John Bowlby's use of evolutionary theory as a cornerstone of his attachment theory was innovative in its day and remains useful. Del Giudice's target article extends Belsky et al.'s and Chisholm's efforts to integrate attachment theory with more current thinking about evolution, ecology, and neuroscience. His analysis would be strengthened by (1) using computer simulation to clarify and simulate the effects of early environmental stress, (2) incorporating information about non-stress related sources of individual differences, (3) considering the possibility of adaptive behavior without specific evolutionary adaptations, and (4) considering whether the attachment construct is critical to his analysis.

If parenting behavior influences attachment, then parental investment (PI) theory can predict sex differences and distributions of attachment styles across cultures. Trivers-Willard, local resource competition, and local resource enhancement models make distinct predictions for sex-biased parental responsiveness relevant to attachment. Parental investment and attachment probably vary across cultures in relation to “local fitness currencies” for status, wealth, and well-being.

The translation of research findings from other primates to humans, and from infants to adults within our own species, requires great care. If the many neurological, behavioral and adaptive distinctions between these groups are not precisely defined and considered, erroneous conclusions about evolutionary history and developmental processes may result.

Del Giudice's examination of sex differences in reproductive strategy within an attachment context is well taken. Sex has been studied as behavior within romantic relationships, but attachment styles should also be reflected in strategic behavior within relationships that are sexual. This seems particularly true within adolescence, and sex differences may be better reflected as differences in correlation patterns of process variables than as main effects models.

Sexual behaviour and mate choice are key intervening variables between attachment and life histories. We propose a set of predictions relating attachment, reproductive strategies, and mate choice criteria.

Del Giudice proposes that short-term mating strategies are adaptive for attachment-avoidant men. We argue that this model (1) does not apply to the majority of avoidant men (fearful-avoidants); (2) is based on limited evidence that the remaining subset of avoidant men (dismissing-avoidants) engage in short-term mating strategies; and (3) disregards the importance of pair bonding even for dismissing-avoidants.

In the first part of this response, commentators' critiques to the target article are reviewed and addressed. The main discussion topics are the role of attachment in developmental plasticity; the relationship between attachment, mating, and reproductive strategies; the existence, magnitude, and developmental timing of sex differences in attachment; the adaptiveness of insecure styles; and the neurobiology of attachment and reproduction. The model's assumptions are clarified, and a number of methodological issues that can confound the interpretation of research findings are examined. In the second part, various proposals made by commentators are synthesized, and directions for future research and theoretical improvement are outlined. In addition, the issue of disorganized and fearful attachment is tentatively addressed. It is argued that different theoretical perspectives are converging toward a consistent and comprehensive theory of human reproductive strategies.

According to Aristotle, humans are the rational animal. The borderline between rationality and irrationality is fundamental to many aspects of human life including the law, mental health, and language interpretation. But what is it to be rational? One answer, deeply embedded in the Western intellectual tradition since ancient Greece, is that rationality concerns reasoning according to the rules of logic – the formal theory that specifies the inferential connections that hold with certainty between propositions. Piaget viewed logical reasoning as defining the end-point of cognitive development; and contemporary psychology of reasoning has focussed on comparing human reasoning against logical standards.

Bayesian Rationality argues that rationality is defined instead by the ability to reason about uncertainty. Although people are typically poor at numerical reasoning about probability, human thought is sensitive to subtle patterns of qualitative Bayesian, probabilistic reasoning. In Chapters 1–4 of Bayesian Rationality (Oaksford & Chater 2007), the case is made that cognition in general, and human everyday reasoning in particular, is best viewed as solving probabilistic, rather than logical, inference problems. In Chapters 5–7 the psychology of “deductive” reasoning is tackled head-on: It is argued that purportedly “logical” reasoning problems, revealing apparently irrational behaviour, are better understood from a probabilistic point of view. Data from conditional reasoning, Wason's selection task, and syllogistic inference are captured by recasting these problems probabilistically. The probabilistic approach makes a variety of novel predictions which have been experimentally confirmed. The book considers the implications of this work, and the wider “probabilistic turn” in cognitive science and artificial intelligence, for understanding human rationality.

