Animals detect and acquire resources through a sequence of shape changes. This process is tightly coupled to the sensory and mechanical ecology of the animal. Building physical models allow us to prescind from modeling these aspects of the environment, which may not yet be described or suitably abstracted. The significance of this hybrid of physical modeling and experimentation to the acquisition of scientific knowledge is discussed.

The drug-reinforcement theory explains why humans get engaged in drug taking behavior. This theory posits that drugs of abuse serve as biological rewards by activating the reinforcement system. Although from a psychological and neurobiological perspective this theory is extremely helpful, it does not tell us about the drug-taking motives and motivation of an individual. The definition of drug instrumentalization goals will improve our understanding of individual drug-taking profiles.

Studies of conditioning in simple systems are best interpreted in terms of the formation of excitatory links. The mechanisms responsible for such conditioning contribute to the associative learning effects shown by more complex systems. If a dual-system approach is to be avoided, the best hope lies in developing standard associative theory to deal with phenomena said to show propositional learning.

Rational models vary in their goals and sources of justification. While the assumptions of some are grounded in the environment, those of others – which I label probabilistic models – are induced and so require more traditional sources of justification, such as generalizability to dissimilar tasks and making novel predictions. Their contribution to scientific understanding will remain uncertain until standards of evidence are clarified.

Darwinian evolution, defined as evolution arising from selection based directly on the properties of individuals, does not account for cultural constructs providing the organizational basis of human societies. The difficulty with linking Darwinian evolution to structural properties of cultural constructs is exemplified with kinship terminologies, a cultural construct that structures and delineates the domain of kin in human societies.

Those who identify colours with physical properties need to say how the content of colour experiences relate to their favoured identifications. This is because it is not plausible to hold that colour experiences represent things as having the physical properties in question. I sketch how physical realists about colour might tackle this item of unfinished business.

We see no grounds for insisting that, because the concept natural number is abstract, its foundations must be innate. It is possible to specify domain general learning processes that feed into more abstract concepts of numerical infinity. By neglecting the messiness of children's slow acquisition of arithmetical concepts, Rips et al. present an idealized, unnecessarily insular, view of number development.

Sussman et al. describe an ecological property of the speech signal that is putatively functional in perception. An important issue, however, is whether their putative cue is an emerging feature or whether the second formant (F2) onset and the F2 vowel actually provide independent cues to perceptual categorization. Regardless of the outcome of this issue, an important goal of speech research is to understand how multiple cues are evaluated and integrated to achieve categorization.

Keller & Miller (K&M) assert that mental disorders could not have evolved as adaptations, but they fail to make their case against the theory of the evolutionary origin of bipolar disorder that I have proposed (Sherman 2001). Such an idea may be unorthodox, but it has considerable explanatory power and heuristic value.

Cognitive function certainly poses the biggest challenge for computational neuroscience. As we argue, past efforts to build neural models of cognition (the target article included) had too narrow a focus on implementing rule-based language processing. The problem with these models is that they sacrifice the advantages of connectionism rather than building on them. Recent and more promising approaches for modeling cognition build on the mathematical properties of distributed neural representations. These approaches truly exploit the key advantages of connectionism, that is, the high representational power of distributed neural codes and similarity-based pattern recognition. The architectures for cognitive computing that emerge from these approaches are neural associative memories endowed with additional mapping operations to handle invariances and to form reduced representations of combinatorial structures.

What people report is, at times, the best evidence we have for what they experience. Newell & Shanks (N&S) do a service for debates regarding the role of unconscious influences on decision making by offering some sound methodological recommendations. We doubt, however, that those recommendations go far enough. For even if people have knowledge of the factors that influence their decisions, it does not follow that such knowledge is conscious, and plays a causal role, at the time the decision is made. Moreover, N&S fail to demonstrate that unconscious thought plays no role at all in decision making. Indeed, such a claim is quite implausible. In making these points we comment on their discussion of the literature on expertise acquisition and the Iowa Gambling Task.

The facial expression of pain is the end product of a complex process that is, in part, emotional. The evolutionary study of facial expression must account for the social nature of human consciousness and should address the questions of why empathy exists, the adaptive importance of empathy, and whether facial expression is a mechanism of empathy and second-person consciousness.

Shanker & King's (S&K's) dynamic systems approach converges with developments in social anthropological studies of communication which were long ago anticipated in the writings of Volosinov and Schutz. Following a review of these writings, this commentary suggests that a dynamic systems approach should distinguish communion from communication. It concludes with a remark on the evolutionary implications of the approach.

Cowan assumes a unitary capacity-limited attentional focus. We argue that two main problems need to be solved before this assumption can complement theoretical knowledge about human cognition. First, it needs to be clarified what exactly the nature of the elements (chunks) within the attentional focus is. Second, an elaborated process model needs to be developed and testable assumptions about the proposed capacity limitation need to be formulated.

The locus equation proposal ignores a fundamental difference between human speech perception and nonhuman echolocation and sound localization, offers a questionable account of the function of consonant-vowel coarticulation, and is further undermined if the effects of other forms of coarticulation are considered. The function of coarticulation is to convey phonetic information rapidly and reliably.

In O'Regan & Noë's (O&N's) account for the phenomenal experience of seeing, awareness is equated to what is within the current focus of attention. They find no place for a distinction between phenomenal and access awareness. In doing so, they essentially present a dualistic solution to the mind-brain problem, and ignore that we do have phenomenal experience of what is outside the focus of attention.

NHSTP is embedded in the research of “cognitive science.” Its use is based on unstated assumptions about the practices of sampling, “operationalizing,” and using group data. NHSTP has facilitated both research and theorizing – research findings of limited interest – diverse theories that seldom complement one another. Alternative methods are available for data acquisition and analysis, and for assessing the “truth- value” of generalizations.

Evolutionary psychologists should go beyond research on individual differences in attitudes and focus more on detailed models of psychological mechanisms. We argue for complementing attitude research with agent-based computational modeling of mate choice. Agent-based models require detailed specification of individual choice mechanisms that can be evaluated in terms of both their psychological plausibility and the population-level outcomes they produce.

Most psychometricians believe that the significance test is counterproductive. I have read Chow's book to see whether it addresses or rebuts any of the key facts brought out by the psychometricians. The book is empty on this score; it is entirely irrelevant to the current debate. It presents nothing new and is riddled with errors.

Mitchell et al. propose that associative learning in humans and other animals requires the formation of propositions by means of conscious and controlled reasoning. This approach neglects important aspects of current thinking in evolutionary biology and neuroscience that support the claim that learning, here exemplified by fear learning, neither needs to be conscious nor controlled.