Both the theoretical frameworks that organize the first part of Barbey & Sloman's (B&S's) target article and the empirical evidence marshaled in the second part are marked by distinctions that should not exist (i.e., false dichotomies), conflations where distinctions should be made, and selective omissions of empirical results – within the very studies discussed – that create illusions of theoretical and empirical favor.

Causal and counterfactual thoughts are bound together in Byrne's theory of human imagination. We think there are two issues in her theory that deserve clarification. First, Byrne describes which counterfactual possibilities we think of, but she leaves unexplained the mechanisms by which we generate these possibilities. Second, her exploration of “strong causes” and enablers gives two different predictions of which counterfactuals we think of in causal scenarios. On one account, we think of the counterfactuals which we have control over. On the other, which counterfactuals we think of depends on whether something is a strong cause or an enabler. Although these two accounts sometimes give the same predictions, we present cases in which they differ, and we would like to see Byrne's theory provide a way of reconciling these differences.

Jablonka & Lamb (J&L) have presented a number of different possible mechanisms for finessing design. The extra-genetic nature of these mechanisms has led them to challenge orthodox neo-Darwinian views. However, these mechanisms are for calibration and have been designed by natural selection. As such, they add detail to our knowledge, but neo-Darwinism is sufficiently resourced to account for them.

This commentary compares the P-FIT model with psychometric and developmental models of intelligence and shows that there are isomorphisms and divergences between them. All three models involve some common dimensions, but the P-FIT model lacks many of the dimensions of the other models. Then we point to research that can lead to the integration of brain models with cognitive-developmental models.

We support the ambitious goal of unification within the behavioral sciences. We suggest that Darwinian evolution by means of natural selection can provide the integrative glue for this purpose, and we review our own work on selective investment theory (SIT), which is an example of how other-regarding preferences can be accommodated by a gene-centered account of evolution.

Evolution in Four Dimensions claims that epigenetic, behavioral, and symbolic inheritance systems should be considered equal partners to genetics in evolutionary biology. The evidence for, and applicable scope of, these additional inheritance systems is limited, particularly with regard to areas involving learning. It is unclear how including these extra dimensions in mainstream evolutionary thinking translates into testable hypotheses for a productive research program.

Research in mathematical cognition has shown that rates, and other interpretations of x/y, are hard to learn and understand. On the other hand, there is extensive evidence that the brain is endowed with a specialized mechanism for representing and manipulating the numerosities of sets – that is, frequencies. Hence, base-rates are neglected precisely because they are rates, whereas frequencies are indeed natural.

Gintis's article is an example of growing awareness by social scientists of the significance of evolutionary theory for understanding human nature. Although we share its main point of view, we comment on some disagreements related to levels of behavioral analysis, the explanation of social cooperation, and the ubiquity of inter-individual differences in human decision-making.

The P-FIT model is a major step forward in understanding biological causes of intelligence. It is consistent with evidence on the influence of working memory and speediness upon intelligence, and with models that emphasize the role of interaction between modules to produce intelligence. The contribution to understanding genetic contributions is problematical, due to the difficulty of isolating the genes involved.

Ruth Byrne presents a three-step argument to the conclusion that counterfactual imagining is rational. Insofar as this argument is valid, the conclusion is weaker than it seems. More importantly, it does not represent the central contributions of this book – contributions that, if anything, point instead to what is irrational about counterfactual imagining.

Counterfactual thinking may be described as disciplined, realistic, and rational, but we move a step further to describe a theoretical perspective centering on behavior regulation. According to this perspective, counterfactual thinking primarily centers on coordination of ongoing behavior. In short, most “if only” thoughts in daily life center on the acquisition of goals; hence, counterfactual thinking may be illuminated by considering the large literature on goal cognition.

Barbey & Sloman (B&S) claim that frequency formats and other task manipulations induce people to substitute associative thinking for rule-based thinking about nested sets. My critique focuses on the substitution assumption. B&S demonstrate that nested sets are important to solve base-rate problems but they do not show that thinking about these nested sets relies on a different type of reasoning.

Although the first three dimensions of evolution outlined by Jablonka & Lamb (J&L) are persuasively presented as aspects of evolutionary science, the fourth dimension, symbolic evolution, is problematic: Though it may in some metaphorical sense be happening, there cannot be a science of symbolic evolution. Symbolic evolution essentially involves meaning, which, besides being nonphysical, resolutely resists scientific categorization.

The contemporary behavioral sciences are disunified and could not easily become unified, as they operate with incompatible explanatory models. According to Gintis, tolerance of this situation is “scandalous” (sect. 12). I defend the ordinary behavioral scientist's lack of commitment to a unifying explanatory model and identify several reasons why the behavioral sciences should remain disunified for the foreseeable future.

A large number of experimental results clearly indicate that sleep has an important role for human intelligence. Sleep-wake stages and their specific patterns of brain activation and neuromodulation subserve human memory, states of consciousness, and modes of information processing that strongly relate to intelligence. Therefore, human intelligence should be explained in a broader framework than is implicated by neuroimaging data alone.

We agree that current evolutionary accounts of base-rate neglect are unparsimonious, but we dispute the authors' account of the effect in terms of parallel associative and rule-based processes. We also question their assumption that cueing of nested set relations facilitates performance due to recruitment of explicit reasoning processes. In our account, such reasoning is always involved, but usually unsuccessful.

The argument that heritable epigenetic change plays a distinct role in evolution would be strengthened through recognition that it is what bootstrapped the origin and early evolution of life, and that, like behavioral and symbolic change, it is non-Darwinian. The mathematics of natural selection, a population-level process, is limited to replication with negligible individual-level change that uses a self-assembly code.

Byrne's approach to the semifactual conditional captures the reasoning data. However, we argue that it does not account for the processes or principles by which people arrive at representations of even-if conditionals, upon which their reasoning is said to be based. Drawing upon recent work on the suppositional conditional we present such an account.

General intelligence is largely based on two distinguishable mental abilities: crystallized intelligence (gC) and fluid reasoning ability (gF). The target article authors' P-FIT model emphasizes a network of regions throughout the brain as the neural basis for fluid reasoning and/or working memory. However, it provides little significant insight into the neural basis of gC, or how or why gC is more stable than gF across the life span.

Even if game theory is broadened to encompass other-regarding preferences, it cannot adequately model all aspects of interactive decision making. Payoff dominance is an example of a phenomenon that can be adequately modeled only by departing radically from standard assumptions of decision theory and game theory – either the unit of agency or the nature of rationality.