How useful are bio-developmental approaches for understanding how cognitive capabilities are acquired? One bio-developmental hypothesis is that human cognition unfolds with maturation as a massive collection of adaptive cognitive “capabilities” expressing pre-structured genetic programs. But the seeming plasticity of human cognition argues against simple formulations of innately-specified anatomical & functional processing system composed of specialized computational modules. One alternative is an architecture using domain-specific predispositions and general learning mechanisms to construct modules from interactions. This lets them emerge and unfold in a self- organized fashion as part of developmental experience. The result is a more dynamic, complex cognitive architecture explaining such things as the drive for sensorimotor control in infants, which is combines the generation of exploratory movements constrained by the interaction of ability and environment followed by the selection and maintenance of adaptive movement patterns (Schlesinger et al. 2000). Such findings are consistent with a view that ontogenetic processes are co-important (and co-dependent) with gene- based evolutionary processes for behavior and cognition.
That intelligence is a form of information processing and that the framework of modern digital computers provides pretty much all that is needed for representing and processing information for doing AI are two of the most foundational of such assumptions. Turing (1950) explicitly articulated this idea in the late 1940s, and later Newell and Simon (1976) proposed the physical symbol system hypothesis (PSSH) as a newer form of the same set of intuitions about the relation between computation and thinking. In this tradition, the computational approach is not just one way of making intelligent systems, but representing and processing information within the computational framework is necessary for intelligence as a process, wherever it is implemented. The language of thought (LOT) hypothesis, of which Fodor (1975) has given the most well-known exposition, is a variant of the computational hypothesis in AI. LOT holds that underlying thinking is a medium that has the properties of formal symbolic languages that we are familiar with in computer science.
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After providing some essential background information on robotics and developmental psychology, the book looks in detail at how developmental robotics models and experiments have attempted to realize a range of behavioral and cognitive capabilities. The examples in these chapters were chosen because of their direct correspondence with specific issues in child psychology research; each chapter begins with a concise and accessible overview of relevant empirical and theoretical findings in developmental psychology. The chapters cover intrinsic motivation and curiosity; motor development, examining both manipulation and locomotion; perceptual development, including face recognition and perception of space; social learning, emphasizing such phenomena as joint attention and cooperation; language, from phonetic babbling to syntactic processing; and abstract knowledge, including models of number learning and reasoning strategies. Boxed text offers technical and methodological details for both psychology and robotics experiments.