Philosophical, dynamical, neural, network-theoretical, and cognitive ingredients of Tsuda's brain theory are discussed and anayzed. The integrative approach emphasized by Tsuda would be a welcome one.

Thelen et al. have a strong case for linking behavior with mind through nonrepresentational dynamics. Their case linking mind with brain is less compelling. Modified avenues are proposed for further exploration: greater emphasis on the dynamics of perception; use of chaotic instead of deterministic dynamics with noise; and use of intentionality instead of motivation, taking advantage of its creative dynamics to model genesis of goal-directed behaviors.

This commentary provides a critique of Tsuda's target article, focusing on the hippocampus and episodic long-term memory. More specifically, the relevance of Cantor coding and chaotic itinerancy for long-term memory functioning is considered, given what we know about the involvement of the hippocampus in the mediation of long-term episodic memory (based on empirical neuroimaging studies and investigations of brain-damaged amnesic patients).

A general framework for the explanation of perceptual phenomena as internalizations of external regularities was developed by R. N. Shepard. A particular example of this framework is his account of perceived curvilinear apparent motions. This paper contains a brief summary of the relevant psychophysical data, some basic kinematical considerations and examples, and several criticisms of Shepard's account. The criticisms concern the feasibility of internalization of critical motion types, the roles of simplicity and uniqueness, the contrast between classical physics and kinematic geometry, the import of perceived path curvilinearity, and the relation of perceptual and scientific knowledge.

The idea that biorobots can be used as a testbed for the evaluation of hypotheses about how an animal functions is supported. Generation of realistic feedback is a major advantage of biorobotic models. Nevertheless, skeptics can only be convinced that this approach is valid if significant biological insights are generated from its application.

Stoffregen & Bardy argue that unimodal invariants do not exist, and that only “amodal” invariants are possible. But they confuse two separate issues. Amodal invariants, we argue, do indeed exist to specify features of the environment, but not even an amodal invariant, in isolation, could specify their “presence” or “reality.”

An embodied movement-planning field cannot account for behavior and cognition more abstract than that of reaching. Instead, we propose an affordance field, and we sketch how it could enhance the analysis of the A-not-B error, underlie cognition, and serve as a base for language. Admittedly, a dynamic systems account of an affordance field awaits significant further development.

Most students are trained in using but not in actively choosing a research methodology. I support Hertwig and Ortmann's call for more rationality in the use of methodology. I comment on additional practices that sacrifice experimental control to the experimenter's convenience, and on the strange fact that such laissez-faire attitudes and rigid intolerance actually co-exist in psychological research programs.

After identifying similarities in the paradigmatic problems of biorobotics and ecological psychology, we suggest a way to compare the performance of robots with that of their biological targets. The crucial comparison is between the intentional dynamics of the robot and those of the targeted animal, a measure that depends critically on recognizing and describing the underlying affordance-effectivity match of the target system.

Tsuda offers advanced concepts to model brain functions, includ-ing “chaotic itinerancy,” “attractor ruins,” “singular-continuous nowhere-differentiable attractors,” “Cantor coding,” “multi-Milnor attractor systems,” and “dynamically generated noise.” References to physiological descriptions of attractor landscapes governing activity over cortical fields maintained by millions of action potentials may facilitate their application in future experimental designs and data analyses.

The information required for perception may be available in the energy arrays that stimulate sensory organs but in a form not directly suitable for the planning and execution of the organism's actions in the environment. The requisite form of information is obtained, with no loss of adequate perception, by representation of sensory stimuli in frames of reference determined by internal control signals producing actions. This process seems evolutionarily advantageous but makes perception essentially non-direct, regardless of the degree of intra- or inter-modal sensory integration.

I argue that (1) the accusation that psychological methods are too diverse conflates “reliability” with “validity”; (2) one must not choose methods by the results they produce – what matters is whether a method acceptably models the real-world situation one is trying to understand; (3) one must also distinguish methodological failings from differences that arise from the pursuit of different theoretical questions.

We present data on colour generalisation by chicks relevant to Tenenbaum and Griffiths' (T&G) Bayesian framework. Chicks were trained with either one or two colours, and tested for interpolation and extrapolation. T&G's framework predicts an observed lack of extrapolation on the red to yellow line in colour space. A modification incorporating stimulus uncertainty deals with a prototype effect, where an intermediate is preferred to exemplars. After training to complementary colours, chicks do not generalise across an intermediate grey as T&G predict. [Tenenbaum & Griffiths]

A-not-B behavior in various mammals and birds suggests it has been selected for during evolution. One scenario is that displacement to B of one food item from a trove at A should not distract the forager. Piagetian stage V experiments may not test for object permanence, but rather for the more abstract notion that physical objects can be unique.

Features of experimental design impose auxiliary hypotheses on experimenters. Hertwig & Ortmann rightly argue that the ways some variables are implemented in psychology cloud results, whereas the different implementations in economics provide for more robust results. However, not all design variables support this general conclusion. The repetition of trials may confuse results depending on what theory is being tested. We explore this in the case of simple bargaining games.

Robots, as well as software agents, can be of use in biology as implementations of a theory rather than as simulations of specific real world target systems. Such implementations generate hypotheses rather than representing them. Their behavior is not predicted, but rather observed, and is not expected to duplicate that of a target system. Scientific knowledge is gained through the testing of generated hypotheses.

Neurally inspired models of human cognition exude explanatory power without necessarily making predictions that can be verified behaviorally. This is the case for Tsuda's dynamic model. It is suggested that a simpler principle based on the nonlinear dynamic interaction between modules based on control of chaos, can achieve a similar theoretical goal in a cognitively verifiable way.

We find Stoffregen & Bardy's argument that the senses are united and that specificity exists within the global array compelling. However, this view is not entirely new and research on the development and the origins of perception in infancy, inspired by Gibson's ecological perspective, also supports their claims. The inclusion of this developmental research will strengthen and challenge some of Stoffregen & Bardy's views.

Object identity, the apprehension that two glimpses refer to the same object, is offered as an example of combining generality, mathematics, and evolution. We argue that it applies to glimpses in time (apparent motion), modality (ventriloquism), and space (Gestalt grouping); that it has a mathematically elegant solution of nested geometries (Euclidean, Similarity, Affine, Projective, Topology); and that it is evolutionarily sound despite our Euclidean world. [Shepard]

Top-down dynamical models of cognitive processes, such as the one presented by Thelen et al., are important pieces in understanding the development of cognitive abilities in humans and biological organisms. Unlike standard symbolic computational approaches to cognition, such dynamical models offer the hope that they can be connected with more bottom-up, neurologically inspired dynamical models to provide a complete view of cognition at all levels. We raise some questions about the details of their simulation and about potential limitations of top-down dynamical models.