A sequential, hierarchical stage model of empathy can account for a comprehensive range of empathic behaviors. We provide an illustrative table, “Stages of Empathy,” to demonstrate how increasingly complex empathic behaviors emerge at each stage, beginning with the infant's “automatic empathy” and ending with the advanced adult's “coconstruction of empathetic reality.”

Several lines of evidence indicate that rapid target-aiming movements, involving both the eyes and hand, can be biased by the visual context in which the movements are performed. Some of these contextual influences carry-over from trial to trial. This research indicates that dissociation between the dorsal and ventral systems based on speed, conscious awareness, and frame of reference is far from clear.

Using robots that operate in the real world as opposed to computer simulations of animal behavior is a form of modeling that may provide some biological insights. However, since engineering principles and materials differ significantly from those used in biology, one should be extremely cautious in interpreting robot biomimicry as providing an explanation of biological mechanisms.

(1) The main issue with regard to modal and amodal completion is not which phenomena are cognitive, and which perceptual. At the level of the animal, both are visuo-cognitive. At the level of visual processing, however, we need to dissect the different functional effects of these kinds of completion. (2) Resonant binding between distributed cortical areas may play a role in perceptual completion, but evidence is needed.

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.

We support Webb's insights into the potential benefits of using robotic modeling to better understand biological behavior. We defend the major points put forward by Webb by presenting a specific case study in which robotic modeling of mobile ball catching has helped refine and clarify aspects of our understanding of biological interceptive behavior.

We propose that Bloom's focus on cognitive factors involved in word learning still lacks a broader perspective. We emphasize the crucial relevance of working memory in learning elements of language. Specifically, we demonstrate through our data that in impaired populations knowledge of some linguistic elements can be dissociated according to the subcomponent of working memory (visual or verbal) involved in a task. Further, although Bloom's concentration on theory of mind as a precondition for word learning is certainly correct, theory of mind being a necessary condition does not make it a sufficient one. On the basis of our studies we point out the importance of a theory of mind related goal preference in acquiring spatial language. In general, we claim that more specific cognitive preferences and constraints should be outlined in detail for the preconditions of acquiring linguistic elements.

Although Bloom gives more credit to social cognition (mind reading) than do most other theorists of word learning, he does not go far enough. He still relies fundamentally on a learning process of association (or mapping), neglecting the joint attentional and cultural learning skills from which linguistic communication emerges at one year of age.

Bloom provides a detailed account of children's word learning and comprehension. Yet, this book falls short of explaining the developmental process of word learning. The studies reviewed do not explain how infants begin to map words onto objects or the environment's facilitative role. Researchers must describe how several factors interact and explain the relative importance of each during the development of word learning.

Bloom's book can be viewed as a long argument for an anti-Whorfian conclusion. According to Bloom, word learning is usually a process of mapping new words to pre-existing concepts. But an exception to this generalization – the learning of words from linguistic context – poses a problem for Bloom's anti-Whorfian argument.

We emphasize the feature of Webb's presentation that bears most directly on contemporary research with real animals. Many neuroscience modelers erroneously conclude that a model that performs like an animal must have achieved this goal through processes analogous with those used by the animal. A simulation failure justifies rejecting a model, but success does not justify acceptance. However, an important benefit of models, successful or otherwise, is to stimulate new research.

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.

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.

Heyes's (1998) triangulation approach to distinguishing a “theory” of mind (ToM) from a “theory” of behavior (ToB) in chimpanzees fails. The ToB theorist can appeal to the explicit training sessions and analogical reasoning to explain/predict the chimpanzees' behaviors. An alternative triangulation experiment is sketched, demonstrating how the removal of such training sessions paves the way toward solving the distinguishability problem.

In order to account for how children can generalize words beyond a very limited set of labeled examples, Bloom's proposal of word learning requires two extensions: a better understanding of the “general learning and memory abilities” involved, and a principled framework for integrating multiple conflicting constraints on word meaning. We propose a framework based on Bayesian statistical inference that meets both of those needs.

There is no best scientific model of anything; there are only models more or less good for different purposes. Thus, there is no general answer to the question of whether one should model biological behavior using computer simulations or robots. It all depends on what one wants to learn. This is not a question about models, but about scientific goals.

It appears there is general support amongst the commentaries for the potential usefulness of biorobots as models, with some caveats. These include the issue that not all areas of biology have been addressed by this methodology (and perhaps some cannot be?); and that other methodologies may sometimes be more useful. Which dimensions of biorobotic (or other models) are considered important varies with the goals of the investigator. These goals are also an essential part of the “modelling relationship.”

Heyes's (1998) skepticism about theory of mind (ToM) in nonhuman primates exploits the idea of a strong and unified theory of mind in humans based on an unanalyzed category of mental state. It also exploits narrow debates about crucial observations and experiments while neglecting wider evolutionary trends. I argue against both exploitations.

Biology can inspire robotic simulations of behavior and thus advance robotics, but the validity of drawing conclusions about real behavior from robotic models is questionable. Robotic models, particularly of learning, do not account, for example, for (a) exaptation: co-opting of previously evolved functions for new behavior, (b) learning through observation, (c) complex biological reality, or (d) limits on computational capacity.

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.