Track D Social Science, Human Rights and Political Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138414/1/jia218442.pd

The probability of conditionals: The psychological evidence

The two main psychological theories of the ordinary conditional were designed to account for inferences made from assumptions, but few premises in everyday life can be simply assumed true. Useful premises usually have a probability that is less than certainty. But what is the probability of the ordinary conditional and how is it determined? We argue that people use a two stage Ramsey test that we specify to make probability judgements about indicative conditionals in natural language, and we describe experiments that support this conclusion. Our account can explain why most people give the conditional probability as the probability of the conditional, but also why some give the conjunctive probability. We discuss how our psychological work is related to the analysis of ordinary indicative conditionals in philosophical logic

The psychology of inferring conditionalsfrom disjunctions: A probabilistic study

There is a new probabilistic paradigm in the psychology of reasoning that is, in part, based on results showing that people judge the probability of the natural language conditional, if A then B, P(if A then B), to be the conditional probability, P(B vertical bar A). We apply this new approach to the study of a very common inference form in ordinary reasoning: inferring the conditional if not-A then B from the disjunction A or B. We show how this inference can be strong, with P(if not-A then B) "close to" P(A or 8), when A or B is non-constructively justified. When A or B is constructively justified, the inference can be very weak. We also define suitable measures of "closeness" and "constructivity", by providing a probabilistic analysis of these notions. (C) 2012 Elsevier Inc. All rights reserved

Contingency, causation, and adaptive inference

In contingency judgment tasks involving 2 event types, individuals weight the a and b cells of a 2 X 2 contingency table more than the c and d cells. Some theorists have argued that they can provide normative justifications For this weighting and that the weighting reflects simple heuristics that are adaptive in the real world. The authors show that, to avoid error, individual judgments about real contingencies should be more subtle than these supposedly adaptive heuristics allow

New normative standards of conditional reasoning and the dual-source model

There has been a major shift in research on human reasoning toward Bayesian and probabilistic approaches, which has been called a new paradigm. The new paradigm sees most everyday and scientific reasoning as taking place in a context of uncertainty, and inference is from uncertain beliefs and not from arbitrary assumptions. In this manuscript we present an empirical test of normative standards in the new paradigm using a novel probabilized conditional reasoning task. Our results indicated that for everyday conditional with at least a weak causal connection between antecedent and consequent only the conditional probability of the consequent given antecedent contributes unique variance to predicting the probability of conditional, but not the probability of the conjunction, nor the probability of the material conditional. Regarding normative accounts of reasoning, we found significant evidence that participants' responses were confidence preserving (i.e., p-valid in the sense of Adams, 1998) for MP inferences, but not for MT inferences. Additionally, only for MP inferences and to a lesser degree for DA inferences did the rate of responses inside the coherence intervals defined by mental probability logic (Pfeifer and Kleiter, 2005, 2010) exceed chance levels. In contrast to the normative accounts, the dual-source model (Klauer et al., 2010) is a descriptive model. It posits that participants integrate their background knowledge (i.e., the type of information primary to the normative approaches) and their subjective probability that a conclusion is seen as warranted based on its logical form. Model fits showed that the dual-source model, which employed participants' responses to a deductive task with abstract contents to estimate the form-based component, provided as good an account of the data as a model that solely used data from the probabilized conditional reasoning task

Suppositions, extensionality, and conditionals: A critique of the mental model theory of Johnson-Laird and Byrne (2002)

P. N. Johnson-Laird and R. M. J. Byrne (see record 2002-18225-002) proposed an influential theory of conditionals in which mental models represent logical possibilities and inferences are drawn from the extensions of possibilities that are used to represent conditionals. In this article, the authors argue that the extensional semantics underlying this theory is equivalent to that of the material, truth-functional conditional, at least for what they term "basic" conditionals, concerning arbitrary problem content. On the basis of both logical argument and psychological evidence, the authors propose that this approach is fundamentally mistaken and that conditionals must be viewed within a suppositional theory based on what philosophical logicians call the Ramsey test. The Johnson-Laird and Byrne theory is critically examined with respect to its account of basic conditionals, nonbasic conditionals, and counterfactuals