Attributing the creation of new features to categorization requirements implies that the exemplars displayed are correctly assigned to their category. This constraint limits the scope of Schyns et al.'s proposal to supervised learning. We present data suggesting that this constraint is unwarranted and we argue that feature creation is better thought of as a byproduct of the attentional, on-line processing of incoming information.

A few findings from our laboratory are provided as evidence favoring “isomrphism” in filling-in. One is the responsivity of macaque-cortical area V1 cells to a stimulus designed for surface filling-in at the blind spot. Another is a phenomenological observation of motion aftereffect confined within a filled-in surface at the blind spot. Our recent study on the monkey's perception of surface filling-in at a scotoma is also mentioned.

Chow's one-tailed null-hypothesis significance-test procedure, with its rationale based on the elimination of chance influences, is not appropriate for theory-corroboration experiments. Estimated effect sizes and their associated standard errors or confidence limits will always suffice.

Edelman's target article purports to be about veridical representations. I argue that it would be a mistake to think it has much to do with veridicality as normally understood.

The claimed link between dominance and free testosterone is an intriguing one but problems remain in attempting to link this single hormonal measure to human behaviour. These include the heterogeneous nature of dominance, the precise nature of the correlation(s), and whether only testosterone is important.

Van Gelder presents the distinction between dynamical systems and digital computers as the core issue of current developments in cognitive science. We think this distinction is much less important than a reassessment of cognition as a neurally, bodily, and environmentally embedded process. Embedded cognition lines up naturally with dynamical models, but it would also stand if combined with classic computation.

Schyns et al. make a strong case for context-dependent feature discovery. The features computed from specialized and diverse data-sets help to coordinate their activity by adapting so as to emphasize what is related across sets. Their perspective can be strengthened and extended by formal arguments for the contextual guidance of learning and processing and by neurobiological and psychological evidence of structures and processes that implement this guidance.

A test of a biosocial model is reported in which we found no impact of circulating testosterone on either status-seeking or aggression. The fact that sex differences in competitiveness and aggression appear in childhood strongly suggests that the major impact of testosterone is organisational. Whereas dominance and resources are linked among males, female aggression may be a function of pure resource competition, with no element of status-seeking.

The dynamics/computation debate recalls a similar debate in the evolutionary biology community concerning the relative primacy of theories of structure versus theories of change. A full account of cognition will require a rapprochement between such theories and will include both computational and dynamical notions. The key to making computation relevant to cognition is not making it analog, but rather understanding how functional information-processing structures can emerge in complex dynamical systems.

Edelman's memory-based approach to visual representation is preferable to parts-based alternatives. However, the existing algorithms for learning the shape prototypes are biologically implausible because they are nonlocal and nonconstructive. There is an alternative learning algorithm that constructs a mixture model of prototypes on-line, using only local information, and is more biologically plausible and may perform sufficiently well.

Analytical isomorphism is an instance of the demand for a transparent relation between vehicle and content, which is central to the mind-body problem. One can abandon transparency without begging the question with regard to the mind-body problem.

Chow correctly pinpoints several confusions in the criticisms of statistical hypothesis testing but his book is considerably weakened by its own confusions about concepts of testing (perhaps owing to an often very confusing literature). My focus is on his critique of power analysis (Ch. 6). Having denied that NHSTP considers alternative statistical hypotheses, and having been misled by a quotation from Cohen, Chow finds power analysis conceptually suspect.

The attempt to provide a faithful mapping from distal shape space to proximal state space in terms of a higher order relationship defined over proximal similarity space stumbles on the context sensitivity of higher order relationships. Proportional analogy problems using quadruples of figures illustrate that for a number of interesting perceptual problems, the number of relevant dimensions cannot be reduced.

Chow's (1996) Statistical significance is a defence of null-hypothesis significance testing (NHSTP). The most common and straightforward use of significance testing is for the statistical corroboration of general hypotheses. In this case, criticisms of NHSTP, at least those mentioned in the book, are unfounded or misdirected. This point is driven home by the author a bit too forcefully and meticulously. The awkward and cumbersome organisation and argumentation of the book makes it even harder to read.

The testosterone–dominance model is noteworthy but should incorporate the ecological factors that often underlie variability in basal testosterone. This is evident in the ethnic testosterone differences discussed in the target article (sect. 8). The significance of acute changes in testosterone levels in response to competition is also poorly understood. Significant metabolic effects have been reported, suggesting that other physiological explanations should be explored, independent of potential behavioral or social factors.

Implicit and explicit filling-in phenomena should be distinguished. Blind spot phenomena and mechanisms of boundary completion can be accounted for by implicit filling-in. Surface regions are “painted” with perceptual quantities, such as brightness, by explicit filling-in. “Filling-in” and “finding-out” relate to different computational tasks. Mechanisms of purposive computation (e.g., for navigation) evaluate local measurements, thus “finding out”; whereas mechanisms for grasping might require passive reconstruction, thus “filling in.”

Arguments for feature creation receive support from studies of young infants forming category representations from perceptual experience. A challenge for Schyns et al. will be to determine how a feature creation system might interface with a perceptual system that appears constrained to follow organizational principles that specify how edge segments should be grouped into functional units of coherent object representations.

Technical hitches mar van Gelder's proposed map of the conceptual landscape, particularly with respect to descriptive levels and the trio of instantiation, realisation, and implementation. However, for all the formal quibbles, van Gelder is onto something important; the tension he notes between computationalism and a dynamical alternative threatens to transform the way we conduct cognitive science research.

Chow's book makes a provocative contribution to the debate on the role of statistical significance, but it involves some important misconceptions in the presentation of the Fisher and Neyman/Pearson's theories. Moreover, the author's caricature-like considerations about “Bayesianism” are completely irrelevant for discarding the Bayesian statistical theory. These facts call into question the objectivity of his contribution.

Converging psychophysical evidence suggests that the human visual system parses shapes into component parts for the purposes of object recognition. We examine the Schyns et al. claim of “creation” of features in light of recent work on part-based representations of visual shape, particularly the perceptual rules that human vision uses to parse shapes.