Boundary completion and surface filling-in are computationally complementary processes whose multiple processing stages form processing streams that realize a hierarchical resolution of uncertainty. Such complementarity and uncertainty principles provide a new foundation for philosophical discussions about visual perception, and lead to neural explanations of difficult perceptual data.

Statistical significance testing has its problems, but so do the alternatives that are proposed; and the alternatives may be both more cumbersome and less informative. Significance tests remain legitimate aspects of the rhetoric of scientific persuasion.

Because “people create features to subserve the representation and categorization of objects” (abstract) Schyns et al. “provide an account of feature learning in which the components of a representation have close ties to the categorization history of the organism” (sect. 1.1). This commentary surveys self-organizing neural models that clarify this process. These models suggest how “top-down information should constrain the search for relevant dimensions/features of categorization” (sect. 3.4.2).

The Chorus scheme could be an important step in the search for solutions to the symbol grounding problem (Harnad 1990), but Edelman does not address the potential difficulties inherent in downgrading differences in favor of similarities in a categorization device. Isomorphism rather than representation is a more coherent way of thinking about Chorus whose modules are probably analytic rather than holistic.

This commentary proposes keeping the bridge locus construct with a revised definition which requires the bridge locus to be dynamic, representation-independent and influenced by top-down processes. The denial of the uniformity of content thesis is equivalent to dualism. The active perception perspective is a valuable one.

A consideration of underlying neural dynamics and the evolutionary process producing cognitive agents should complement the development of dynamical models of behavior. The geometrical aspects of dynamical systems theory which make it useful in the description of systems acting in an environment are less useful in understanding agents interacting with a range of environments, and may call for supplementation by evolutionary insights.

Sex dimorphism occurs when group means differ by four or more standard deviations. However, the average size of the corpus callosum is greater in males by about one standard deviation in rats, 0.2 standard deviation in humans, and virtually zero in mice. Furthermore, variations in corpus callosum size are related to brain size and are not sex specific.

From a statistician's viewpoint, the concepts discussed by Chow relating to “statistical” significance bear little resemblance to the concept developed in statistics. Whether or not “statistical significance” has a place in psychological research is a decision for psychologists, not statisticians, to make, but the decision should be based on a less flawed version of what is being considered.

Multidimensional space representations like those posited in Edelman's target article are not sufficient to capture all similarity phenomena. We discuss phenomena that are compatible with models of similarity that assume structured relational representations. An adequate model of similarity and perception will require multiple approaches to representation.

The Schyns et al. target article demonstrates that different classifications entail different representations, implying “flexible space learning.” We argue that flexibility is required even at the within-category level.

What new implications does the dynamical hypothesis have for cognitive science? The short answer is: none. The target article is basically an attack on traditional symbolic artificial intelligence (AI) and differs very little from prior connectionist criticisms of it. For the past 10 years, the connectionist community has been well aware of the necessity of using (and understanding) dynamically evolving, recurrent network models of cognition.

We address two major limitations of Schyns et al. First, we clarify their concept of “features” by postulating several levels for processing. The composition of the feature set at each level determines the set at the next higher level, following simple structural guidelines. Second, we show that our proposed framework reconciles feature-creation and fixed-feature approaches.

The target article discusses the classic blind spot, scotomas, subjective contours, and other so-called filling-in phenomena. Its purpose is to evaluate the idea that some theories of filling-in amount to tacit acceptance of Cartesian materialism and a form of psychophysical isomorphism. Pessoa et al. reject what is termed structural isomorphism as well as Cartesian materialism, but claim that neural processes adduced as underlying filling-in may be acceptable without implying isomorphism. The article supports the idea of perceiving as an active constructive process. However, the various subthemes are not clearly related to each other. Topological psychophysical isomorphism is indeed untenable, but the tacit assumption that filling-in enjoys any kind of unique status with respect to illuminating philosophical questions is doubtful.

The Fitch & Denenberg target article focuses almost exclusively on short gestation mammals, which differ substantially from long-gestation mammals in the timing and type of hormonal contribution to their sexual differentiation. Conclusions regarding the role of ovaries in female sexual differentiation may accordingly apply to only a limited number of species. Specific criticisms of the organizational effects of hormones stem from an incomplete reading of the original literature. The mechanisms proposed in this target article reflect an extension of the principle of hormonal organization, not a revolutionary restructuring.

The Chorus representation is a sparse, similarity-preserving representation achieved by a feedforward neural network. Hence it is probably better suited for interpolation than for categorization. This commentary raises the question of how to combine categorization with interpolation, whether feedforward networks can be reasonable models for parts of the cerebral cortex, and whether people can perform more than one interpolation at a time.

Chow pays lip service (but not much more!) to Type I errors and thus opts for a hard (all-or-none) .05 level of significance (Superego of Neyman/Pearson theory; Gigerenzer 1993). Most working scientists disregard Type I errors and thus utilize a soft .05 level (Ego of Fisher; Gigerenzer 1993), which lets them report gradations of significance (e.g., p < .001).

Schyns, Goldstone & Thibaut argue that categorization experience results in the learning of new perceptual features that are not derivable from the learner's existing feature set. We explore the meaning and implications of this “nonderivability” claim and relate it to the question of whether perceptual invariants are learnable, and if so, what might be entailed in learning them.

The rapprochement between methodology and statistics suggested by Chow's book is a much needed one. His examples suggest that the situation is even worse in psychology than in some other disciplines. It is suggested that both historical accuracy and attention to recent work on the foundations of statistics would be beneficial in achieving the goals that Chow seeks.

We argue that estrogen feminization of the brain is the result of a series of events initiated by differential androgen exposure. There is no need to postulate a feminizing process parallel to androgen-induced masculinization to explain the findings.

This commentary discusses three main requirements for models of vision, namely, translation and scale invariance, scalability, and hierarchy. Edelman's Chorus model falls short of fulfilling these requirements because it ignores the highly dynamic nature of vision. Incorporating an attentional mechanism and assuming geon-like prototype representations may enhance Chorus's plausibility as a model of human object recognition.