The constructs of behavioral mass in research on the momentum of operant behavior and associative strength in Pavlovian conditioning have some interesting parallels, as suggested by Savastano & Miller. Some recent findings challenge the strict separation of operant and Pavlovian determiners of response rate and resistance to change in behavioral momentum, renewing the need for research on the interaction of processes that have traditionally been studied separately. Relatedly, Furedy notes that some autonomic responses may be refractory to conditioning, but a combination of operant contingencies and enriched Pavlovian stimulus-reinforcer relations may prove effective.

The specific mechanisms whereby Pavlovian conditioning leads to adaptive behavior need to be elaborated. There is no evidence that it is via reduction in the “destabilizing effect that time lags have on feedback control” (Domjan et al. 2000, sect. 3.3). The adaptive value of Pavlovian conditioning goes well beyond the regulation of social behavior.

For the authors' responses to comments similar to those expressed here by Balsam & Drew, please see: M. Domjan, B. Cusato, & R. Villarreal (2000). Extensions, elaborations, and explanations of the role of evolution and learning in the control of social behavior. BBS 23(2):269–82. [Authors' Response to first round of commentary.]

Artificial neural networks have weaknesses as models of cognition. A conventional neural network has limitations of computational power. The localist representation is at least equal to its competition. We contend that locally connected neural networks are perfectly capable of storing and retrieving the individual features, but the process of reconstruction must be otherwise explained. We support the localist position but propose a “hybrid” model that can begin to explain cognition in anatomically plausible terms.

Page is to be congratulated for challenging some misconceptions about neural representation. However, his target article, and the commentaries to it, highlight that the terms “local” and “distributed” are open to misinterpretation. These terms provide a poor description of neural coding strategies and a better taxonomy might resolve some of the issues.

Two meta-analyses of pharmacological research are presented, demonstrating that psychoactive drugs have consistent effects on EEG and sleep outside of their effects on REM sleep, and demonstrating that drugs other than those affecting sleep neurotransmitter systems and REM sleep can also alter reported nightmare occurrence. These data suggest that the neurobiology data terrain outside activation-synthesis may include sleep and dream electrophysiology, cognitive reports of dreaming, effects of alterations in consciousness on dreaming, immunology and host defense, and clinical therapies for sleep disorders.

The model and framework presented in the target article by Thelen et al. is an interesting effort that is able to account for the contextual variability in the A-not-B performance of 7–12-month-old infants. In the process of developing their framework, the authors discounted the concept of object as a useful notion in discussions of A-not-B performance. For Piaget and other developmentalists, the main evidence for the acquisition of the concept of object was the disappearance of A-not-B errors after age 12 months. However, the Thelen et al. model makes predictions of A-not-B outcomes over a much shorter, trial-to-trial time scale. Given the mismatch in the time scales over which analyses in the two approaches have been based, we wonder if the challenge to the concept of object has been misplaced.

In the A-not-B situation children reach toward location A when the object is at location B. Researchers interpret this as an error. I question this interpretation. Reaches are inaccurate only if the intention actually is to obtain the hidden object. If this is not the goal, then reaching for A may be accurate and there may be no error to be explained.

The authors' results support a functionalist conception of working memory: a manifold repertoire of schemes/schemas (long-term memory) and a small set of general-purpose “hidden operators.” Using some of these operators I define mental (i.e., endogenous) attention. Then, analyzing two of the authors' unexplained important findings, I illustrate the mental-attention model's explanatory power. Multivariate methodology that varies developmental, task differences, and individual differences is recommended.