The null-hypothesis significance-test procedure (NHSTP) is defended in the context of the theory-corroboration experiment, as well as the following contrasts: (a) substantive hypotheses versus statistical hypotheses, (b) theory corroboration versus statistical hypothesis testing, (c) theoretical inference versus statistical decision, (d) experiments versus nonexperimental studies, and (e) theory corroboration versus treatment assessment. The null hypothesis can be true because it is the hypothesis that errors are randomly distributed in data. Moreover, the null hypothesis is never used as a categorical proposition. Statistical significance means only that chance influences can be excluded as an explanation of data; it does not identify the nonchance factor responsible. The experimental conclusion is drawn with the inductive principle underlying the experimental design. A chain of deductive arguments gives rise to the theoretical conclusion via the experimental conclusion. The anomalous relationship between statistical significance and the effect size often used to criticize NHSTP is more apparent than real. The absolute size of the effect is not an index of evidential support for the substantive hypothesis. Nor is the effect size, by itself, informative as to the practical importance of the research result. Being a conditional probability, statistical power cannot be the a priori probability of statistical significance. The validity of statistical power is debatable because statistical significance is determined with a single sampling distribution of the test statistic based on H0, whereas it takes two distributions to represent statistical power or effect size. Sample size should not be determined in the mechanical manner envisaged in power analysis. It is inappropriate to criticize NHSTP for nonstatistical reasons. At the same time, neither effect size, nor confidence interval estimate, nor posterior probability can be used to exclude chance as an explanation of data. Neither can any of them fulfill the nonstatistical functions expected of them by critics.

In visual science the term filling-in is used in different ways, which often leads to confusion. This target article presents a taxonomy of perceptual completion phenomena to organize and clarify theoretical and empirical discussion. Examples of boundary completion (illusory contours) and featural completion (color, brightness, motion, texture, and depth) are examined, and single-cell studies relevant to filling-in are reviewed and assessed. Filling-in issues must be understood in relation to theoretical issues about neural–perceptual isomorphism and linking propositions. Six main conclusions are drawn: (1) visual filling-in comprises a multitude of different perceptual completion phenomena; (2) certain forms of visual completion seem to involve spatially propagating neural activity (neural filling-in) and so, contrary to Dennett's (1991; 1992) recent discussion of filling-in, cannot be described as results of the brain's “ignoring an absence” or “jumping to a conclusion”; (3) in certain cases perceptual completion seems to have measurable effects that depend on neural signals representing a presence rather than ignoring an absence; (4) neural filling-in does not imply either “analytic isomorphism” or “Cartesian materialism,” and thus the notion of the bridge locus – a particular neural stage that forms the immediate substrate of perceptual experience – is problematic and should be abandoned; (5) to reject the representational conception of vision in favor of an “enactive” or “animate” conception reduces the importance of filling-in as a theoretical category in the explanation of vision; and (6) the evaluation of perceptual content should not be determined by “subpersonal” considerations about internal processing, but rather by considerations about the task of vision at the level of the animal or person interacting with the world.

According to the dominant computational approach in cognitive science, cognitive agents are digital computers; according to the alternative approach, they are dynamical systems. This target article attempts to articulate and support the dynamical hypothesis. The dynamical hypothesis has two major components: the nature hypothesis (cognitive agents are dynamical systems) and the knowledge hypothesis (cognitive agents can be understood dynamically). A wide range of objections to this hypothesis can be rebutted. The conclusion is that cognitive systems may well be dynamical systems, and only sustained empirical research in cognitive science will determine the extent to which that is true.

Advanced perceptual systems are faced with the problem of securing a principled (ideally, veridical) relationship between the world and its internal representation. I propose a unified approach to visual representation, addressing the need for superordinate and basic-level categorization and for the identification of specific instances of familiar categories. According to the proposed theory, a shape is represented internally by the responses of a small number of tuned modules, each broadly selective for some reference shape, whose similarity to the stimulus it measures. This amounts to embedding the stimulus in a low-dimensional proximal shape space spanned by the outputs of the active modules. This shape space supports representations of distal shape similarities that are veridical as Shepard's (1968) second-order isomorphisms (i.e., correspondence between distal and proximal similarities among shapes, rather than between distal shapes and their proximal representations). Representation in terms of similarities to reference shapes supports processing (e.g., discrimination) of shapes that are radically different from the reference ones, without the need for the computationally problematic decomposition into parts required by other theories. Furthermore, a general expression for similarity between two stimuli, based on comparisons to reference shapes, can be used to derive models of perceived similarity ranging from continuous, symmetric, and hierarchical ones, as in multidimensional scaling (Shepard 1980), to discrete and nonhierarchical ones, as in the general contrast models (Shepard & Arabie 1979; Tversky 1977).

