This paper seeks to explain learning by examining five theories of learning—conceptual analysis, behavioural, constructivist, computational and connectionist. The first two are found wanting and rejected. Piaget's constructivist theory offers a general explanatory framework (assimilation and accommodation) but fails to provide an adequate account of the empirical mechanisms of learning. Two theories from cognitive science offering rival explanations of learning are finally considered; it is argued that the brain is not like a computer so the computational model is rejected in (...) favour of a neurally‐based connectionist model of learning. (shrink)

Dynamic systems theory offers conceptual andmathematical tools for describing the performance ofneural systems at very different levels oforganization. Three aspects of the dynamic paradigmare discussed, namely neural rhythms, neural andmental development, and macroscopic brain theories andmodels.

Psychoneural reduction is under attack again, only this time from a former ally: cognitive neuroscience. It has become popular to think of the brain as a complex system whose theoretically important properties emerge from dynamic, non-linear interactions between its component parts. ``Emergence'' is supposed to replace reduction: the latter is thought to be incapable of explaining the brain qua complex system. Rather than engage this issue at the level of theories of reduction versus theories of emergence, I here emphasize a (...) role that reductionism plays – and will continue to play – even if neuroscience adopts this ``complex systems'' view. In detailed investigations into the function of complex neural circuits, certain questions can only be addressed by moving down levels and scales. This role for reduction also finds a place for approaches that dominate mainstream neuroscience, like the widespread use of experimental techniques and theories from molecular biology and biochemistry. These are difficult to reconcile with the anti-reductionist sentiments of the ``complex systems'' branch of cognitive neuroscience. (shrink)

Artificial Cells, , Artificial Ecologies, Artificial Intelligence, Bio-Inspired Hardware Systems, Computational Autopoiesis, Computational Biology, Computational Embryology, Computational Evolution, Morphogenesis, Cyborgization, Digital Evolution, Evolvable Hardware, Cyborgs, Mathematical Biology, Nanotechnology, Posthuman, Transhuman.