I suggest that neurophysiological data, usually interpreted in cognitivist terms, is actually more supportive of dynamic frameworks such as that of Thelen et al. I outline a model of embodied action used to interpret neural data from frontal and parietal cortex, and suggest that it bears strong similarity to the framework described in the target article.

The notion of internalization put forth by Roger Shepard continues to be appealing and challenging. He suggests that we have internalized, during our evolutionary development, environmental regularities, or constraints. Internalization solves one of the hardest problems of perceptual psychology: the underspecification problem. That is the problem of how well-defined perceptual experience is generated from the often ambiguous and incomplete sensory stimulation. Yet, the notion of internalization creates new problems that may outweigh the solution of the underspecification problem. To support this claim, I first examine the concept of internalization, breaking it down into several distinct interpretations. These range from well-resolved dynamic regularities to ill-resolved statistical regularities. As a function of the interpretation the researcher selects, an empirical test of the internalization hypothesis may be straightforward or it may become virtually impossible. I then attempt to cover the range of interpretations by drawing on examples from different domains of visual event perception. Unfortunately, the experimental tests regarding most candidate regularities, such as gravitational acceleration, fail to support the concept of internalization. This suggests that narrow interpretations of the concept should be given up in favor of more abstract interpretations. However, the latter are not easily amenable to empirical testing. There is nonetheless a way to test these abstract interpretations by contrasting internalization with the opposite concept: externalization of body dynamics. I summarize evidence for such a projection of body constraints onto external objects. Based on the combined evidence of well-resolved and ill- resolved regularities, the value of the notion of internalization has to be reassessed.

Using the example of the difficulties which emerge when trying to model complex behaviors – such as emotional expression – that result from stochastic interactions between different components, we argue that biorobotics may well describe one possible evolution of certain features of a biological system, but cannot pretend to be a simulation of the whole behavior of the system.

Experimental evidence and mathematical/computational models show that in many cases chaotic, nonregular oscillations are adequate to describe the dynamical behaviour of neural systems. Further work is needed to understand the meaning of this dynamical regime for modelling information processing in the brain.

Tsuda examines the potential contribution of nonlinear dynamical systems, with many degrees of freedom, to understanding brain function. We offer suggestions concerning symmetry and transients to strengthen the physiological motivation and theoretical consistency of this novel research direction: Symmetry plays a fundamental role, theoretically and in relation to real brains. We also highlight a distinction between chaotic “transience” and “itineracy.”

Hertwig and Ortmann suggest methodological practices from economics (script enactment, repeated measures, performance based payments, and absence of deception) for psychology. Such prescriptive methodologies may be unrepresentative of real world behaviors because people are not: always behaving with complete information, monetarily rewarded for important activities, repeating tasks to perfection, aware of all contributing variables. These proscriptions, while useful in economics, may obfuscate important psychological phenomena.

Shepard has supposed that the mind is stocked with innate knowledge of the world and that this knowledge figures prominently in the way we see the world. According to him, this internal knowledge is the legacy of a process of internalization; a process of natural selection over the evolutionary history of the species. Shepard has developed his proposal most fully in his analysis of the relation between kinematic geometry and the shape of the motion path in apparent motion displays. We argue that Shepard has made a case for applying the principles of kinematic geometry to the perception of motion, but that he has not made the case for injecting these principles into the mind of the percipient. We offer a more modest interpretation of his important findings: that kinematic geometry may be a model of apparent motion. Inasmuch as our recommended interpretation does not lodge geometry in the mind of the percipient, the motivation of positing internalization, a process that moves kinematic geometry into the mind, is obviated. In our conclusion, we suggest that cognitive psychologists, in their embrace of internal mental universals and internalization may have been seduced by the siren call of metaphor.

Stoffregen & Bardy propose an absolutist transmodal array structure model, intended to displace models of specification in all existing perceptual theories. Absolute specification of world structure in array structures, either unimodal or transmodal, may not be provable, but might be falsifiable. Absolute specification, moreover, may not be a necessary postulate in an ecological approach to understanding perception-action.

