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Kristen Syrett Published in Language Acquisition

Prof. Kristen Syrett has a new article published in Language Acquisition.

The article, “More hard words: Learning emotion and mental state adjectives from linguistic context,” is joint work with Prof. Misha Becker of UNC Chapel Hill. It was published on April 18.

Abstract: How do young children learn the meanings of adjectives that label emotions and mental states, like happy or confident? The concepts behind these words seem easy to grasp, and yet the properties they denote are abstract and lack reliable visual correlates, much as with verbs such as think or know. There is robust evidence that the linguistic context in which mental state verbs appear supports their acquisition. Using a two-pronged approach, we explore whether the same is true for these adjectives. First, we present a comprehensive study of adjectives in child-directed speech (CDS) in CHILDES corpora, revealing that these adjectives have a unique distributional profile based on the semantics of co-occurring words, and the syntactic complements with which they appear, distinguishing them from other adjective types. Second, we present a word learning study with adults using a version of the Human Simulation Paradigm manipulating key variables from the corpus study (subject animacy and syntactic complements), while holding other features (morphosyntactic form and syntactic position of the adjective) constant. Participants’ guesses demonstrate that they recruit these cues to constrain the meaning of novel adjectives to one corresponding to an emotion or mental state. By presenting a thorough comparison of adjective types across key linguistic contexts and providing evidence that such contexts constrain adjective meaning, we systematically extend syntactic bootstrapping to the adjectival domain in a way that goes well beyond previous work, and paints a vibrant picture of what supports the acquisition of adjectives.

Find the article on Language Acquisition here: https://www.tandfonline.com/doi/full/10.1080/10489223.2024.2329071