The investigation of reasoning involves the study of what is rational as well as the empirical study of human reasoning. We are interested in rational answers to reasoning problems but also in the way human reasoning works. While the first aspect is typically covered by logic or formal epistemology, the second is a matter of empirical psychological research. However, many approaches relate both aspects. This paper discusses these hybrid approaches, their motivation, general criticism that has been raised against them as well as the kind of philosophical commitment behind choosing such kind of framework.

Although there is a great deal of interest in conditional probabilities in Bayesian cognitive science, there is still little understanding of how well human agents can learn them. This paper addresses the issue by theoretical and experimental means. In the theoretical part, we distinguish between cases of vacuous learning, where learned probabilistic information is not new, and belief revision is unwarranted, from cases with truly new information. In the experimental part, we investigate how well participants can distinguish these cases and how well they respect the probabilistic norms, thus adding new insights to the long-standing question of the extent to which the human mind is adapted to probabilistic norms.

Recent years have seen a surge of interest in probabilistic measures of argument strength. Such measures have been used to elucidate longstanding questions of both theoretical and practical interest around fallacies of argumentation, scientific argument, legal argument, or argument and evidence aggregation. The measures typically used have drawn, in one form or other, on conditional probability as a central component for measuring argument quality, strength or confirmation. Work in this area has also consistently highlighted the value of Bayesian Belief Networks for computing argument strength in multi-argument (variable) contexts. However, to date, there has been no consideration of whether the key structural property underlying such networks – the notion of conditional independence- has a bearing on argument quality. In this paper, we consider the potential impact of causal structure on the utility of arguments.