Stanovich & West's target article undervalues the power of implicit learning (particularly reinforcement learning). Implicit learning may allow the learning of more rational responses–and sometimes even generalisation of knowledge–in contexts where explicit, abstract knowledge proves only of limited value, such as for economic decision-making. Four other comments are made.

The Domjan et al. target article is a valuable summary of a vital field of conditioning and learning. It lists, thumbnails, and organizes classical and recent findings into a useful and familiar structure. Perhaps it is time to consider modern developments in ethology, experimental psychology, and computer science that supersede the traditional structure.

Arbib, Érdi, and Szentágothai's book seeks to present a multidisciplinary, multistrategied approach to the study of the mind-brain, encompassing structural, functional, and dynamic perspectives. However, the articulated framework is somewhat underspecified at the cognitive level. The representational level of analysis will need to be fleshed out if the explanatory potential of Arbib et al.'s framework is to be fulfilled.

Nunez's description of the brain as a medium capable of wave propagation has provided some fundamental insights into its dynamics. This approach soon reaches the descriptive limits of the brain as a physical system, however. We point out some biological constraints which differentiate the brain from physical systems and we elaborate on its consequences for future research.

The insights described by Nevin & Grace may be summarized without reference to the Newtonian concepts they suggest. The metaphor to Newtonian mechanics seems dubious in three ways: (1) extensions seem to lead to paradoxes; (2) many well-known phenomena are ignored; (3) the Newtonian concepts seem difficult to reconcile with the larger framework of evolutionary theory.

I agree with Revonsuo that dreaming, particularly about risky scenes, has a great selective advantage. Although the paleoamygdala system generally facilitates stress and alarm, the system which inhibits stress and alarm, initiates bold actions, and mediates learning in risky scenes is the arche, hippocampal system (Papez circuit). Because all thalamic nuclei are inhibited during sleep except arche, Papez probably also dreams in risky scenes.

[Revonsuo]

Lucid dreaming provides a test case for theories of dreaming. For example, whether or not “loss of self-reflective awareness” is characteristic of dreaming, it is not necessary to dreaming. The fact that lucid dreamers can remember to perform predetermined actions and signal to the laboratory allows them to mark the exact time of particular dream events, allowing experiments to establish precise correlations between physiology and subjective reports, and enabling the methodical testing of hypotheses.

[Hobson et al.; Solms]

Domjan et al. continue a consistent trend in Pavlovian conditioning, that of accounting for more behaviors while sacrificing specificity of predictions. Despite the sacrifice, their model provides a valuable framework within which social behavioral research may operate. It may also allow ethologists and evolutionary psychologists to pursue questions about which feed-forward systems should produce which behaviors in social settings.

Arbib, Érdi, and Szentágothai's book should be a required reading for any serious student of the brain. The scope and the accessibility of its presentation of the neurobiological data (especially the functional anatomy of select parts of the central nervous system) more than make up for the peculiarities of the theoretical stance it adopts.

The theory of local/global neocortical EEG dynamics responds to newly emerging conceptualizations in neuroscience. An extended application of the model to event-related EEG activity composed of distinctive global and local functional epochs with presumably different timing is proposed.

Nevin & Grace's approach is an interesting and useful attempt to find ways to measure “core” effects of a history of exposure to reinforcement. The momentum analogy makes intuitive sense, and the evidence for its utility is increasing. Several questions remain, however, about how the analogy will fare in the case of concurrent rather than sequential activities, about the use of extinction as a method to test resistance to change, and about the generality of some of the effects.

In this response, we clarify several misunderstandings of the understanding/acceptance principle and defend our specific operationalization of that principle. We reiterate the importance of addressing the problem of rational task construal and we elaborate the notion of computational limitations contained in our target article. Our concept of thinking dispositions as variable intentional-level styles of epistemic and behavioral regulation is explained, as is its relation to the rationality debate. Many of the suggestions of the commentators for elaborating two-process models are easily integrated into our generic dual-process account. We further explicate how we view the relation between System 1 and System 2 and evolutionary and normative rationality. We clarify our attempt to fuse the contributions of the cognitive ecologists with the insights of the original heuristics and biases researchers.

Neural organization describes an approach to analyzing neural function in anatomically defined subsystems in the brain, the hippocampus, cerebellum, sensory systems, thalamus, basal ganglia, and cerebral cortex, combining information on neurocircuitry with mathematical models that link structure with function. It is an up-to-date source on the major schemes and background for neural modeling of the central nervous system and is combined with a Web site that includes tutorials and on-line modeling possibilities.

Nunez is to be applauded for putting forward a theoretical brain model. In order to improve any model it needs to be experimentally testable. The model presented in the target article suffers from insufficient clarity as to how new experimental designs could be derived. This is a consequence of neglecting the purpose of the brain, which is to produce effective and adaptive behavior. It might be possible to overcome this drawback by including Hebb-based modeling.

To make possible the integration proposed by Domjan et al., psychologists first need to close the research gap between behavioral ecology and the study of Pavlovian conditioning. I suggest two strategies, namely, to adopt more behavioral ecological approaches to social behavior or to co-opt problems already addressed by behavioral ecologists that are especially well suited to the study of Pavlovian conditioning.

Evidence from 45 early studies of resistance to extinction following reinforcement of differing amounts, taken in sum, challenges both the basic and the augmented models of Nevin & Grace. The augmented model seems too ad hoc in salvaging the analogy between persistence in behavior and concepts from physics, as my meta-analysis of these data affirms.

How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), we explore fast and frugal heuristics – simple rules in the mind's adaptive toolbox for making decisions with realistic mental resources. These heuristics can enable both living organisms and artificial systems to make smart choices quickly and with a minimum of information by exploiting the way that information is structured in particular environments. In this précis, we show how simple building blocks that control information search, stop search, and make decisions can be put together to form classes of heuristics, including: ignorance-based and one-reason decision making for choice, elimination models for categorization, and satisficing heuristics for sequential search. These simple heuristics perform comparably to more complex algorithms, particularly when generalizing to new data – that is, simplicity leads to robustness. We present evidence regarding when people use simple heuristics and describe the challenges to be addressed by this research program.

The continuous ongoing mentation is experienced as dreams in some functional states. Mentation occurs with high speed, is driven by individual memory, and uses state-dependent processing strategies, context material, storage options, and retrieval access. Retrieval deserves more attention. Multiple state-shifts owing to individual meaning as extracted also during sleep concatenate dream narratives and define access to segments for awake recall.

[Hobson et al.; Nielson; Solms]

Metaphors and models involve correspondences between events in separate domains. They differ in the form and precision of how the correspondences are expressed. Examples include correspondences between phylogenic and ontogenic selection, and wave and particle metaphors of the mathematics of quantum physics. An implication is that the target article's metaphors of resistance to change may have heuristic advantages over those of momentum.

If fast and frugal heuristics are as good as they seem to be, who needs logic and probability theory? Fast and frugal heuristics depend for their success on reliable structure in the environment. In passive environments, there is relatively little change in structure as a consequence of individual choices. But in social interactions with competing agents, the environment may be structured by agents capable of exploiting logical and probabilistic weaknesses in competitors' heuristics. Aspirations toward the ideal of a demon reasoner may consequently be adaptive for direct competition with such agents.