Young children's early understanding of emotion was investigated by examining their use of emotion terms such as happy, sad, mud, and cry. Five children's emotion language was examined longitudinally from the age of 2 to 5 years, and as a comparison their reference to pains via such terms as burn, sting, and hurt was also examined. In Phase 1 we confirmed and extended prior findings demonstrating that by 2 years of age terms for the basic emotions of happiness, sadness, anger, (...) and fear are commonly used by children as are terms for such related states as crying and hurting. At this early age children produce such terms to refer to self and to others, and to past and future as well as to present states. Over the years from 2 to 5 children's emotion vocabulary expands, their discussion of hypothetical emotions gets underway, and the complexity of their emotion utterances increases. In Phase 2 our analyses go beyond children's production of emotion terms to analyses of their conception of emotion. We focus especially on when children use emotion terms to refer to subjective experiential states of persons. From their earliest uses of these terms in our data children. (shrink)

How do children acquire the meaning of words? And why are words such as know harder for learners to acquire than words such as dog or jump? We suggest that the chief limiting factor in acquiring the vocabulary of natural languages consists not in overcoming conceptual difficulties with abstract word meanings but rather in mapping these meanings onto their corresponding lexical forms. This opening premise of our position, while controversial, is shared with some prior approaches. The present discussion moves forward (...) from there to a detailed proposal for how the mapping problem for the lexicon is solved, as well as a presentation of experimental findings that support this account. We describe an overlapping series of steps through which novices move in representing the lexical forms and phrase structures of the exposure language, a probabilistic multiple-cue learning process known as syntactic bootstrapping. The machinery is set in motion by word-to-world pairing, a procedure available to novices from the.. (shrink)

In this paper, we describe a model that learns semantic representations from the distributional statistics of language. This model, however, goes beyond the common bag‐of‐words paradigm, and infers semantic representations by taking into account the inherent sequential nature of linguistic data. The model we describe, which we refer to as a Hidden Markov Topics model, is a natural extension of the current state of the art in Bayesian bag‐of‐words models, that is, the Topics model of Griffiths, Steyvers, and Tenenbaum (2007), (...) preserving its strengths while extending its scope to incorporate more fine‐grained linguistic information. (shrink)