Date of Award
Master of Arts
Daniela Corbetta, Aaron Buss
Eight-month-old monolingual English learning infants are able to use co-occurrence statistics to find words in continuous artificial (e.g., Saffran, Aslin, & Newport, 1996) and natural languages (Pelucchi, Hay, & Saffran, 2009). Although these findings have been replicated numerous times, we still know very little about how these newly extracted words are represented. For example, if infants use TP information to segment a word with a trochaic (strong/weak) stress pattern in speech, will they recognize the same newly encountered word if it is presented with an iambic (weak/strong) stress pattern? Building on work by Pelucchi et al. (2009), infants were familiarized with Italian sentences that had two embedded high transitional probability (HTP; TP=1.0) trochaic target words (e.g., FUga & MElo) – their syllables never occurred anywhere else in the corpus. Following familiarization, infants were tested using the head-turn preference procedure on their ability to discriminate HTP words from two novel words (e.g., PAne & TEma) that had never occurred in the corpus. In a counterbalanced language the HTP and novel words were switched. In Control condition, the trochaic stress pattern of the target words was consistent across familiarization and test, while in the Experimental condition, the stress pattern of the words was changed between familiarization and test, such that if the HTP words in the corpus were trochaic (e.g., FUga and MElo), infants were tested on their ability to discriminate the iambic version of the target words (e.g. fuGA & meLO) from novel iambic words (e.g., paNE & teMA). Across conditions infants listened significantly longer to HTP words compared to Novel words, suggesting that infants’ representation of stress pattern in newly encountered words is not robust yet. These findings suggest that segmental information may override suprasegmental information at this age.
Parvanezadeh Esfahani, Sara, "How Accurately Do Infants Represent Lexical Stress Information in Recently Segmented Words?. " Master's Thesis, University of Tennessee, 2019.