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Fedforward neural network models of cognitive development are reviewed within the framework of a functional distinction between learning and development. This analysis suggests that static architecture networks implement a learning theory, whereas generative architecture networks combine learning and development. Both types of networks are then evaluated m terms of genetic costs. Within a levels-of-innateness framework, generative architectures are viewed as more plausible than static ones. Static architecture networks appear to implement a form of nativistic elicitation.

The importance of discrimination shifts to learning and developmental psychology is highlighted. Basic tasks used in continuous and total change paradigms are presented, and major theoretical accounts are briefly reviewed. The lack of a general and comprehensive interpretation of human shift learning is identified, and a recent model based on neural network research is described. This model suggests that human adult performance in discrimination shifts differs from preschool performance because of a process called spontaneous overtraining. This hypothesis has been previously used in neural network simulations to successfully capture developmental regularities in continuous discrimination shifts (e.g., reversal and nonreversal shifts). In the present paper, new simulations using this model are applied to total change discrimination shifts (e.g., intradimensional and extradimensional shifts). Several developmental regularities are successfully captured by the networks. The contribution of the spontaneous overtraining hypothesis is discussed.

This study investigated maternal prenatal docosahexaenoic acid (DHA) intake and infant cognitive development at 22 months. Estimates for second- and third-trimester maternal DHA intake levels were obtained using a comprehensive Food Frequency Questionnaire. Infants (n = 67) were assessed at 22 months on a novel object search task. Mothers' DHA intake levels were divided into high or low groups, with analyses revealing a significant positive effect of third-trimester DHA on object search task performance. The third trimester appears to be a critical time for ensuring adequate maternal DHA levels to facilitate optimum cognitive development in late infancy.

This paper examines the relative merits of looking time and pupil diameter measures in the study of early cognitive abilities of infants. Ten-month-old infants took part in a modified version of the classic drawbridge experiment used to study object permanence (Baillargeon, Spelke, & Wasserman, 1985). The study involved a factorial design where angle of rotation and presence or absence of an object were crossed. Looking time results are consistent with previous work and could suggest object permanence if one ignored data from all cells of the factorial design. When all cells are considered, the data rather suggest a perceptual interpretation. Dynamic changes in pupil diameter uniquely support this interpretation, illustrating which aspects of events (and when) infants primarily respond to. Overall, the results fail to support object permanence in 10-month-olds, and pupil dilation provides a much finer-grained interpretation of infants' information processing, relative to looking time. (Contains 5 figures and 3 footnotes.)

The violation-of-expectation (VOE) paradigm and related methods are the main tools used to study high-level cognition in preverbal infants. Infants' differential looking to conceptually implausible/impossible events has been used as an index of early cognitive competence in many areas, including object knowledge, physics, language, and number. However, an event's plausibility is commonly confounded with its perceptual novelty or familiarity, leading to a variety of interpretations for looking time data (Bogartz, Shinskey & Speaker, 1997). This illustrative study demonstrates the value of factorial designs, in which perceptual (novelty-familiarity) and conceptual (possible-impossible) variables are independently and jointly explored. It also introduces pupil dilation as a viable and complementary dependent measure to study infant cognition. We show that pupil data can assist in the interpretation of otherwise equivocal looking time data. The discussion focuses on methodological considerations in infancy research.

This paper presents autoassociator neural networks. A first section reviews the architecture of these models, common learning rules, and presents sample simulations to illustrate their abilities. In a second section, the ability of these models to account for learning phenomena such as habituation is reviewed. The contribution of these networks to discussions about infant cognition is highlighted. A new, modular approach is presented in a third section. In the discussion, a role for these learning models in a broader developmental framework is proposed.

Presents a theoretical account of human shift learning with the use of neural network tools. Details how simulations using the cascade-correlation algorithm which show that networks can capture the regularities of the discrimination shift literature better than existing psychological theories. Suggests that human developmental differences in shift learning arise from spontaneous overtraining by older participants, consistent with overtraining literature. (LBT)

In this response, we consider four main issues arising from the commentaries to the target article. These include further details of the theory of interactive specialization, the relationship between neuroconstructivism and selectionism, the implications of neuroconstructivism for the notion of representation, and the role of genetics in theories of development. We conclude by stressing the importance of multidisciplinary approaches in the future study of cognitive development and by identifying the directions in which neuroconstructivism can expand in the Twenty-first Century.

Neuroconstructivism: How the Brain Constructs Cognition proposes a unifying framework for the study of cognitive development that brings together (1) constructivism (which views development as the progressive elaboration of increasingly complex structures), (2) cognitive neuroscience (which aims to understand the neural mechanisms underlying behavior), and (3) computational modeling (which proposes formal and explicit specifications of information processing). The guiding principle of our approach is context dependence, within and (in contrast to Marr [1982]) between levels of organization. We propose that three mechanisms guide the emergence of representations: competition, cooperation, and chronotopy; which themselves allow for two central processes: proactivity and progressive specialization. We suggest that the main outcome of development is partial representations, distributed across distinct functional circuits. This framework is derived by examining development at the level of single neurons, brain systems, and whole organisms. We use the terms encellment, embrainment, and embodiment to describe the higher-level contextual influences that act at each of these levels of organization. To illustrate these mechanisms in operation we provide case studies in early visual perception, infant habituation, phonological development, and object representations in infancy. Three further case studies are concerned with interactions between levels of explanation: social development, atypical development and within that, developmental dyslexia. We conclude that cognitive development arises from a dynamic, contextual change in embodied neural structures leading to partial representations across multiple brain regions and timescales, in response to proactively specified physical and social environment. Keywords: brain; cognition; development; constructivism; embodiment

This paper explores novel predictions from the spontaneous overtraining interpretation of human discrimination shift learning (Sirois & Shultz, 1998a). Results from six experiments where adults perform a discrimination shift task with or without a cognitive distractor are reported. In three experiments with a concurrent distractor task (Experiments 1A, 2A, and 3A), performance of adults is comparable to what would be expected from preschoolers performing only the learning task. These adults show no dimensional transfer from initial learning, unless new attributes are introduced in shift learning. On the same tasks without a cognitive load (Experiments 1B, 2B, and 3B), performance is typical of normal adults. The discussion focuses on the relative ability of competing theoretical models (i.e., levels of processing, attentional mediation, and perceptual differentiation) to account for these data.

A study was conducted to compare the ability of artificial neural networks with existing psychological theories in stimulating age effects in shift learning. A new simulation model that uses cascade-correlation algorithm was applied to the balance scale and discrimination shifts. Results showed that the model is better than leading theories in terms of capturing the regularities of the discrimination shift and suggest that spontaneous overtraining by older participants causes human developmental differences in shift learning.