Mechanisms of developmental change in infant categorization

Infant color perception: Insight into perceptual development

A remarkable amount of perceptual development occurs in the first year after birth. In this article, we spotlight the case of color perception. We outline how within just 6 months, infants go from very limited detection of color as newborns to a more sophisticated perception of color that enables them to make sense of objects and the world around them. We summarize the evidence that by 6 months, infants can perceive the dimensions of color and categorize it, and have at least rudimentary mechanisms to keep color perceptually constant despite variation in illumination. In addition, infants’ sensitivity to color relates to statistical regularities of color in natural scenes. We illustrate the contribution of these findings to understanding the development of perceptual skills such as discrimination, categorization, and constancy. We also discuss the relevance of the findings for broader questions about perceptual development and identify directions for research.

Infants Learn to Follow Gaze in Stages: Evidence Confirming a Robotic Prediction

Gaze following is an early-emerging skill in infancy argued to be fundamental to joint attention and later language development. However, how gaze following emerges is a topic of great debate. Representational theories assume that in order to follow adults’ gaze, infants must have a rich sensitivity to adults’ communicative intention from birth. In contrast, learning-based theories hold that infants may learn to gaze follow based on low-level social reinforcement, without the need to understand others’ mental states. Nagai et al. (2006) successfully taught a robot to gaze follow through social reinforcement and found that the robot learned in stages: first in the horizontal plane, and later in the vertical plane—a prediction that does not follow from representational theories. In the current study, we tested this prediction in an eye-tracking paradigm. Six-month-olds did not follow gaze in either the horizontal or vertical plane, whereas 12-month-olds and 18-month-olds only followed gaze in the horizontal plane. These results confirm the core prediction of the robot model, suggesting that children may also learn to gaze follow through social reinforcement coupled with a structured learning environment.

Infant categorization as a dynamic process linked to memory

Recency effects are well documented in the adult and infant literature: recognition and recall memory are better for recently occurring events. We explore recency effects in infant categorization, which does not merely involve memory for individual items, but the formation of abstract category representations. We present a computational model of infant categorization that simulates category learning in 10-month-olds. The model predicts that recency effects outweigh previously reported order effects for the same stimuli. According to the model, infant behaviour at test should depend mainly on the identity of the most recent training item. We evaluate these predictions in a series of experiments with 10-month-old infants. Our results show that infant behaviour confirms the model's prediction. In particular, at test infants exhibited a preference for a category outlier over the category average only if the final training item had been close to the average, rather than distant from it. Our results are consistent with a view of categorization as a highly dynamic process where the end result of category learning is not the overall average of all stimuli encountered, but rather a fluid representation that moves depending on moment-to-moment novelty. We argue that this is a desirable property of a flexible cognitive system that adapts rapidly to different contexts.

Curiosity-based learning in infants: a neurocomputational approach

Infants are curious learners who drive their own cognitive development by imposing structure on their learning environment as they explore. Understanding the mechanisms by which infants structure their own learning is therefore critical to our understanding of development. Here we propose an explicit mechanism for intrinsically motivated information selection that maximizes learning. We first present a neurocomputational model of infant visual category learning, capturing existing empirical data on the role of environmental complexity on learning. Next we "set the model free", allowing it to select its own stimuli based on a formalization of curiosity and three alternative selection mechanisms. We demonstrate that maximal learning emerges when the model is able to maximize stimulus novelty relative to its internal states, depending on the interaction across learning between the structure of the environment and the plasticity in the learner itself. We discuss the implications of this new curiosity mechanism for both existing computational models of reinforcement learning and for our understanding of this fundamental mechanism in early development.

From perceptual to language-mediated categorization

From at least two months onwards, infants can form perceptual categories. During the first year of life, object knowledge develops from the ability to represent individual object features to representing correlations between attributes and to integrate information from different sources. At the end of the first year, these representations are shaped by labels, opening the way to conceptual knowledge. Here, we review the development of object knowledge and object categorization over the first year of life. We then present an artificial neural network model that models the transition from early perceptual categorization to categories mediated by labels. The model informs a current debate on the role of labels in object categorization by suggesting that although labels do not act as object features they nevertheless affect perceived similarity of perceptually distinct objects sharing the same label. The model presents the first step of an integrated account from early perceptual categorization to language-based concept learning.

Categorization of two-dimensional and three-dimensional stimuli by 18-month-old infants

In two experiments, 18-month-old infants' categorization of 3D replicas and 2D photographs of the same animals and vehicles were compared to explore infants' flexibility in categorization across different object representations. Using a sequential touching procedure, infants completed one superordinate and two basic-level categorization tasks with 3D replicas, 2D cut out photographs, or 2D images on photo cubes ("2D cubes"). For superordinate sets, 3D replicas elicited longer mean run lengths than 2D cut outs, and 3D replicas elicited equivalent mean run lengths as 2D cubes. For basic-level sets, infants categorized high-contrast animal sets when presented with 3D replicas, but they failed to categorize any of the 2D photograph sets. Categorization processes appear to differ for 3D and 2D stimuli, and infants' discovery of object properties over time while manipulating objects may facilitate categorization, as least at the superordinate level. These findings are discussed in the context of infants' representation abilities and the integration of perception and action.