Much confusion exists within the robotics and the biology communities regarding the definition of biorobotics and the aims and strategies that characterize this approach. Not even the basic criteria for identifying biorobotic research are being applied consistently. Barbara Webb has taken a crucial step towards setting a common ground from which biorobotic systems can be described, analyzed, and compared.

Thelen et al.'s model of A-not-B performance is based on behavioral observations obtained with a paradigm markedly different from A-not-B. Central components of the model are not central to A-not-B performance. All data presented fit a simpler model, which specifies that the key abilities for success on A-not-B are working memory and inhibition. Intention and action can be dissociated in infants and adults.

The question “Are ‘biorobots' good models of biological behaviour?” can be seen as a specific instance of a more general question about the relation between computer programs and models, between models and theories, and between theories and reality. This commentary develops a personal view of these relations, from an antirealism perspective. Programs, models, theories and reality are separate and distinct entities which may converge in particular cases but should never be confused.

Although the model proposed by Thelen and co-authors provides a detailed explanation for the processes underlying reaching, many aspects of it are highly speculative. One of the reasons for this is our lack of knowledge about transformation of a hand movement plan into joint movements. The leading joint hypothesis (LJH) allows us to partially fill in this gap. The LJH offers a possible explanation for the formation of movement and how it may be represented in memory. Our explanation converges with the dynamic model described in the target article.

This commentary extends Hertwig & Ortmann's analysis by asking how stricter model selection conventions can facilitate the accumulation of information from experimental studies. In many cases researchers are currently motivated to summarize their data with ambiguous and/or multi parameter models. A “generality first” convention can help eliminate this problem.

Stoffregen & Bardy suggest that the global array provides the specification that is lacking when senses are considered in isolation. This seems to beg the question of the minimum number of senses in a global array. Individuals with sensory loss manage with fewer senses, and humans manage with fewer than electric fish; so specification, if it exists, cannot require all possible senses.

Roger Shepard's proposals and supporting experiments concerning evolutionary internalized regularities have been very influential in the study of vision and in other areas of psychology and cognitive science. This paper examines issues concerning the need, nature, explanatory role, and justification for postulating such internalized constraints. In particular, I seek further clarification from Shepard on how best to understand his claim that principles of kinematic geometry underlie phenomena of motion perception.

My primary focus is on the ecological validity of Shepard's kinematic constraint in the context of ordinary motion perception. First, I explore the analogy Shepard draws between internalized circadian rhythms and the supposed internalization of kinematic geometry. Next, questions are raised about how to interpret and justify applying results from his own and others' experimental studies of apparent motion to more everyday cases of motion perception in richer environments. Finally, some difficulties with Shepard's account of the evolutionary development of his kinematic constraint are considered.

Single cell receptive field dynamics characterized by highly complicated spatio-temporal activity distributions observable during sensory information processing transforms into much simpler spatio-temporal activity pattern at a population level, indicating a qualitative transformational step of time-variant processing from microscopic to mesoscopic levels. As these dynamics are subject to significant modifications during learning, dynamic information processing is in a permanent state of use-dependent fluctuations.

Experimental psychologists in the learning tradition stress the importance of three of the authors' four key variables of experimental design. We review research investigating the roles played by these variables in studies of choice from our laboratory. Supporting the authors' claims, these studies show that the effects of these variables are not fixed and should not be taken for granted.

Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models.

Although the advantage of biorobotics over traditional modelling tools is not always evident from the studies on animal search addressed in the target article, this commentary argues that testing different robotic architectures and specific biological organisms in structured search spaces, where environmental constraints matter, might prove one of the most promising research strategies in comparative cognition.

Is there any ecological purpose in assuming that perception for action exists only through a global array of energy? Unlike Stoffregen & Bardy, who assume that behavior consists of movements, we would argue that behavior consists of a stable coupling between perception and action achieved through experience in an adaptive context. Determining target position in an aiming manual task and temporal control of impact movement illustrate that patterns of energy used for action are task-dependent.