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Developmental neuropsychology is concerned with uncovering the underlying basis of developmental disorders such as specific language impairment (SLI), developmental dyslexia, and autistic disorder. Twin and family studies indicate that genetic influences play an important part in the aeriology of all of these disorders, yet progress in identifying genes has been slow. One way forward is to cut loose from conventional clinical criteria for diagnosing disorders and to focus instead on measures of underlying cognitive mechanisms. Psychology can inform generics by clarifying what the key dimensions are for heritable phenotypes. However, it is not a one-way street. By using genetically informative designs, one can gain insights about causal relationships between different cognitive deficits. For instance, it has been suggested that low-level auditory deficits cause phonological problems in SLI. However, a twin study showed that, although both types of deficit occur in SLI, they have quite different origins, with environmental factors more important for auditory deficit, and genes more important for deficient phonological short-term memory. Another study found that morphosyntactic deficits in SLI are also highly heritable, but have different genetic origins from impairments of phonological short-term memory. A genetic perspective shows that a search for the underlying cause of developmental disorders may be misguided, because they are complex and heterogeneous and are associated with multiple risk factors that only cause serious disability when they occur in combination.

This response discusses progressive modularity, the breadth of the notion of domain; whether Beyond modularity goes beyond Fodor, misrepresents Piaget, and gives sufficient prominence to the external environment and to the early phases of learning; whether the representational redescription (RR) framework has been fully extended to intentional psychology and notational competence; the problems of the implicit/explicit dichotomy, of specifying mechanisms; the role of socio-cultural influences on representational change, connectionist models, and the use of the term 'innate.'

Two assertions of Halford et al. are critiqued: their claim of priority in relational complexity analysis and the sufficiency for cognitive development of their relational-complexity analysis of tasks. Critical discussion of concrete task analyses (i.e., the relational complexity of proportionality problems, of balance settle problems, and the Tower of Hanoi) serves, by way of counterexamples, to highlight problems in their method.

Relational complexity theory has important virtues, but the present model omits key aspects and evidence. In contrast, skill theory specifies (1) a detailed series of developmental changes in relational complexity from birth to age 30, (2) processes of interaction of content and structure that produce variability in complexity, (3) the role of cortical development, and (4) empirical criteria for complexity levels, including developmental discontinuities. Many findings support these specifications.

Working memory limits are best defined ill terms of the complexity of the relations that can be processed in parallel. Complexity is defined as the number of related dimensions or sources of variation. A unary relation has one argument and one source of variation; its argument can be instantiated in only one way at a time. A binary relation has two arguments, two sources of variation, and two instantiations, and so on. Dimensionality is related to the number of chunks, because both attributes on dimensions and chunks are independent units of information of arbitrary size. Studies of working memory limits suggest that there is a soft limit corresponding to the parallel processing of one quaternary relation. More complex concepts are processed by 'segmentation' or 'conceptual chunking.' In segmentation, tasks are broken into components that do not exceed processing capacity and can be processed serially. In conceptual chunking, representations are 'collapsed' to reduce their dimensionality and hence their processing load, but at the cost of making some relational information inaccessible. Neural net models of relational representations show that relations with more arguments have a higher computational cost that coincides with experimental findings on higher processing loads in humans. Relational complexity is related to processing load in reasoning and sentence comprehension and can distinguish between the capacities of higher species. The complexity of relations processed by children increases with age. Implications for neural net models and theories of cognition and cognitive development are discussed. Keywords: capacity limits: chunking; cognitive development; comparative psychology; complexity; neural nets; relations; representations; resources; working memory

Developmental psychology should play an essential constraining role in developmental cognitive neuroscience. Theories of neural development must account explicitly for the early emergence of knowledge and abilities in infants and young children documented in developmental research. Especially in need of explanation at the neural level is the early emergence of meta-representation.

The contribution of Karmiloff-Smith's book lies not in its bearing on the debate over generality and specificity, but rather in its message that development does not stop with the first rudimentary manifestations of cognitive competency. Explicit knowing is at least as significant as knowing in more implicit forms., A theory on the cognitive development of humans through processes occuring within specific domains such as mathematical capability and language has opened up questions on what type of knowledge is developed. The theory calls for further studies on how implicit or innate knowledge develops into explicit knwoledge. In addition, developmental studies should also focus on how children learn to build theories on how they think apart from the act of pondering on these theories.

Karmiloff-Smith's landmark work decisively addresses central problems in developmental psychology and cognitive science. Her most valuable contributions include specifying how nativist and constructivist views of development are complementary, how development can be both domain-specific and domain-general, and how we are special. Her solution to the latter problem correctly focuses on the development of metacognition. Two questions about Karmiloff-Smith's theory are raised: Can it explain its own development? Can it enhance the acquisition of expertise?

Karmiloff-Smith's developmental perspective forces us to recognize that there are many levels at which knowledge can be represented. We first offer empirical support for a distinction made on theoretical grounds between two such levels. We then argue that 'redescription' onto a new level need not await success (as Karmiloff-Smith proposes), and that this modification of the theory has important implications for the role redescription plays in development.

An explanation for the representational redescription theory of cognitive development that is derived from analysis of artificial intelligence systems is presented. Studies of artifical intelligence have revealed learning processes which only requires that learning proceed as solutions to problems by generating predictions for the consequences of actions on a system. This ability to emulate the results to actions can be used as an analogy for the role of imagination in human thinking.