Replacing logical coherence by effectiveness as criteria of rationality, Gigerenzer et al. show that simple heuristics can outperform comprehensive procedures (e.g., regression analysis) that overload human limited information processing capacity. Although their work casts long overdue doubt on the normative status of the Rational Choice Paradigm, their methodology leaves open its relevance as to how decisions are actually made.

A research program is announced, and initial, exciting progress described. Many inference problems, poorly modeled by some traditional approaches, are surprisingly well handled by kinds of simple-minded Bayesian approximations. Fuller Bayesian approaches are typically more accurate but rarely are they either fast or frugal. Open issues include codifying when to use which heuristic and to give detailed evolutionary explanations.

The simple heuristics that may indeed usually make us smart–or at least smart enough–in contexts of individual choice will sometimes make us dumb, especially in contexts of social choice. Here each individual choice (or vote) has little impact on the overall choice, although the overall choice is compounded out of the individual choices. I use an example (risk aversion) to illustrate the point.

I investigate whether heuristics similar to those studied by Gigerenzer and his co-authors can apply to the problem of finding a suitable heuristic for a given problem. I argue that not only can heuristics of a very similar kind apply but they have the added advantage that they need not incorporate specific trade-off parameters for balancing the different desiderata of a good decision-procedure.

The notion of ecological rationality, although plausible, does not readily lead to testable predictions. This is illustrated with respect to heuristics in syllogistic reasoning. Several possible heuristics have been proposed but ecological rationality does not appear to offer a sensible rationale for choosing between these.

This commentary questions the claim that Take-The-Best provides a cognitively more plausible account of cue utilisation in decision making because it is faster and more frugal than alternative algorithms. It is also argued that the experimental evidence for Take-The-Best, or non-integrative algorithms, is weak and appears consistent with people normally adopting an integrative approach to cue utilisation.

Although we welcome Gigerenzer, Todd, and the ABC Research Group's shift of emphasis from “coherence” to “correspondence” criteria, their rejection of optimality in human decision making is premature: In many situations, experts can achieve near-optimal performance. Moreover, this competence does not require implausible computing power. The models Gigerenzer et al. evaluate fail to account for many of the most robust properties of human decision making, including examples of optimality.

Simple heuristics that make us smart offers an impressive compilation of work that demonstrates fast and frugal (one-reason) heuristics can be simple, adaptive, and accurate. However, many decision environments differ from those explored in the book. We conducted a Monte Carlo simulation that shows one-reason strategies are accurate in “friendly” environments, but less accurate in “unfriendly” environments characterized by negative cue intercorrelations, that is, tradeoffs.

There are parallels between Gigerenzer et al.'s emphasis on the rationality of adults' reasoning in terms of simple heuristics and developmental researchers' emphasis on the rationality of children's reasoning in terms of intuitive theories. Indeed, just as children become better at using their theories, so might some people, experts, become better at using simple heuristics.

The simple heuristics described in this book are ingenious but are unlikely to be optimally helpful in real-world, consequential, high-stakes decision making, such as mate and job selection. I discuss why the heuristics may not always provide people with such decisions to make with as much enlightenment as they would wish.

To evaluate the success of simple heuristics we need to know more about how a relevant heuristic is chosen and how we learn which cues are relevant. These meta-abilities are at the core of ecological rationality, rather than the individual heuristics.

The smartness of simple heuristics depends upon their fit to the structure of task environments. Being fast and frugal becomes psychologically demanding when a decision goal is bounded by the risk distribution in a task environment. The lack of clear goals and prioritized cues in a decision problem may lead to the use of simple but irrational heuristics. Future research should focus more on how people use and integrate simple heuristics in the face of goal conflict under risk.

Gigerenzer et al.'s is an extremely important book. The ecological validity of the key heuristics is strengthened by their relation to ubiquitous Poisson processes. The recognition heuristic is also used in conspecific cueing processes in ecology. Three additional classes of problem-solving heuristics are proposed for further study: families based on near-decomposability analysis, exaptive construction of functional structures, and robustness.

Quartz & Sejnowski's (Q&S's) model for constructive learning agrees with the basic assumptions of mainstream cognitive developmental theories. However, it does not detail the neural equivalents of (1) the process of cognitive change per se, (2) the construction and functioning of thought modules, and (3) the involvement of “mindreading” and “mindsteering” in constructive learning. Specifying these equivalents is necessary if cognitive developmental neuroscience and mainstream cognitive development are to be directly connected.

There is a wealth of data on the perceptual and cognitive capacities of infants strongly supporting early nativistic influences on development. Without considering these initial determinants, constructivist models of development are at best incomplete.

Three arguments are given to show that neural constructivism lacks an essential ingredient to explain cognitive development. Based on results in the theory of adaptive signal analysis, adaptive biological pattern information and self-organization in nonlinear systems of information processing, it is concluded that neural constructivism should be further extended to accommodate the occurrence of phase transitions generating qualitative development in the sense of Piaget.

We suggest that innatism and constructivism may differ only in their time scale.

It is becoming increasingly clear that acquiring cognitive skills is feasible only with significant developmental constraints. However, recent research provides the strongest evidence to date for constructivist development. Here, we examine how these two apparently conflicting perspectives may be reconciled. Specifically, we suggest that subcortical and cortical structures possess divergent developmental strategies, with many subcortical structures satisfying Fodor's criteria for modularity. These structures constitute an early behavioral system that guides the construction of later emerging cortical structures, for which there is little evidence for modularity. Thus, we focus on how the dynamic time course of development itself implicitly constrains the emergence of cortical representations, reducing the requirement for built-in encodings of knowledge in cortical circuits, as on the traditional nativist conception.