The Miyashita experiments are very interesting and the results should be examined from a viewpoint of attractor dynamics. Amit's target article shows a path toward realistic modeling by artificial neural networks (ANN), but it is not necessarily the only one. I introduce another model that can explain a substantial part of the empirical observations and makes an interesting prediction. This model consists of such units that have nonmonotonic input-output characteristics with local inhibition neurons.

Event-related potentials (ERPs) – neglected almost entirely by Wright & Liley – allow objective investigation of information processing in the brain. The application of chaos theory to such an analysis broadens this possibility. Through the use of the point correlation dimension (PD2) accurate dimensional analysis of different Event-Related Potential components such as the P3 wave is possible.

Persistent activity can be the product of mechanisms other than attractor reverberations. The single-unit data presented by Amit cannot discriminate between the different mechanisms. In fact, single-unit data do not appear to be adequate for testing neural network models.

Wilkins and Wakefield's hypothesis that language is fundamentally a cognitive rather than cominunicational adaptation is reasonable, but there are flaws in their anatomical and fossil evidence. Their analysis of reorganization also needs clarification. Finally, the origin of language ability must have occurred with australopithecine rather than habiline adaptations on entry into the novel hominid adaptive zone.

The psychology of emotions illuminates the questions of intentional capacities raised by Barresi & Moore (B&M). Complex emotions require the development of a sense of self and are based on social comparisons between mainly imagined objects. The fourth level in B&M's framework requires something like a constructive agent rather than a mental agent.