In the target article, Barbey & Sloman (B&S) do an admirable job of demonstrating that a dual process model of judgment provides a better account of base-rate neglect than the various alternative accounts. We were struck, however, by a curious dissociation in their article that is representative of research on base-rate neglect in general. The examples provided to illustrate how research on base-rate neglect may be important to “real world” decisions typically involve intrinsically emotional contexts such as cancer diagnosis, pandemic infections, or judgments about the guilt of a defendant. Nevertheless, the target article continues the tradition of neglect of affective factors in reasoning. This neglect is odd considering the recent resurgence of interest in affect in cognitive neuroscience and the increasing evidence that both hot and cold cognition are involved in decision making (e.g., Lee Reference Lee2006; Sanfey et al. Reference Sanfey, Loewenstein, McClure and Cohen2006). In fact, one of the most important advantages of the dual-process model of reasoning may be that it provides a coherent framework for understanding sources of affective influences on reasoning. Before we turn to why the dual process model is a useful framework for understanding hot and cold cognition during reasoning, we first briefly review some of the evidence that suggests that affective influences should be integrated into research on reasoning.

As one illustration of the central role of affect in decision making and reasoning, consider the risk-as-feeling hypothesis (Loewenstein Reference Loewenstein2005; Loewenstein et al. Reference Loewenstein, Weber, Hsee and Welch2001). Loewenstein and colleagues argue that, when in conflict, hot cognitive factors will supersede cold ones in decision making, and that the precedence of hot factors helps to explain some violations of normative decision making in traditional theory. For example, the certainty effect (e.g., Kahneman & Tversky Reference Kahneman and Frederick1979) is a commonly observed nonlinearity in the way probabilities are weighted in decision outcomes. Although the difference in a very high probability event and certainty may seem trivial from a cold cognitive perspective, real emotion may be either absent or present in these two cases. A medical patient who is told that the risk of a specific form of cancer is zero, acts and feels very differently from one who is told there is a non-zero probability of the disease. Once the prospect of the disease becomes real, the associated worry and dread become vivid.

In a related area of research, Slovic and colleagues have emphasized the role of an affect heuristic in judgment and decision making (e.g., Finucane et al. Reference Finucane, Alhakami, Slovic and Johnson2000; Slovic et al. Reference Slovic, Finucane, Peters and MacGregor2004). According to this view, affect is central to reasoning because the information used during decision making often carries affective tags. These affective tags can contribute to quicker and more efficient decision making, but they pose a risk of constraining and biasing decisions, as well. Slovic and colleagues have shown how the affect heuristic is consistent with a wide range of data on reasoning under uncertainty. For example, the affect heuristic provides an explanation for why some medical risks may be overestimated. For instance, the mere knowledge that a chemical could be carcinogenic will lead non-experts to overestimate cancer risks from low-level exposure (Slovic et al. Reference Slovic, Peters, Finucane and MacGregor2005). Because of the emotional impact of cancer, the absence of detailed knowledge of actual risks is ignored. The emotional associations of a disease can have other, unintended consequences. A cancer-screening procedure that reduces risk may, ironically, lead to heightened perceptions of the likelihood of contracting the disease as dread and worry are made more emotionally salient.

A key point made by these and other researchers who have examined affective influences on reasoning is that affect is not a secondary consideration to take up once we have a good “cold” model of reasoning. There are two main reasons to consider affect as fundamental to the study of reasoning. First, affective influences are pervasive. Even in the case of formal logical reasoning with syllogisms, both incidental affective reactions during reasoning and manipulation of affective dimensions of the stimulus material have powerful effects (e.g., Blanchette Reference Blanchette2006). Second, affective influences are not simply distractions that perturb the functioning of a cold rational system. Affect is part of the very nature of the reasoning process (e.g., Damasio Reference Damasio1994; Loewenstein Reference Loewenstein2005). Given that research on hot and cold cognition in reasoning has stressed the integration of multiple kinds of information in explaining choice behavior (e.g., Hinson et al. Reference Hinson, Whitney, Holben and Wirick2006), modular views of reasoning are theoretic non-starters. In contrast, the dual process framework favored by B&S to explain base-rate neglect can be integrated readily with the data on affective influences on reasoning.

The most obvious way to integrate affective influences into the dual process model is to assign affective influences to the primitive associative judgment system (cf. Epstein Reference Epstein1994). This idea embodies the classic notion that the primary influence of affect in judgment is to cloud our otherwise efficient deliberative cognitive system. However, this view, at the very least, greatly oversimplifies the interaction of hot and cold factors in reasoning. For example, one role for affective processing may be to push the reasoning system either toward or away from deliberative processing. In a positive affective context, the simpler associative processing mode of judgment is more likely to be used, whereas negative context can induce more deliberative processing (see Fiedler Reference Fiedler and Forgas2001). In addition, there is mounting evidence that there are specific affective influences within the deliberative system itself. Particularly relevant to the target article are data on why probability and frequency representation of events often lead to different decision making outcomes (Peters & Slovic Reference Peters and Slovic2000). For example, if people are asked to provide a recommendation about release of prisoners, they are far more likely to recommend release if told there is a 10% chance of reoffense, rather than being told than 10 out of 100 will reoffend. This difference in judgment in two identical situations results from the ease with which the frequency representation is associated with the affective consequences of actual people committing real crimes. This example is one of many that suggest that affect neglect is a suboptimal research heuristic.