An impressive review of brain neurophysiology provides the basis for modelling the dynamics of transmission in neural circuits, using appropriate nonlinear mathematics. The coverage is unbalanced, however: the parallel dynamics at the level of behaviour and sensory-cognitive processes are sparsely addressed, so the final chapter fails to indicate the complexity and subtlety of relevant modern work.

Hierarchical systems such as cortical tissue will indeed exhibit dynamics that are scale-dependent, but Nunez's conjecture that theoretically independent transfer functions for each level can be specified and then combined ignores the fact that, in cortex, the same excitatory neuronal populations will contribute to both local and global dynamics.

A convincing case is made for the importance of conditioning in social interaction, but more than Pavlovian conditioning is involved: UR (unconditioned response) modification, imprinting, Skinnerian conditioning, and other forms of behavior modification are adduced as Pavlovian. Beyond its value as an icon, control theory is not brought to bear in an informative fashion on these phenomena.

Revonsuo's evolution-based theory places the nightmare as a prototype dream, which fully realizes its biological function. However, individuals who experience both repetitive (PTSD) nightmares and/or lifelong nontraumatic nightmares demonstrate impaired psychological functioning and attenuated information-processing. The importance of reconciling these discrepancies are addressed and ideas for providing stronger empirical tests of the model are presented.

[Revonsuo]

Robert Koch (1843–1910) discovered the causal agents for tuberculosis, cholera, and anthrax. The 1905 Nobel Prize acknowledged Koch's criteria for identifying the causal agent of an infectious disease. These criteria remain useful and the data reviewed below show that the cholinergic contributions to REM sleep control are confirmed by Koch's postulates.

[Hobson et al.]

Evolutionary theory supplies a framework for integrative models of social behavior. In addition to those that include conditioning, evolutionary theory is equipped to explain the acquisition of structures designed to enable individuals to learn by observing others, create mental models of the environment, and coordinate social interactions by taking the perspectives of others.

Simple heuristics that make us smart presents a valuable and valid interpretation of how we make fast decisions particularly in situations of ignorance and uncertainty. What is missing is how this intersects with thinking under even greater uncertainty or ignorance, such as novice problem solving, and with the development of expert cognition.

A direct equivalence between neural function and neural structure does not provide a fruitful approach to understanding brain functioning. Arbib et al. describe a new and powerful approach to circumvent this problem, which they call schema theory. However, in examples they fall prey to the tradition of finding such equivalences, not doing schema theory justice.

Nevin & Grace's behavioral-momentum model accommodates a large body of data. This commentary highlights some experimental findings that the model does not always predict. The model does not consistently predict resistance to change when response-independent food is delivered simultaneously with response-contingent food, when drugs are used as response disrupters, and when responding is reinforced under single rather than multiple schedules of reinforcement.

This commentary focuses on how the large-scale cortical dynamics described in Nunez's target article are related to various phenomena at different scales, both spatial and temporal, in particular, how the brain dynamics measured with EEG could relate to (i) experience and mental state, (ii) neuromodulatory effects, and (iii) spontaneous firing and autogenerated electromagnetic effects.

Nevin & Grace identify a difference between the predictions of delay reduction theory and the contingency-ratio account underlying behavioral momentum approaches to choice. This is shown not to be a true difference. The role of the overall context of reinforcement must be carefully incorporated by any theory of choice.

Neural organization contains a wealth of facts from all areas of brain research and provides a useful overview of physiological data for those working outside the immediate field. Furthermore, it gives a good example that the approach of dynamical system theory together with the concepts of cooperative and competitive interaction can be fruitful for an interdisciplinary approach to cognition.

Vertes & Eastman argue against the popular idea that dreams promote memory consolidation and suggest instead that REM provides periodic endogenous stimulation during sleep. Although we suspect that much of the debate on the function of dreams reflects a too eager acceptance of the “adaptationist program,” we nonetheless support the position of the authors and propose a specific advantage of periodic REM activity.

[Vertes & Eastman]

Despite a promising introduction, Domjan et al.'s target article fails to capitalize on the concept of information intrinsic to control theory. The authors limit their application of feed-forward models to simple nondynamic cases. Their applications to social behavior are stimulus-occasioned responses. Agents might as well be dogfood! The notion of “conditioning” is generalized without warrant to explain virtually any acquired predictive capability.

Fast and frugal heuristics function accurately and swiftly over a wide range of decision making processes. The performance of these algorithms in the social domain would be an object for research. The use of simple algorithms to investigate social decision-making could prove fruitful in studies of nonhuman primates as well as humans

Among the metaphors used in the target article are “musical instruments,” “water waves,” and other types of mechanical oscillators. The corresponding equations have inertial properties and lead to standing waves that depend on boundary conditions. Other, physiologically relevant quantities like refractory times are not contained in the mechanical oscillator model but occur naturally, for instance, in biological forest fire metaphors.

The extent to which Pavlovian feed-forward mechanisms operate in primates is debatable. Monkeys and apes are long-lived, usually gregarious, and intelligent animals reliant on learned behavior. Learning occurs during play, mother-infant interactions, and grooming. We address these situations, and are hesitant to accept Domjan et al.'s reliance on Pavlovian conditioning as a major operant in primates.

The AIM model proposed by Hobson et al. is interesting: We know the neurophysiological aspects of the activation process (A) and the external input (I), but very little about the internal input and neurocognitive process (M). Internal input could be an expression of unconscious experiences memorised by the subject containing his emotional and cognitive history. Therefore internal input could not be chaotic but might have an emotional and affective sense bound to the unconscious. The fact that dreams are present in the absence of REM sleep means that they may originate from other structures besides the pons. These structures may represent the archives of dreamer's affective history.

[Hobson et al.; Solms]

Gigerenzer and his co-workers make some bold and striking claims about the relation between the fast and frugal heuristics discussed in their book and the traditional norms of rationality provided by deductive logic and probability theory. We are told, for example, that fast and frugal heuristics such as “Take the Best” replace “the multiple coherence criteria stemming from the laws of logic and probability with multiple correspondence criteria relating to real-world decision performance.” This commentary explores just how we should interpret this proposed replacement of logic and probability theory by fast and frugal heuristics.

In order to reach a better understanding of brain function, conceptual synergies linking empirical neurobiological studies and neurocomputational studies should be pursued. I describe an example of a potential synergy based on studies of neural network pruning. Simulations demonstrate that selective elimination of connections enhances the computational capacity of networks capable of temporal processing. These findings may shed light on the functional significance of postnatal neuro-developmental pruning of cortical connections that occurs in mammals.