Normal children learn tens of thousands of words, and do so quickly and efficiently, often in highly impoverished environments. In How Children Learn the Meanings of Words, I argue that word learning is the product of certain cognitive and linguistic abilities that include the ability to acquire concepts, an appreciation of syntactic cues to meaning, and a rich understanding of the mental states of other people. These capacities are powerful, early emerging, and to some extent uniquely human, but they are (...) not special to word learning. This proposal is an alternative to the view that word learning is the result of simple associative learning mechanisms, and it rejects as well the notion that children possess constraints, either innate or learned, that are specifically earmarked for word learning. This theory is extended to account for how children learn names for objects, substances, and abstract entities, pronouns and proper names, verbs, determiners, prepositions, and number words. Several related topics are also discussed, including naïve essentialism, children's understanding of representational art, the nature of numerical and spatial reasoning, and the role of words in the shaping of mental life. Key Words: cognitive development; concepts; meaning; semantics; social cognition; syntax; theory of mind; word learning. (shrink)

An ongoing debate in affective science concerns whether certain discrete, “basic” emotions have evolutionarily based signals that are easily, universally, and innatel...

Children's reasoning about biological concepts is influenced not only by their experiences in the natural world and in their classrooms, but also by the way that these concepts are named. In English, 'animal' can refer either to exclusively non-human animals, or all animate beings. In Indonesian, this category of animate beings has no dedicated name. Here, we ask whether this difference in naming has consequences for children's reasoning about humans and non-human animals. Results from English- and Indonesian-speaking children reveals differences (...) in reasoning at age 6, differences that become attenuated by age 9. These results suggest that not only naming practices, but also biologically-relevant formal and informal learning experiences, influence children's reasoning about biological concepts. (shrink)

A substantial body of experimental evidence has demonstrated that labels have an impact on infant categorization processes. Yet little is known regarding the nature of the mechanisms by which this effect is achieved. We distinguish between two competing accounts: supervised name‐based categorization and unsupervised feature‐based categorization. We describe a neurocomputational model of infant visual categorization, based on self‐organizing maps, that implements the unsupervised feature‐based approach. The model successfully reproduces experiments demonstrating the impact of labeling on infant visual categorization reported in (...) Plunkett, Hu, and Cohen (2008). It mimics infant behavior in both the familiarization and testing phases of the procedure, using a training regime that involves only single presentations of each stimulus and using just 24 participant networks per experiment. The model predicts that the observed behavior in infants is due to a transient form of learning that might lead to the emergence of hierarchically organized categorical structure and that the impact of labels on categorization is influenced by the perceived similarity and the sequence in which the objects are presented. The results suggest 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 they may play a more mundane but nevertheless powerful role as additional features that are processed in the same fashion as other features that characterize objects and object categories. (shrink)

Why are some words easier to learn than others? And what enables the eventual learning of the more difficult words? These questions were addressed for nouns using a paradigm in which adults were exposed to naturalistic maternal input that was manipulated to simulate access to several different information sources, both alone and in combination: observation of the extralinguistic contexts in which the target word was used, the words that co‐occurred with the target word, and the target word's syntactic context. Words (...) that were not accurately identified from observation alone were both abstract (e.g., music) and concrete (e.g., tail). Whether a noun could be learned from observation depended on whether it labeled a basic‐level object category (BLOC). However, the difference between BLOC labels and non‐BLOC labels was eliminated when observation was supplemented with linguistic context. Thus, although BLOC labels can be learned from observation alone, non‐BLOC labels require richer linguistic context. These findings support a model of vocabulary growth in which an important role is played by changes in the information to which learners have access. (shrink)

Prelinguistic infants must find a way to isolate meaningful chunks from the continuous streams of speech that they hear. BootLex, a new model which uses distributional cues to build a lexicon, demonstrates how much can be accomplished using this single source of information. This conceptually simple probabilistic algorithm achieves significant segmentation results on various kinds of language corpora - English, Japanese, and Spanish; child- and adult-directed speech, and written texts; and several variations in coding structure - and reveals which statistical (...) characteristics of the input have an influence on segmentation performance. BootLex is then compared, quantitatively and qualitatively, with three other groups of computational models of the same infant segmentation process, paying particular attention to functional characteristics of the models and their similarity to human cognition. Commonalities and contrasts among the models are discussed, as well as their implications both for theories of the cognitive problem of segmentation itself, and for the general enterprise of computational cognitive modeling. (shrink)