Historically, studies of the role of endogenous hormones in developmental differentiation of the sexes have suggested that mammalian sexual differentiation is mediated primarily by testicular androgens, and that exposure to androgens in early life leads to a male brain as defined by neuroanatomy and behavior. The female brain has been assumed to develop via a hormonal default mechanism, in the absence of androgen or other hormones. Ovarian hormones have significant effects on the development of a sexually dimorphic cortical structure, the corpus callosum, which is larger in male than in female rats. In the females, removal of the ovaries as late as Day 16 increases the cross-sectional area of the adult corpus callosum. Treatment with low-dose estradiol starting on Day 25 inhibits this effect. Female callosa are also enlarged by a combination of daily postnatal handling and exogenous testosterone administered prior to Day 8. The effects of androgen treatment are expressed early in development, with males and testosterone-treated females having larger callosa than control females as early as Day 30. The effects of ovariectomy do not appear until after Day 55. These findings are more consistent with other evidence of a later sensitive period for ovarian feminization as compared to androgenic masculinization.

According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individual's existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction.

The suggestion that analytic isomorphism should be rejected applies especially to the domain of speech perception because (1) the guiding assumption that solving the lack of invariance problem is the key to explaining speech perception is a form of analytic isomorphism, and (2) after nearly half a century of research there is virtually no empirical evidence of isomorphism between perceptual experience and lower-level processing units.

Studies of callosal sexual differentiation have concentrated on global measures of callosal size, using the rat as a model for studies of potential hormonal mechanisms. It is time to shift the study of callosal sexual differentiation to a more cellular level. Finally, there are potential problems with using the female rat as the primary model for understanding hormonal mechanisms during postnatal life.

Eigenfeatures are created by the principal component approach (PCA) used on objects described by a low-level code (i.e., pixels, Gabor jets). We suggest that eigenfeatures act like the flexible features described by Schyns et al. They are particularly suited for face processing and give rise to class-specific effects such as the other-race effect. The PCA approach can be modified to accommodate top-down constraints.

Van Gelder's characterization of the differences between the dynamical and computational hypotheses, in terms of the contrast between change versus state and geometry versus structure, suggests that the dynamical approach is also at odds with classical mechanism. Dynamical and mechanistic approaches are in fact allies: mechanism can identify components whose properties define the variables that are related in dynamical analyses.

Although various statistical measures may have other valid uses, the single purpose served by statistical significance testing in the epistemology of experimental science is as a peremptory rebuttal of one potential alternative interpretation of the data.

Failure to take note of distinctive attributes in the distal stimulus leads to an inadequate proximal encoding. Representation of similarities in Chorus suffers in this regard. Distinctive qualities may require additional complex representation (e.g., reference to linguistic terms) in order to facilitate discrimination. Additional semantic information, which configures proximal attributes, permits accurate identification of true veridical stimuli.

The human visual system is capable of learning both abstract and specific mappings to underlie shape recognition. How could dissimilar shapes be mapped to the same location in visual representation space, yet similar shapes be mapped to different locations? Without fundamental changes, Chorus, like other single-system models, could not accomplish both mappings in a manner that accounts for recent evidence.

van Gelder argues that computational and dynamical systems are mathematically distinct kinds of systems. Although there are real experimental and theoretical differences between adopting a computational or dynamical perspective on cognition, and the dynamical approach has much to recommend it, the debate cannot be framed this rigorously. Instead, what is needed is careful study of concrete models to improve our intuitions.

Physiological evidence predicts a model of concept categorisation that evolves through direct interaction with object feature selection. The requirement stated by Schyns et al. for feature plasticity is supported, but important caveats raise a question about the level at which feature identification can occur. Visual attribute selection for feature creation is likely to be directed by top-down and attentional processes.

The question is broadened from isomorphism to invertible transformation and optimal representation. Motivations are drawn from image compression but with an emphasis on object segmentation. Filling-in is considered as the phenomenal side of the binding process with back-surface filling-in being important. Finally, re-normalization of local filtering by globally integrated context is emphasized.

If the NHSTP procedure is essential for controlling for chance, why is there little, if any, discussion of the nature of chance by Chow and other advocates of the procedure. Also, many criticisms that Chow takes to be aimed against the NHSTP (null-hypothesis significance-test) procedure are actually directed against the kind of theory that is tested by the procedure.

I discuss Fitch & Denenberg's argument that no correction for brain size is needed when assessing callosal size. Morphometric criteria may not be sufficient to determine whether corrections are needed. Functional studies of callosal transfer will ultimately specify whether corrections for size are necessary in each case.

Van Gelder presents the dynamical hypothesis as a novel law of qualitative structure to compete with Newell and Simon's (1976) physical symbol systems hypothesis. Unlike Newell and Simon's hypothesis, the dynamical hypothesis fails to provide necessary and sufficient conditions for cognition. Furthermore, imprecision in the statement of the dynamical hypothesis renders it unfalsifiable.

The concept of distal similarity that plays a crucial role in Edelman's theory of representation is called into question in this commentary on theoretical as well as empirical grounds. A possible confusion between shape and (knowledge of) its referent, the problem of the subjective world, redundancy, and large individual differences in subjective space encountered in contrived universes are discussed.