Evidence from neurophysiological and psychological studies is coming together to shed light on how we represent and recognize objects. This review describes evidence supporting two major hypotheses: the first is that objects are represented in a mosaic-like form in which objects are encoded by combinations of complex, reusable features, rather than two-dimensional templates, or three-dimensional models. The second hypothesis is that transform-invariant representations of objects are learnt through experience, and that this learning is affected by the temporal sequence in which (...) different views of the objects are seen, as well as by their physical appearance. (shrink)

Jeffery et al. propose a non-uniform representation of three-dimensional space during navigation. Fittingly, we recently revealed asymmetries between horizontal and vertical path integration in humans. We agree that representing navigation in more than two dimensions increases computational load and suggest that tendencies to maintain upright head posture may help constrain computational processing, while distorting neural representation of three-dimensional navigation.

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.