Online research in philosophy

According to the transitive dynamics model, people can construct causal structures by linking together configurations of force. The predictions of the model were tested in two experiments in which participants generated new causal relationships by chaining together two (Experiment 1) or three (Experiment 2) causal relations. The predictions of the transitive dynamics model were compared against those of Goldvarg and Johnson-Laird’s model theory (Goldvarg & Johnson- Laird, 2001). The transitive dynamics model consistently predicted the overall causal relationship drawn by participants for both types of causal chains, and, when compared to the model theory, provided a better fit to the data. The results suggest that certain kinds of causal reasoning may depend on force dynamic—rather than on logical or purely statistical—representations.

Regress arguments seem to have convinced most philosophers that reasoning cannot require beliefs about what follows from what. In this paper I argue that this is a mistake. Regress arguments rest on dubious (although deeply entrenched) assumptions about the nature of reasoning — most prominently, the assumption that believing p by reasoning is simply a matter of having a belief in p with the right causal ancestry. I propose an alternative account, according to which beliefs about what follows from what play a constitutive role in reasoning. As I argue, such beliefs are not exercises of any special-purpose faculty for detecting evidential relations. In the most basic cases, they arise from exercises of our capacity for self-knowledge.

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.

Like all causal theories in philosophy, the causal theory of action is plagued by the problem of deviant causal chains. I have proposed a solution on the basis of the assumption that mental states and events are causally efficacious in virtue of their contents. This solution has been questioned by Torbjörn Tännsjö (2009). First, I will reply to the objection, and then I will discuss Tännsjö’s alternative.

Causal conditional reasoning means reasoning from a conditional statement that refers to causal content. We argue that data from causal conditional reasoning tasks tell us something not only about how people interpret conditionals, but also about how they interpret causal relations. In particular, three basic principles of people's causal understanding emerge from previous studies: the modal principle, the exhaustive principle, and the equivalence principle. Restricted to the four classic conditional inferences—Modus Ponens, Modus Tollens, Denial of the Antecedent, and Affirmation of the Consequent—causal conditional reasoning data are only partially able to support these principles. We present three experiments that use concrete and abstract causal scenarios and combine inference tasks with a new type of task in which people reformulate a given causal situation. The results provide evidence for the proposed representational principles. Implications for theories of the na ve understanding of causality are discussed.

There is now substantial agreement about the representational component of a normative theory of causal reasoning: Causal Bayes Nets. There is less agreement about a normative theory of causal discovery from data, either computationally or cognitively, and almost no work investigating how teaching the Causal Bayes Nets representational apparatus might help individuals faced with a causal learning task. Psychologists working to describe how naïve participants represent and learn causal structure from data have focused primarily on learning from single trials under a variety of conditions. In contrast, one component of the normative theory focuses on learning from a sample drawn from a population under some experimental or observational study regime. Through a virtual Causality Lab that embodies the normative theory of causal reasoning and which allows us to record student behavior, we have begun to systematically explore how best to teach the normative theory. In this paper we explain the overall project and report on pilot studies which suggest that students can quickly be taught to (appear to) be quite rational.

There are two accounts describing causal conditional reasoning: the probabilistic and the mental models account. According to the probabilistic account, the tendency to accept a conclusion is related to the probability by which cause and effect covary. According to the mental models account, the tendency to accept a conclusion relates to the availability of counterexamples. These two accounts are brought together in a dual-process theory: It is argued that the probabilistic reasoning process can be considered as a heuristic process whereas the mental models account can be seen as its analytic counterpart. Experiment 1 showed that the two processes differ on a temporal dimension: The variation in fast responses was best predicted by the variation in likelihood information, while the variation in slow responses was best predicted by variation in counterexample information. Experiments 2 and 3 validate the override principle: The likelihood conclusion can be overwritten when specific counterexamples are retrieved in time. In Experiment 2 both accounts were compared based on their difference in input. In Experiment 3 we used a verbal protocol analysis to validate the dual-process idea at the output level. The data of the three experiments provide converging support for framing the two reasoning accounts in a dual-process theory.

Counterfactual theories of causation of the sort presented in Mackie, 1974, and Lewis, 1973 are a familiar part of the philosophical landscape. Such theories are typically advanced primarily as accounts of the metaphysics of causation. But they also raise empirical psychological issues concerning the processes and representations that underlie human causal reasoning. For example, do human subjects internally represent causal claims in terms of counterfactual judgments and when they engage in causal reasoning, does this involves reasoning about counterfactual claims? This paper explores several such issues from a broadly interventionist perspective.

Background: Causal reasoning as a way to make a diagnosis seems convincing. Modern medicine depends on the search for causes of disease and it seems fair to assert that such knowledge is employed in diagnosis. Causal reasoning as it has been presented neglects to some extent the conception of multifactorial disease causes. Goal: The purpose of this paper is to analyze aspects of causation relevant for discussing causal reasoning in a diagnostic context.

This paper proposes a causal-dispositional account of rule-following as it occurs in reasoning and intentional agency. It defends this view against Kripke’s (1982) objection to dispositional accounts of rule-following, and it proposes a solution to the problem of deviant causal chains. In the first part, I will outline the causal-dispositional approach. In the second part, I will follow Martin and Heil’s (1998) realist response to Kripke’s challenge. I will propose an account that distinguishes between two kinds of rule-conformity and two kinds of rule-following, and I will defend the realist approach against two challenges that have recently been raised by Handfield and Bird (2008). In the third part, I will turn to the problem of deviant causal chains, and I will propose a new solution that is partly based on the realist account of rule-following.