Overview

A substantial body of experimental evidence has demonstrated that labels have an impact on infant categorisation processes. Yet little is known regarding the nature of the mechanisms by which this effect is achieved. I distinguish two accounts, supervised name-based categorisation and unsupervised feature-based categorisation, and describe a neuro-computational model of infant visual categorisation, based on self-organising maps, that implements the unsupervised feature-based approach. The model successfully reproduces experiments demonstrating the impact of labelling on infant visual categorisation.
The model predicts that the impact of labels on categorisation is influenced by the perceived similarity and the sequence in which the objects are presented to infants and that the observed behaviour in infants is due to a transient form of learning that might lead to the emergence of hierarchically organised categorical structure. New evidence corroborates these predictions. I argue that early in development, say before 12-months-old, labels need not act as invitations to form categories, nor highlight the commonalities between objects, but may play a more mundane but nevertheless powerful role as additional features that are processed in a similar fashion to other features that characterise objects and object categories.