Quantum probability theory offers a viable alternative to classical probability, although there are some ambiguities inherent in transferring the quantum formalism to a less determined realm. A number of physicists are now looking at the applicability of quantum ideas to the assessment of physics learning, an area particularly suited to quantum probability ideas.

According to Hurford, PREDICATE (x) is correlated with deictic object variables during event perception. This claim is inconsistent with some core literature on the perception of motion events. We point out that the perception of events involves the activation of the modal properties and amodal properties of underlying event structure, for which Hurford's target article fails to account.

A slight modification of Webb's diagrammatic representation of the dimensions for describing models is proposed which extends it to cover a range of theoretical models as well as material models. It is also argued that beyond a certain level robotic simulations could offer a number of real advantages over computer simulations of organisms interacting with their environment.

The history of the natural sciences repeatedly shows that the unification of a higher level theory with a lower level theory by reduction does not eliminate the need for the higher level theory nor preclude its further development, leading to changes in the understanding of the lower level. The radical neuron doctrine proposes that the future science of psychology or linguistics will derive principally from the evolution of understanding at the neural level and not from current theories based on the observation of behavior. It is far more likely that the two bodies of theory will coevolve in semiautonomous fashion.

A consideration of underlying neural dynamics and the evolutionary process producing cognitive agents should complement the development of dynamical models of behavior. The geometrical aspects of dynamical systems theory which make it useful in the description of systems acting in an environment are less useful in understanding agents interacting with a range of environments, and may call for supplementation by evolutionary insights.