Over the first year of life, infants gain conceptual skills which allow them to construe semantically related items as similar, even when they have few if any directly-perceived attributes in common. Moreover, this skill first encompasses only broad semantic categories, and only later extends to more subtle distinctions, when conceptual and perceptual similarity relations do not coincide. In this paper we suggest that a new mechanism must be added to the mix of possible bases for this observed developmental change. In agreement with many others, we suggest that infants’ earliest conceptual representations are organised with respect to certain especially useful or salient properties, regardless of whether such properties can be directly observed. However we suggest that in many cases this salience may itself be acquired, through domain-general learning mechanisms that are sensitive to the high-order coherent covariation of directly-observed stimulus properties across a breadth of experience. To support this argument we will describe simulations with a simple PDP model of semantic memory. When trained with backpropagation to complete queries about the properties of different objects, the model’s internal representations differentiate in a coarse-to-fine manner. As a consequence, different sets of properties come to be especially “salient” to the

Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge, and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. We present a Bayesian framework that attempts to meet both goals and describe four applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the four models are defined over different kinds of structures that capture different relationships between the categories in a domain. Our framework therefore shows how statistical inference can operate over structured background knowledge, and we argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning.

Dehaene (this volume) articulates a naturalistic approach to the cognitive foundations of mathematics. Further, he argues that the `number line' (analog magnitude) system of representation is the evolutionary and ontogenetic foundation of numerical concepts. Here I endorse Dehaene's naturalistic stance and also his characterization of analog magnitude number representations. Although analog magnitude representations are part of the evolutionary foundations of numerical concepts, I argue that they are unlikely to be part of the ontogenetic foundations of the capacity to represent natural number. Rather, the developmental source of explicit integer list representations of number are more likely to be systems such as the object--file representations that articulate mid--level object based attention, systems that build parallel representations of small sets of individuals.

the first three parts of her ar- gument. I reserve serious doubts only for the fourth, and offer a friendly amend- ment to Mandler's project. 1. Distinguishing perceptual from conceptual representations. In distin- guishing perceptual from conceptual representations, Mandler most often appeals to a difference between what entities look like and what kinds of entities they are. Additionally, she claims that conceptual representations differ from perceptual representations in being consciously accessible, supporting problem solving and inference, and being stored in long-term memory. To quibble, it is not clear that these different properties determine a single type of representation, or whether Mandler considers all of these to be properties of conceptual representations. Each may characterize at least some perceptual representations. For example, some clear cases of perceptual representations, such as a toothache or the experience of redness, may be consciously accessible. N

Naming requires recognition. Recognition requires the ability to categorize objects and events. Infants under six months of age are capable of making fine-grained discriminations of object boundaries and three-dimensional space. At 8 to 10 months, a child’s object categories are sufficiently stable and flexible to be used as the foundation for labeling and referencing actions. What mechanisms in the brain underlie the unfolding of these capacities? In this article, we describe a neural network model which attempts to simulate, in a biologically plausible way, the process by which infants learn how to recognize objects and words through exposure to visual stimuli and vocal sounds. 1

Intuitive physical reasoning about occluded objects by inexperienced chicks

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The human capacity for language is one of the most contentious topics in cognitive science. While some researchers attribute language to domain-general mechanisms, others postulate a specialized language system. When it comes to the phonological component, however, even proponents of domain-specificity concede that specialization is unlikely (Fitch et al., 2005). Phonological competence, in this view, is the product of experience, auditory perception, and motor control. And indeed, phonological systems are intimately grounded in phonetics. But while the domain-general perspective can account for this fact, it offers no explanation for several key features of language. It fails to explain why all languages—signed and spoken—have a phonological system, why phonological systems emerge spontaneously, in the absence of a model

We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for developmentalists. We emphasize a qualitative understanding of Bayesian inference, but also include information about additional resources for those interested in the cognitive science applications, mathematical foundations, or machine learning details in more depth. In addition, we discuss some important interpretation issues that often arise when evaluating Bayesian models in cognitive science.