In agreement with the target article, we would like to point out a few aspects related to embodiment which further support the position of biorobotics. We argue that, especially when complex systems are considered, modeling through a physical implementation can provide hints to comprehend the whole picture behind the specific set of experimental data.

Webb compares robots to other behavior models in a novel space, but neglects significant portions of this space. The article's analysis of models would have been strengthened by including a broader coverage of physically embodied models and of models of social behavior. Nevertheless, Webb is correct to claim that robot construction will contribute to understanding behavior.

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.

From the perspective of biological cybernetics, “real world” robots have no fundamental advantage over computer simulations when used as models for biological behavior. They can even weaken biological relevance. From an engineering point of view, however, robots can benefit from solutions found in biological systems. We emphasize the importance of this distinction and give examples for artificial systems based on insect biology.

While robotics has benefited from inspiration gained from biology, the opposite is not the case: there are few if any cases in which robotic models have lead to genuine insight into biology. We analyze the reasons why biorobotics has been essentially a one-way street. We argue that the development of better tools is essential for progress in this field.

To be plausible, biorobots will need to build themselves. Such autopoietic systems will be autonomous, active learners whose functional architecture is a joint product of factors supplied by the designer and factors learned from encountering an environment. Creating such biorobots will require appropriate theories of cognition, learning, and evolution, all of which are available.

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.

Webb's scheme for classifying behavioral models is applicable to a wide range of theories and simulations, nonrobotic as well as robotic. It is suggested that a meta-analysis of existing models, characterized according to the proposed scheme, could identify regions of the seven-dimensional modelling space that are particularly likely to lead to new insights in understanding behavior.

Webb has articulated a clear, multi-dimensional framework for discussing simulation models and modelling strategies. This framework will likely co-evolve with modelling. As such, it will be important to continue to clarify these dimensions and perhaps add to them. I discuss the dimension of generality and suggest that a dimension of integrativeness may also be needed.

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.

Any discussion comparing different models with respect to their quality qua models must presuppose a notion of model, that is, what it is to be a model. While Webb provides seven criteria to assess the quality of various proposed biorobotic models, she does not clarify the very notion of “model of animal behavior” itself.

Biorobotics research should not only target “realistic” models of living systems and be judged exclusively from that perspective. It should pay just as much attention to formal models and artificial systems. They allow the examination of assumptions which do not necessarily hold for living systems, but precisely therein lies their value. They generate insight by enabling a comparison between the artificial and the real.

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.

Biological models are useful not only because they can simulate biological behaviors, but because they may shed light on the inner workings of complex biological structures and functions as deduced by top-down and/or bottom-up reasoning. Beyond the stylistic appeal of specific implementation methods, a model should be appraised according to its ability to bring out the underlying organizing and operating principles – which are truly the model's heart and soul.

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.”

Normal children learn tens of thousands of words, and do so quickly and efficiently, often in highly impoverished environments. In How Children Learn the Meanings of Words, I argue that word learning is the product of certain cognitive and linguistic abilities that include the ability to acquire concepts, an appreciation of syntactic cues to meaning, and a rich understanding of the mental states of other people. These capacities are powerful, early emerging, and to some extent uniquely human, but they are not special to word learning. This proposal is an alternative to the view that word learning is the result of simple associative learning mechanisms, and it rejects as well the notion that children possess constraints, either innate or learned, that are specifically earmarked for word learning. This theory is extended to account for how children learn names for objects, substances, and abstract entities, pronouns and proper names, verbs, determiners, prepositions, and number words. Several related topics are also discussed, including naïve essentialism, children's understanding of representational art, the nature of numerical and spatial reasoning, and the role of words in the shaping of mental life.

Though Bloom makes a good case that learning content-word meanings requires no task-specific apparatus, he does not seriously address problems inherent in learning the meanings of functional items. Evidence from creole languages suggests that the latter process presupposes at least some task-specific mechanisms, perhaps including a list of the limited number of semantic distinctions that can be expressed via functional items, as well as default systems that may operate in cases of impoverished input.

This review proposes that Bloom's linkage of word meaning with more general cognitive capacities could be extended through examination of the social contexts in which children learn. Specifically, the child's developing theory of mind can be viewed as part of the process by which children learn word meanings through engagement in social interactions that facilitate both language and strategic behaviours.

Bloom argues that concepts depend on psychological essentialism. He rejects the proposal that concepts are based on perceptual similarity spaces because it cannot account for how we handle new properties and does not fit with our intuitions about essences. I argue that by using a broader notion of similarity space, it is possible to explain these features of concepts.

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.