Oaksford & Chater (O&C) are to be commended for their comprehensive account of deductive reasoning in Bayesian Rationality (Oaksford & Chater Reference Oaksford and Chater2007, henceforth BR). They don't just explain Wason's selection task, or conditional reasoning, or syllogistic reasoning, but all of these. Furthermore, the fact that their account is Bayesian is important for several reasons. First, an inductive account of deductive behavior is interesting and novel. Second, because the pattern of data which they explain consists largely of deductive errors, they are providing an alternative rational view of these “errors.” Third, because their account is both rational and explains behavior in these tasks, there is an important sense in which readers (such as myself) gain an understanding of why people respond as they do in these tasks. The explanation is, essentially, a teleological one. Finally, the approach connects deductive reasoning to other areas of psychological research that are otherwise disconnected, such as vision, categorization, and language, all of which are influenced by Bayesian approaches. In short, O&C offer an account that is parsimonious both locally, in the sense that it is a rational framework that can explain a wide range of deductive behavior, and globally, in that it ties in with a much broader collection of diverse research topics.

Especially important is the environmental aspect of the “rational analysis” that they pursue (Anderson Reference Anderson1990). Researchers tend to point to the many examples of purportedly irrational behavior as evidence against a rational approach (e.g., Gilovich et al. Reference Gilovich, Griffin and Kahneman2002; Kahneman et al. Reference Kahneman, Slovic and Tversky1982; Kahneman & Tversky Reference Kahneman and Tversky2000). Often overlooked by researchers, though, is that people make strong and reasonable assumptions about the structure of their environment. Unaware of these assumptions, researchers can draw misleading conclusions about behavior. For example, O&C point out that people appear to make the “rarity assumption,” that is, that named categories tend to be small (see also McKenzie & Amin Reference McKenzie and Amin2002; McKenzie & Mikkelsen Reference McKenzie and Mikkelsen2000; Reference McKenzie and Mikkelsen2007; McKenzie et al. Reference McKenzie, Ferreira, Mikkelsen, McDermott and Skrable2001). Without the rarity assumption, some behavior can appear irrational and even nonsensical, but with it, behavior is not only reasonable, it is consistent with a Bayesian approach.

Similarly, McKenzie and Mikkelsen (Reference McKenzie and Mikkelsen2007) recently argued that the rarity assumption explains why people, when assessing the correlation between two variables that can be either present or absent, are especially influenced by observing the joint presence of the variables. Traditionally, the large influence of joint presence observations has been seen as an error, but it makes sense from a Bayesian perspective combined with the rarity assumption. A completely different example of the importance of environmental structure comes from framing effects, which refer to situations in which listeners respond differently to logically equivalent utterances (or frames). Framing effects have been considered irrational, but it has been pointed out that speakers do not choose randomly among frames. Instead, speakers choose frames systematically, and listeners know this. Thus, although many frames are logically equivalent, they are not information equivalent, and responding differently to them is not irrational (McKenzie Reference McKenzie2004; McKenzie & Nelson Reference McKenzie and Nelson2003; Sher & McKenzie Reference Sher and McKenzie2006; Reference Sher, McKenzie, Chater and Oaksford2008). Understanding the structure of the environment, and what makes sense in light of this structure, goes a long way toward understanding people's behavior.

The power of rational analysis is apparent at the computational level (using Marr's [Reference Marr1982] terminology), where the aim is to understand what problem the organism is trying to solve. O&C's accounts of Wason's selection task and conditional reasoning are essentially computational. But when O&C discuss syllogistic reasoning and their probability heuristics model, they also show what rational analysis can do at the algorithmic level, which is concerned with the processes used by the organism to solve the problems it faces. The rational model remains the same, but it becomes clear that behavior, at this level, diverges from the model. Relative to the rational model, people use simplified processes (i.e., heuristics) that lead to systematic errors (biases; e.g., Kahneman et al. Reference Kahneman, Slovic and Tversky1982). One must be careful here because, as others have pointed out, the “heuristics and biases” paradigm in judgment and decision making has left that field with a “laundry list” of heuristics and biases rather than an overarching theory (Gigerenzer Reference Gigerenzer1996; Krueger & Funder Reference Krueger and Funder2004). However, rational analysis severely constrains the set of potential heuristics that people might use, which, to a large extent, would guard against the explosion of heuristics that has plagued the judgment and decision making literature. Furthermore, because this constrained set of plausible heuristics would come from a rational analysis, the adaptiveness of heuristics would be less likely to be a contentious topic, as it often is in the field of judgment and decision making. It would be fascinating (and a little ironic) if rational analysis were to pave the way for the next step in the heuristics and biases paradigm.