The authors present a similarity-based model of induction and categorization in young children (SINC). The model suggests that (a) linguistic labels contribute to the perceived similarity of compared entities and (b) categorization and induction are a function of similarity computed over perceptual information and linguistic labels. The model also predicts young children’s similarity judgment, induction, and categorization performance under different stimuli and task conditions. Predictions of the model were tested and confirmed in 6 experiments, in which 4- to 5-year-olds performed similarity judgment, induction, and categorization tasks using artificial and real labels (Experiments 1–4) and recognition memory tasks (Experiments 5A and 5B). Results corroborate the similarity-based account of young children’s induction and categorization, and they support both qualitative and quantitative predictions of the model. Inductive inference, or extending knowledge from known to novel instances, is ubiquitous in human cognition. For example, if one learned that a particular lion has a certain neurotransmitter in its brain, one would expect another lion also to have this neurotransmitter, even if one did not have factual knowledge of the brain

The authors identify 2 major types of statistical data from which semantic representations can be learned. These are denoted as experiential data and distributional data. Experiential data are derived by way of experience with the physical world and comprise the sensory-motor data obtained through sense receptors. Distributional data, by contrast, describe the statistical distribution of words across spoken and written language. The authors claim that experiential and distributional data represent distinct data types and that each is a nontrivial source of semantic information. Their theoretical proposal is that human semantic representations are derived from an optimal statistical combination of these 2 data types. Using a Bayesian probabilistic model, they demonstrate how word meanings can be learned by treating experiential and distributional data as a single joint distribution and learning the statistical structure that underlies it. The semantic representations that are learned in this manner are measurably more realistic—as verified by comparison to a set of human-based measures of semantic representation—than those available from either data type individually or from both sources independently. This is not a result of merely using quantitatively more data, but rather it is because experiential and distributional data are qualitatively distinct, yet intercorrelated, types of data. The semantic representations that are learned are based on statistical structures that exist both within and between the experiential and distributional data types.

How have connectionist models informed the study of development? This paper considers three contributions from specific models. First, connectionist models have proven useful for exploring nonlinear dynamics and emergent properties, and their role in nonlinear developmental trajectories, critical periods and developmental disorders. Second, connectionist models have informed the study of the representations that lead to behavioral dissociations. Third, connectionist models have provided insight into neural mechanisms, and why different brain regions are specialized for different functions. Connectionist and dynamic systems approaches to development have differed, with connectionist approaches focused on learning processes and representations in cognitive tasks, and dynamic systems approaches focused on mathematical characterizations of physical elements of the system and their interactions with the environment. The two approaches also share much in common, such as their emphasis on continuous, nonlinear processes and their broad application to a range of behaviors.

The ability to perform induction appears early; however, underlying mechanisms remain unclear. Some argue that early induction is category based, whereas others suggest that early induction is similarity based. Categoryand similarity-based induction should result in different memory traces and thus in different memory accuracy. Performing induction resulted in low memory accuracy in adults and 11-year-olds, whereas 5-, and 7-year-olds were highly accurate (Experiment 1). After training to perform category-based induction, 5- and 7-year-olds exhibited patterns of accuracy similar to those of adults (Experiment 2). Furthermore, 7-year-olds, but not 5-year-olds, retained this training over time (Experiment 3). With novel categories, even adults performed similarity-based induction, exhibiting high memory accuracy (Experiment 4). These results suggest a gradual transition from similarity- to category-based induction with familiar categories. The ability to generalize from the known to the unknown is crucial for learning new information: On learning that polar bears use dopamine as a neurotransmitter, one could generalize this information to brown bears, black bears, giant pandas, and possibly to other mammals. Although it has been amply demonstrated that even infants and very young children are capable of simple inductive generalizations

What is available to developmental programs in autonomous mental development, and what should be learned at the very early stages of mental development? Our observation is that sensory and motor primitives are the most basic components present at the beginning, and what developmental agents need to learn from these resources is what their internal sensory states stand for. In this paper, we investigate the question in the context of a simple biologically motivated visuomotor agent. We observe and acknowledge, as many other researchers do, that action plays a key role in providing content to the sensory state. We propose a simple, yet powerful learning criterion, that of invariance, where invariance simply means that the internal state does not change over time. We show that after reinforcement learning based on the invariance criterion, the property of action sequence based on an internal sensory state accurately reflects the property of the stimulus that triggered that internal state. That way, the meaning of the internal sensory state can be firmly grounded on the property of that particular action sequence. We expect the framing of the problem and the proposed solution presented in this paper to help shed new light on autonomous understanding in developmental agents such as humanoid robots.

I hereby declare that this thesis is entirely my own work and that it has not been submitted as an

Abstract not found

This article is a response to E. Heit and B. K. Hayes’s (2005) comment on the target article “Induction

As the papers in this issue attest, U-shaped curves in development have stimulated a wide spectrum of research across disparate task domains and age groups and have provoked a variety of ideas about their origins and theoretical significance. In our view, the ubiquity of the general pattern suggests that U-shaped curves can arise from multiple factors, and that the various viewpoints represented herein may be useful for explaining some aspects of developmental change. In this spirit, we offer an additional way of thinking about such phenomena. Specifically, we suggest that U-shaped curves can arise within a domain-general learning mechanism as it slowly masters a domain characterized by statistical regularities and exceptions. This idea differs from those considered thus far, and may encompass many of the phenomena addressed by other views, three of which we outline briefly here. U-shaped curves indicate a transition from "associative " to "rule-based " behavior Early in language acquisition, young children often master unusual but very frequent constructions (e.g., past-tense forms such as went and fell), only to reverse this accomplishment later, producing regularized but incorrect forms (such as goed and falled) (Ervin, 1964). Such phenomena are cited in the challenge to so-called “associative learning ” theories of language

Interest in the nature of conceptual knowledge extends back at least to the ancient Greek philosophers. In recent years, there has been a wide range of different approaches to understanding the nature of conceptual knowledge, its development, and its neural basis. In most other work, however, these issues are not all treated together. Instead, workers in philosophy, adult experimental psychology, child development, and cognitive neuroscience have pursued related questions in relative ignorance of each other's efforts. Even within cognitive neuroscience, there has been until recently a relative separation between approaches taken by neuropsychologists, who study the effects of brain disease on cognition in patients, and researchers who study the neural basis of conceptual knowledge in neurologically intact populations, using functional imaging and related methods.