We propose probability logic as an appropriate standard of reference for evaluating human inferences. Probability logical accounts of nonmonotonic reasoning with system p, and conditional syllogisms (modus ponens, etc.) are explored. Furthermore, we present categorical syllogisms with intermediate quantifiers, like the “most . . . ” quantifier. While most of the paper is theoretical and intended to stimulate psychological studies, we summarize our empirical studies on human nonmonotonic reasoning.

We take coherence based probability logic as the basic reference theory to model human deductive reasoning. The conditional and probabilistic argument forms are explored. We give a brief overview of recent developments of combining logic and probability in psychology. A study on conditional inferences illustrates our approach. First steps towards a process model of conditional inferences conclude the paper.

Originally published in 1982, this was an extensive and up-to-date review of research into the psychology of deductive reasoning, Jonathan Evans presents an alternative theoretical framework to the rationalist approach which had dominated much of the published work in this field at the time. The review falls into three sections. The first is concerned with elementary reasoning tasks, in which response latency is the prime measure of interest. The second and third sections are concerned with syllogistic and propositional reasoning respectively, (...) in which interest has focused on the explanation of frequently observed logical errors. In an extended discussion it is argued that reasoning processes are content specific, and give little indication of the operation of any underlying system of logical competence. Finally, a dual process theory of reasoning, with broad implications and connections with other fields of psychology, is elaborated and assessed in the light of recent evidence. (shrink)

Probabilistic models have started to replace classical logic as the standard reference paradigm in human deductive reasoning. Mental probability logic emphasizes general principles where human reasoning deviates from classical logic, but agrees with a probabilistic approach (like nonmonotonicity or the conditional event interpretation of conditionals). -/- This contribution consists of two parts. In the first part we discuss general features of reasoning systems including consequence relations, how uncertainty may enter argument forms, probability intervals, and probabilistic informativeness. These concepts are of (...) central importance for the psychological task analysis. In the second part we report new experimental data on the paradoxes of the material conditional, the probabilistic modus ponens, the complement task, and data on the probabilistic truth table task. The results of the experiments provide evidence for the hypothesis that people represent indicative conditionals by conditional probability assertions. (shrink)

The present chapter describes a probabilistic framework of human reasoning. It is based on probability logic. While there are several approaches to probability logic, we adopt the coherence based approach.