Barbey & Sloman's (B&S's) analysis of previous studies of the effect of base rate information demonstrates that in many cases the effect increases when the set structure of the problem is made more transparent. As a result, the participants can perform more complete analysis of the data. For example, the reliance on base rates is enhanced by the following manipulations: Partitioning the data into exhaustive subsets, using diagrammatic representation of all relevant sets, and formulating the question in a way that encourages participants to compute the two terms of the Bayesian ratio first, instead of direct computation of the probability.

The main goal of our commentary is to highlight an interesting set of conditions that lead to the opposite pattern. Under these conditions the presentation of information concerning the set structure reduces the effect of the relevant base rates. For an example, consider the task of reading the text in Figure 1.

Figure 1. The text used in Experiment 1 of Erev et al. (Reference Erev, Shimonowitch, Schurr, Hertwig, Plessner, Betsch and Betsch2007).

This task was studied in Erev et al. (Reference Erev, Shimonowitch, Schurr, Hertwig, Plessner, Betsch and Betsch2007) following Bruner and Minturn (Reference Bruner and Minturn1955; cf. Kahneman Reference Kahneman2003). Their control condition suggests that the participants are not likely to consider the possibility the central stimulus is a number. About 90% of the participants read the central stimulus as the letter “B” (the remaining 10% read it as the number “13”). Thus, the vast majority behaved as if they overweighted the base rate – that is, the fact that a stimulus that appears between letters is more likely to be a letter than a number. Erev et al. show that manipulations that make the set structure of the problem more transparent (e.g., the presentation of the possible hypotheses) decrease such base-rate effects. In the example given here, the presentation of the possible hypotheses (“B” or “13”) increases the proportion of the low base rate responses (“13”) from 10% to 50%.

We believe that the difference between the present example and the situations examined by B&S reflects the difference between decisions from description, and decisions from experience (see Hertwig et al. Reference Hertwig, Barron, Weber and Erev2004). The main tasks analyzed by B&S involve decisions (or judgment) from description. The decision makers were presented with a description of the task that includes the key factors. Hertwig et al. (Reference Hertwig, Barron, Weber and Erev2004; see also Erev et al. Reference Erev, Shimonowitch, Schurr, Hertwig, Plessner, Betsch and Betsch2007) show that in decisions from description people deviate from optimal choice in the direction of giving equal weight to all the possibilities. That is, the low base rate categories receive “too much” attention and the objective base rate is neglected.

The example in Figure 1, in contrast, involves decisions from experience. The participants did not receive a description of the possible categories and/or their base rates. Recent research (see review in Erev & Barron Reference Erev and Barron2005) shows a bias toward underweighting of rare events (low base-rate categories) in decisions from experience. People behave “as if” they forget to consider the low base-rate category. That is, in this case forgetting and similar cognitive limitations imply a very strong base-rate effect.

In summary, we propose that it is constructive to distinguish between two ways in which base rates affect human behavior. The first effect is likely to emerge in decisions from description as a product of careful analysis. B&S focus on this effect and note that it can be described as an outcome of the rule-based reasoning. The second effect is likely to emerge in decisions from experience as a product of forgetting and/or neglect of the low base-rate categories. We assert that this effect is rather common, and is likely to be decreased by careful analysis.