Reasoning with conditionals is often thought to be non-monotonic, but there is no incompatibility with classical logic, and no need to formalise inference itself as probabilistic. When the addition of a new premise leads to abandonment of a previously compelling conclusion reached by modus ponens, for example, this is generally because it is hard to think of a model in which the conditional and the new premise are true.

Oaksford & Chater (O&C) argue that a rational analysis is required to explain why a functional process model is successful, and that, when a rational analysis is intractable, the prospects for understanding cognition from a functional perspective are gloomy. We discuss how functional explanations can be arrived at without seeking the optimal response function demanded by a rational analysis, and argue that explaining function does not require optimality.

Oaksford & Chater (O&C) aim to provide teleological explanations of behavior by giving an appropriate normative standard: Bayesian inference. We argue that there is no uncontroversial independent justification for the normativity of Bayesian inference, and that O&C fail to satisfy a necessary condition for teleological explanations: demonstration that the normative prescription played a causal role in the behavior's existence.

Oaksford & Chater (O&C) rely on a data fitting approach to show that a Bayesian model captures the core reasoning data better than its logicist rivals. The problem is that O&C's modeling has focused exclusively on response output data. I argue that this exclusive focus is biasing their conclusions. Recent studies that focused on the processes that resulted in the response selection are more positive for the role of logic.

I agree with Oaksford & Chater (O&C) that human beings resemble Bayesian reasoners much more closely than ones engaging standard logic. However, I have many problems with their “rational analysis” framework, which appears to be rooted in normative rather than ecological rationality. The authors also overstate everyday rationality and neglect to account for much relevant psychological work on reasoning.

Bayesian Rationality (Oaksford & Chater 2007) illustrates the strengths of Bayesian models of cognition: the systematicity of rational explanations, transparent assumptions about human learners, and combining structured symbolic representation with statistics. However, the book also highlights some of the challenges this approach faces: of providing psychological mechanisms, explaining the origins of the knowledge that guides human learning, and accounting for how people make genuinely new discoveries.

One of the most striking features of “Bayesian rationality” is the detail with which behavior on logical reasoning tasks can now be predicted and explained. This detail is surprising, given the state of the field 10 to 15 years ago, and it has been brought about by a theoretical program that largely ignores consideration of cognitive processes, that is, any kind of internal behavior that generates overt responding. It seems that an increase in explanatory power can be achieved by restricting a psychological theory.

Bayesian rationality is an important contribution to syllogistic inference, but it has limitations. The claim that confidence in a conclusion is a function of informativeness of the max-premise is anomalous because this is the least probable premise. A more plausible account is that confidence is inversely related to complexity. Bayesian rationality should be supplemented with principles based on cognitive complexity.

Oaksford & Chater (O&C) would need to define rationality if they want to argue that stomachs are not rational. The question of rationality, anyhow, is orthogonal to the debate concerning whether humans use classical deductive logic or probabilistic reasoning.

Because the addition of the conditional premise tends to increase modus ponens (MP) inferences, Oaksford & Chater argue that the additional knowledge is assimilated to world knowledge before the Ramsey test is carried out to evaluate P(q|p), so that the process of applying the Ramsey test could become indistinguishable from the process of applying the second-step conditionalization.

Oaksford & Chater's (O&C's) account of deductive reasoning is parsimonious at a local level (because a rational model is used to explain a wide range of behavior) and at a global level (because their Bayesian approach connects to other areas of research). Their emphasis on environmental structure is especially important, and the power of their approach is seen at both the computational and algorithmic levels.

Is human cognition best described by optimal models, or by adaptive but suboptimal heuristic strategies? It is frequently hard to identify which theoretical model is normatively best justified. In the context of information search, naïve subjects' heuristic strategies are better motivated than some “optimal” models.