Online research in philosophy

Four experiments are reported which investigated the types of truth tables that people associate with conditional sentences and the kinds of inferences that they will draw from them. The present studies differed from most previous ones in using different types of content in the conditionals, for example promises and warnings. It was found that the type of content had a strong and consistent effect on both truth tables and inferences. It is suggested that this is because in real life conditionals make probabilistic assertions, and that the strength of the probabilistic link is determined by the situation in which the conditional occurs. The implications of these findings for current theories of reasoning are considered and it is concluded that none of them is entirely satisfactory. It is suggested that more linguistically based theories may prove more successful.

Why do people get sick? I argue that a disease explanation is best thought of as causal network instantiation, where a causal network describes the interrelations among multiple factors, and instantiation consists of observational or hypothetical assignment of factors to the patient whose disease is being explained. This paper first discusses inference from correlation to causation, integrating recent psychological discussions of causal reasoning with epidemiological approaches to understanding disease causation, particularly concerning ulcers and lung cancer. It then shows how causal mechanisms represented by causal networks can contribute to reasoning involving correlation and causation. The understanding of causation and causal mechanisms provides the basis for a presentation of the causal network instantiation model of medical explanation.

Hume, in "An Enquiry Concerning Human Understanding", holds (1) that all causal reasoning is based on experience and (2) that causal reasoning is based on nothing but experience. (1) does not imply (2), and Hume's good reasons for (1) are not good reasons for (2). This essay accepts (1) and argues against (2). A priori reasoning plays a role in causal inference. Familiar examples from Hume and from classroom examples of sudden disappearances and radical changes do not show otherwise. A priori causal reasoning is closely related to understanding causal mechanisms. One uncovers the intelligibility of a causal process by understanding its mechanism.

Two experiments examined the contribution of working memory (WM) to the retrieval and inhibition of background knowledge about counterexamples (alternatives and disablers, Cummins, 1995) during conditional reasoning. Experiment 1 presented a conditional reasoning task with everyday, causal conditionals to a group of people with high and low WM spans. High spans rejected the logically invalid AC and DA inferences to a greater extent than low spans, whereas low spans accepted the logically valid MP and MT inferences less frequently than high spans. In Experiment 2, an executive-attention-demanding secondary task was imposed during the reasoning task. Findings corroborate that WM resources are used for retrieval of stored counterexamples and that people with high WM spans will use WM resources to inhibit the counterexample activation when the type of counterexample conflicts with the logical validity of the reasoning problem.

In order to resolve the controversial discussion regarding content effects in deductive reasoning, we propose distinguishing between two inferential sources—an argument's form , and additional relations people associate with the argument's content —and analysing their interplay. Both sources are equally necessary in order to understand the role content plays in deductive reasoning. People make valid deductions from the content relations ( content competence ), but in thematic reasoning tasks, these deductions lead to the intriguing phenomenon known as content effects . Focusing on the interplay of both sources of inferences, the dual source distinction enables a novel class of predictions to be made, namely the correct mastery of the logical connectors ( form competence ) in tasks that require the individual to think about an argument's form in relation to its content. To illustrate the dual source approach and its implications, the selection task paradigm of conditional reasoning with a point of view is used in combination with two content domains: conditional promises with cheating and non-cheating perspectives and technical systems with causal perspectives. Experimental findings corroborate all three phenomena: content competence, content effects, and form competence. The dual source distinction is discussed with regard to current theories of reasoning.

This study examined the hypothesis that a key process in conditional reasoning with concrete premises involves on-line retrieval of information about potential alternate antecedents. Participants were asked to solve reasoning problems with causal conditional premises (If cause P then effect Q). These premises were inserted into short contexts. The availability of potential alternatives was varied from one context to another by adding statements that explicitly invalidated one or more of these alternatives (i.e., other causes that lead to the effect Q). The invalidated alternatives differed in the degree of their semantic association to the consequent term (Q). The results show that the effect of invalidating one or more potential alternatives on the two uncertain logical forms, AC and DA, was largely determined by their relative associative strength. These results strongly support a model for conditional reasoning with causal premises that supposes that a key element in responding to the uncertain logical forms is on-line retrieval of at least one potential alternative antecedent.

Deontic reasoning is thinking about whether actions are forbidden or allowed, obligatory or not obligatory. It is proposed that social norms, imposing constraints on individual actions, constitute the fundamental concept for the system of these four deontic modalities, and that people reason from such norms flexibly according to deontic core principles. Two experiments are presented, one on deontic conditional reasoning, the other on “pure” deontic reasoning. Both experiments demonstrate people's high deontic competence and confirm the proposed representational and inferential principles. Experiment 1 additionally shows small effects of the conditional formulations. These findings support the dual source approach (Beller & Spada, 2003) that distinguishes between domain-specific and domain-general inferences. Implications for other theories of deontic reasoning are discussed.

An experiment was conducted to investigate the relative contributions of syntactic form and content to conditional reasoning. The content domain chosen was that of causation. Conditional statements that described causal relationships (if (cause>, then (effect>) were embedded in simple arguments whose entailments are governed by the rules -oftruth-functional logic (i.e., modus ponens, modus tollens, denying the antecedent, and affirming the consequent). The causal statements differed in terms ofthe number of alternative causes and disabling conditions that characterized the causal relationship. (A disabling condition is an event that prevents an effect from occurring even though a relevant cause is present.) Subjects were required to judge whether or not each argument’s conclusion could be accepted. Judgments were found to vary systematically with the number of alternative causes and disabling conditions. Conclusions of arguments based on conditionals with few alternative causes or disabling conditionswerefoun~d:tobe-rnore accept~ able than cdnclusions based on those with many.

The conditional intervention principle is a formal principle that relates patterns of interventions and outcomes to causal structure. It is a central assumption of the causal Bayes net formalism. Four experiments suggest that preschoolers can use the conditional intervention principle both to learn complex causal structure from patterns of evidence and to predict patterns of evidence from knowledge of causal structure. Other theories of causal learning do not account for these results.

Two experiments were conducted to investigate the roles of covariation and of causality in people's readiness to believe a conditional. The experiments used a probabilistic truth-table task (Oberauer & Wilhelm, 2003) in which people estimated the probability of a conditional given information about the frequency distribution of truth-table cases. For one group of people, belief in the conditional was determined by the conditional probability of the consequent, given the antecedent, whereas for another group it depended on the probability of the conjunction of antecedent and consequent. There was little evidence that covariation, expressed as the probabilistic contrast or as the pCI rule (White, 2003), influences belief in the conditional. The explicit presence of a causal link between antecedent and consequent in a context story had a weak positive effect on belief in a conditional when the frequency distribution of relevant cases was held constant.