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Full-Text Articles in Psychology
Newborn Infants Learn During Sleep, William P. Fifer, Dana L. Byrd, Michelle Kaku, Inge-Marie Eigsti, Joseph R. Isler, Jillian Grose-Fifer, Amanda R. Tarullo, Peter D. Balsam
Newborn Infants Learn During Sleep, William P. Fifer, Dana L. Byrd, Michelle Kaku, Inge-Marie Eigsti, Joseph R. Isler, Jillian Grose-Fifer, Amanda R. Tarullo, Peter D. Balsam
Publications and Research
Newborn infants must rapidly adjust their physiology and behavior to the specific demands of the novel postnatal environment. This adaptation depends, at least in part, on the infant’s ability to learn from experiences. We report here that infants exhibit learning even while asleep. Bioelectrical activity from face and scalp electrodes was recorded from neonates during an eye movement conditioning procedure in which a tone was followed by a puff of air to the eye. Sleeping newborns rapidly learned the predictive relationship between the tone and the puff. Additionally, in the latter part of training, these infants exhibited a frontally maximum …
Children's Tolerance Of Word-Form Variation, Paul Reeves Breuning
Children's Tolerance Of Word-Form Variation, Paul Reeves Breuning
Dissertations, Theses, and Capstone Projects
This study compared children's (N=96, mean age 4;1, range 2;8-5;3) and adults' (N=96, mean age 21 years) tolerance of word-onset modifications (e.g., wabbit and warabbit) and pseudo affixes (e.g., kocat and catko) in a label extension task. Trials comprised an introductory phase where children saw a picture of an animal and were told its name, and a test phase where they were shown the same picture along with one of a different animal. For `similar-name' trials, participants heard a word-form modification of the previously introduced name (e.g., introduced to a dib, they were asked, `which animal is a wib?'). For …
Semi-Supervised Learning For Connectionist Networks, Rebecca Robare
Semi-Supervised Learning For Connectionist Networks, Rebecca Robare
Dissertations, Theses, and Capstone Projects
At the computational level, language is often assumed to require both supervised and unsupervised learning. Although we have a certain understanding of these computational processes both biologically and behaviorally, our understanding of the environmental conditions under which language learning takes place falls short. I examine the semi-supervised learning paradigm as the most accurate computational description of the environmental conditions of lexical acquisition during language development. This paradigm is assessed for task learning and generalization and I argue that its real ecological validity and occasional improvements in performance over supervised learning make it an ideal candidate for modeling of language acquisition …