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We propose that the crucial difference between human cognition and that of other species is the ability to participate with others in collaborative activities with shared goals and intentions: shared intentionality. Participation in such activities requires not only especially powerful forms of intention reading and cultural learning, but also a unique motivation to share psychological states with others and unique forms of cognitive representation for doing so. The result of participating in these activities is species-unique forms of cultural cognition and evolution, enabling everything from the creation and use of linguistic symbols to the construction of social norms and individual beliefs to the establishment of social institutions. In support of this proposal we argue and present evidence that great apes (and some children with autism) understand the basics of intentional action, but they still do not participate in activities involving joint intentions and attention (shared intentionality). Human children's skills of shared intentionality develop gradually during the first 14 months of life as two ontogenetie pathways intertwine: (1) the general ape line of understanding others as animate, goal-directed, and intentional agents; and (2) a species-unique motivation to share emotions, experience, and activities with other persons. The developmental outcome is children's ability to construct dialogic cognitive representations, which enable them to participate in earnest in the collectivity that is human cognition. Keywords: collaboration; cooperation; cultural learning; culture; evolutionary psychology; intentions; shared intentionality; social cognition; social learning; theory of mind; joint attention

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.'

Beyond modularity attempts a synthesis of Fodor's anticonstructivist nativism and Piaget's antinativist constructivism. Contra Fodor, I argue that: (1) the study of cognitive development is essential to cognitive science, (2) the module/central processing dichotomy is too rigid, and (3) the mind does not begin with prespecified modules; rather, development involves a gradual process of 'modularization.' Contra Piaget, I argue that: (1) development rarely involves stagelike domain-general change and (2) domain-specific predispositions give development a small but significant kickstart by focusing the infant's attention on proprietary inputs. Development does not stop at efficient learning. A fundamental aspect of human development ('representational redescription') is the hypothesized process by which information that is in a cognitive system becomes progressively explicit knowledge to that system. Development thus involves two complementary processes of progressive modularization and progressive 'explicitation.' Empirical findings on the child as linguist, physicist, mathematician, psychologist, and notator are discussed in support of the theoretical framework. Each chapter concentrates first on the initial state of the infant mind/brain and on subsequent domain-specific learning in infancy and early childhood. It then goes on to explore data on older children's problem solving and theory building, with particular focus on evolving cognitive flexibility. Emphasis is placed throughout on the status of representations underlying different capacities and on the multiple levels at which knowledge is stored and accessible. Finally, consideration is given to the need for more formal developmental models, and a comparison is made between representational redescription and connectionist simulations of development. In conclusion, I consider what is special about human cognition by speculating on the status of representations underlying the structure of behavior in other species. Keywords: cognitive development; connectionism; domain-specific/domain-general; modularity; nativism/constructivism; other species; phase versus stage; representational redescription

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.

Halford et al. are not redefining capacity in the sense of limit on resources but in the sense of limits on what resources call do. Furthermore, the necessity of using resource theory as a theoretical frame is questionable.

We argue that Halford et al.'s characterisation of relational complexity offers an unadaptive principle in terms of cognitive economy, that its relation with the empirical evidence is highly selective, and that the task behaviours used in support of a multivector processing space are better described by linear serial processes which do not require n-dimensional mappings for their emergence.

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.

Posner & Raichle illustrate how neuroimaging blends profitably with neuropsychology and electrophysiology to advance cognitive theory. Recognizing that there are limitations to each of these techniques, we nonetheless argue that their confluence has fundamentally changed the way cognitive psychologists think about problems of the mind.

Representational redescription (RR) poses a significant challenge to cognitive science; but Karmiloff-Smith underestimates the extent to which some current computational models already engage in RR. Moreover, a large part of the existing challenge is to produce convincing psychological evidence that deserves to be modeled. Finally, task constraints are essential for success in both psychological theorizing and modeling.

Mental models for deductive reasoning fail to consider that logical assumptions are made when the subject has exhausted all possibilities that the conclusion could be found invalid. A mental model formed in this manner would be logical only to the extent no counterexamples can be found. They should also establish the impossibility of any contradicting conclusion. Cognitive reasoning may benefit from research on the logical systems that act during the deductive process.

The use of innate abilities to explain cognitive development fails to provide the mechanism that would enable infants to create or acquire knowledge without input from the environment. This nativist theory of cognition has traditionally avoided explanations by assuming that genes produce phenotypes which provide individuals with the ability to learn with only a little help from environmental influences. In addition, no explanation is provided on how these capabilities come to exist as expressions of genetic material.

Concepts of level of explicitation and of representational redescription are useful in explaining cognitive transformations in several domains. An excellent framework theory results, but its nested specific theories are difficult to assess in the absence of an explicit account of representation. Treatment of the domain of intentional mental states can be straightforwardly strengthened, but the domain of depiction merits reworking. In Beyond modularity (hereafter Modularity), we get an excellent advance in analyzing what internal resources the cognitive systen affords in the furtherance of intellectual transformations.

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?

Developmental theories on the manner through which concepts are learned have traditionally assumed that knowledge is based on observations from which theories are based. However, a comobination of nativist and constructivist theories propose that innate knowledge exists in the individual and that this knowledge develops into higher-order thinking and conceptualization through internal processes of representation. However, this view fails to consider the fact that knowledege is derived primarily on sensory input and by implication is empirical in nature.

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.

Karmiloff-Smith's representational redescription poses some architectural problems. We discuss the implicit/explicit dichotomy and the relation between natural language and the language of thought, arguing that the model addresses how knowledge is used rather than how it is represented in the system.

The notion that cognitive development might be a redescriptive and demodularising process raises two issues: (1) the apparent symmetry between initial state and adult state modularity, and (2) the continuity and temporal logic assumed to link implicit and explicit representations. Beyond modularity (henceforth, Modularity) presents a post-modular picture of mental representations, arguing that modular initial state representations implicit in behavioral mastery and in domain-specific processing undergo successive phases of recasting. This process ultimately brings about the uniquely mensch-like achievement of explicit representation in symbolic thought.

Humphreys et al. have begun a process that could bring coherence to the field of memory research. However, their theory is not sufficiently specific to be very useful in modeling other cognitive tasks.

A taxonomy of memorial data structures reconciles theory and experimental tasks for evaluating memory models efficiently using paradigms such as multiple list learning and the list strength effect. Since the computational scheme eschews experimental verification, its theoretical weakness lies in the improbability that an orderly functional scheme will maintain its integrity within the complex nonlinear dynamics that subserve human cognition.

Tomasello, Kruger and Ratner have developed a theory on human cultural learning which argues for a more prominent role for social factors in learning and development. However, their theory lacks sufficient discussion on the ways humans conceptualize culture, communication and cognition. To develop a credible theory on cultural learning, factors such as semiotically mediated communication and specific cultural practices must be taken into account.

Human learning processes depend upon three subsytems of memory that can be explained in terms of progressivly increasing levels of abstraction and representation. These subsystems, namely, procedural, semantic and episodic, are actually mental processes through which information is retained and represented to create concepts which are in turn used to create the next higher order of concepts. Thus, cognitive development relies upon the processes of representation which has been traditionally referred to as memory.

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.