories are also briefly considered and are shown to yield mixed results. We consider a number of possible explanations of the failure of the criterion in the case of eleven categories, and suggest that, as color categorizations get more complex, further criteria come to play a role, alongside Regier, Kay, and Khetarpal's optimality criterion.

Measuring Graded Membership: The Case of Color.

This paper considers Kamp and Partee's account of graded membership within a conceptual spaces framework and puts the account to the test in the domain of colors. Three experiments are reported that are meant to determine, on the one hand, the regions in color space where the typical instances of blue and green are located and, on the other hand, the degrees of blueness/greenness of various shades in the blue-green region as judged by human observers. From the locations of the typical blue and typical green regions in conjunction with Kamp and Partee's account follow degrees of blueness/greenness for the color shades we are interested in. These predicted degrees are compared with the judged degrees, as obtained in the experiments. The results of the comparison support the account of graded membership at issue.

Vagueness, graded membership, and conceptual spaces.

This paper is concerned with a version of Kamp and Partee's account of graded membership that relies on the conceptual spaces framework. Three studies are reported, one to construct a particular shape space, one to detect which shapes representable in that space are typical for certain sorts of objects, and one to elicit degrees of category membership for the various shapes from which the shape space was constructed. Taking Kamp and Partee's proposal as given, the first two studies allowed us to predict the degrees to which people would judge shapes representable in the space to be members of certain categories. These predictions were compared with the degrees that were measured in the third study. The comparison yielded a test of the account of graded membership at issue. The outcome of this test was found to support the conceptual spaces version of Kamp and Partee's account of graded membership.

Probabilistic alternatives to Bayesianism: the case of explanationism.

There has been a probabilistic turn in contemporary cognitive science. Far and away, most of the work in this vein is Bayesian, at least in name. Coinciding with this development, philosophers have increasingly promoted Bayesianism as the best normative account of how humans ought to reason. In this paper, we make a push for exploring the probabilistic terrain outside of Bayesianism. Non-Bayesian, but still probabilistic, theories provide plausible competitors both to descriptive and normative Bayesian accounts. We argue for this general idea via recent work on explanationist models of updating, which are fundamentally probabilistic but assign a substantial, non-Bayesian role to explanatory considerations.

The role of explanatory considerations in updating.

There is an ongoing controversy in philosophy about the connection between explanation and inference. According to Bayesians, explanatory considerations should be given weight in determining which inferences to make, if at all, only insofar as doing so is compatible with Strict Conditionalization. Explanationists, on the other hand, hold that explanatory considerations can be relevant to the question of how much confidence to invest in our hypotheses in ways which violate Strict Conditionalization. The controversy has focused on normative issues. This paper investigates experimentally the descriptive question of whether judgments of the explanatory goodness of hypotheses do play a role when people revise their degrees of belief in those hypotheses upon the receipt of new evidence. We present the results of three experiments that together strongly support the predictive superiority of the explanationist position.

Rationality: a social-epistemology perspective.

Both in philosophy and in psychology, human rationality has traditionally been studied from an "individualistic" perspective. Recently, social epistemologists have drawn attention to the fact that epistemic interactions among agents also give rise to important questions concerning rationality. In previous work, we have used a formal model to assess the risk that a particular type of social-epistemic interactions lead agents with initially consistent belief states into inconsistent belief states. Here, we continue this work by investigating the dynamics to which these interactions may give rise in the population as a whole.