This study discusses belief-change as the problem of deciding which previously-accepted belief, or premise, to abandon, when an inference from an initial belief set is subsequently contradicted. The data concern how "disbelieving" a previously-accepted conditional premise is realized as a particular modification to that premise. The types of revisions that are made are influenced by the kind of knowledge expressed in the conditional. The results and the broader issues of belief-revision are related to other concerns that have emerged in the literature on propositional inference, such as the reported reluctance of people to make simple valid modus ponens inferences in some circumstances and the general interest in incorporating subjective belief into accounts of deductive inference.

Simple belief-revision tasks were defined by a giving subjects a conditional premise, (p—>q), a categorical premise, (p, for a modus-ponens belief-set, or ~q, for a modus tollens belief-set), and the associated inference (q or ~p, respectively). "New" information contradicted the initial inference (~q or p, respectively). Subjects indicated their degree of belief in the conditional premise and the categorical premise, given the contradiction. Results indicated that the choice was a function of the knowledge type expressed in the conditional form; when that knowledge type was causal, the choice was affected by the number of disabling factors associated with the causal relationship. A "possible worlds" interpretation of the data is related to formal notions such as epistemic entrenchment, used in normative models of belief revision, and to reasoning from uncertain premises, from the human deduction literature.

Default reasoning occurs when the available information does not deductively guarantee the truth of the conclusion; and the conclusion is nonetheless correctly arrived at. The formalisms that have been developed in Artificial Intelligence to capture this mode of reasoning have suffered from a lack of agreement as to which non-monotonic inferences should be considered correct; and so Lifschitz 1989 produced a set of "Nonmonotonic Benchmark Problems" which all future formalisms are supposed to honor. The present work investigates the extent to which humans follow the prescriptions set out in these Benchmark Problems.

In this paper we report preliminary results on how people revise or update a previously held set of beliefs. When intelligent agents learn new things which conflict with their current belief set, they must revise their belief set. When the new information does not conflict, they merely must update their belief set. Various AI theories have been proposed to achieve these processes. There are two general dimensions along which these theories differ: whether they are syntactic-based or model-based, and what constitutes a minimal change of beliefs. This study investigates how people update and revise semantically equivalent but syntactically distinct belief sets, both in symbolic-logic problems and in quasi-real-world problems. Results indicate that syntactic form affects belief revision choices. In addition, for the symbolic problems, subjects update and revise semantically-equivalent belief sets identically, whereas for the quasi-real-world problems they both update and revise differently. Further, contrary to earlier studies, subjects are sometimes reluctant to accept that a sentence changes from false to true, but they are willing to accept that it would change from true to false.

We report empirical results on factors that influence how people reason with default rules of the form "Most x's have property P", in scenarios that specify information about exceptions to these rules and in scenarios that specify default-rule inheritance. These factors include (a) whether the individual, to which the default rule might apply, is similar to a known exception, when that similarity may explain why the exception did not follow the default, and (b) whether the problem involves classes of naturally occurring kinds or classes of artifacts. We consider how these findings might be integrated into formal approaches to default reasoning and also consider the relation of this sort of qualitative default reasoning to statistical reasoning.