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Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 149-160). Available also in a digital version from Dissertation Abstracts.

This manuscript-based thesis explores the relationship between learning and development. The first manuscript reviews the important empirical regularities identified in human discrimination shift learning, including a qualitative age-related change in performance observed in childhood. Leading theoretical accounts of the empirical data are discussed, suggesting that none provides a comprehensive interpretation. The manuscript presents the novel, spontaneous overtraining interpretation. It hypothesizes that age-related changes in human shift learning stem from differences in amount of processing. Successful neural network simulations of the reversal and nonreversal shift tasks and of the optional shift task are reported as tests of the hypothesis. / The second manuscript reports simulations of additional discrimination shift tasks. These are the intradimensional and extradimensional shift tasks, in which novel stimuli are introduced in the relearning phase. Preschoolers and adults exhibit the same pattern of behavior in this variant of shift learning. Simulation results show that the spontaneous overtraining hypothesis captures this effect. / The third chapter reports an empirical validation of the shift learning model. If the shift learning performance of adults is a consequence of more extensive processing, it follows that adults in whom such processing is prevented should perform as preschoolers. Sixty adults took part in a shift learning experiment with a Brown-Peterson task as a cognitive load. Results mirror those observed with preschoolers. As a control, 40 adults performed the shift learning experiment without the cognitive load. These results replicate the typical adult performance. Overall, these experiments lend additional support to the model developed in Manuscript 1. / The final manuscript is a theoretical discussion of the relationship between learning and development. Two classes of neural networks are discussed, and their underlying assumptions about learning and development are highlighted. These are static architecture and generative architecture networks. It is argued that only generative algorithms, such as used in the shift learning simulations, qualify as developmental models. Both classes of networks are further contrasted with respect to innateness. The comparison suggests that only generative networks can acquire genuinely new representations. The manuscript proposes a novel formulation of Piaget's constructivism from the generative neural network perspective.