This $577,409 Project Grant from the National Science Foundation's Social, Behavioral, and Economic Sciences program will fund research at American University to study infant visual object recognition and its implications for computational models of human vision. Over a three-year period from September 2022 to August 2025, researchers will use electroencephalography to analyze how 12-15 month old infants represent and process visual objects. Results will be compared to predictions from...
This federal Project Grant award, provided by the National Institute of Child Health and Human Development (NICHD) under the Child Health and Human Development Extramural Research program (CFDA 93.865), supports research to investigate how infants learn and recognize words despite variability in how those words are pronounced in natural speech. The $192,056 award, running from June 1, 2024 to December 31, 2025, will enable researchers at the University of Pennsylvania to conduct studies...
This federal Project Grant award, valued at $183,763.00 and awarded on September 30, 2024 by the National Institute of Child Health and Human Development (NICHD), supports research on "The Role of Environmental Context in Early Semantic Network Development." The award is funded under the NICHD's Child Health and Human Development Extramural Research program (CFDA 93.865), which focuses on fundamental biological mechanisms, clinical interventions, and translational research across...
This Project Grant award from the National Institute of Child Health and Human Development (NICHD), under the Child Health and Human Development Extramural Research program (CFDA 93.865), supports research on the role of affective information in early word learning. The $146,816 award to The Leland Stanford Junior University, spanning December 2024 to August 2027, aims to characterize how the affective context of caregiver-child interactions, including the impact of caregiver depression,...
This $132,867 federal Project Grant award from the National Institute of Child Health and Human Development (NICHD), under the Child Health and Human Development Extramural Research program (CFDA 93.865), supports research to better understand how individual differences in statistical learning impact the transfer of information from spoken language to written text during early reading development. The principal investigator at Yale University will use electroencephalography (EEG) to identify...
This National Science Foundation (NSF) Project Grant, under the Social, Behavioral, and Economic Sciences (CFDA 47.075) program, awards $213,467 to the University of Pennsylvania to study how infants begin learning language. The project combines observational methods and computer modeling to determine which aspects of speech are most important in helping infants learn language. The research will create a large, annotated database of child-directed speech that can be shared with other researchers...
This National Science Foundation project grant of $550,000 supports research to evaluate computational models of infant language acquisition against experimental data on English- and Spanish-learning infants. Funded under the Social, Behavioral, and Economic Sciences program (CFDA 47.075), the award will enable researchers at the University of California, Los Angeles to directly compare the output of different computational word segmentation models to infant behavioral data on the acquisition of...
This Project Grant award from the National Institute of Child Health and Human Development (NICHD), under the Child Health and Human Development Extramural Research program (CFDA 93.865), provides $542,304 to the University of California, Davis to conduct research on the neural mechanisms underlying word retention and vocabulary development in toddlers. The research project aims to elucidate how the hippocampus, anterior medial temporal lobe, and medial prefrontal cortex support young children's...
This National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) Project Grant of $204,593 awarded to The MGH Institute Of Health Professions, Inc. in Boston, MA supports the development of a "challenge suite" - a comprehensive dataset and platform to systematically evaluate and compare different mathematical theories of how humans learn language through unsupervised pattern detection. The key products to be delivered include: 1) A meta-analysis...
The federal Project Grant award R21HD115222 for $241,500.00 was provided by the National Institute of Child Health and Human Development (NICHD) under the Child Health and Human Development Extramural Research program (CFDA 93.865). The grant supports research to investigate how speech from young children may influence language development in infants, with the overall objective of understanding why language development differs for later-born children compared to first-born children. The key...
This federal Project Grant award of $493,505.49 by the National Institute of Child Health and Human Development (NICHD), under the Child Health and Human Development Extramural Research program (CFDA 93.865), will support research into how the visual experiences of young children interact with statistical learning mechanisms to facilitate early word learning and category formation.
The project aims to: 1) determine how variability in children's visual experiences relates to early word learning outcomes; 2) evaluate how well current deep neural network models and infants learn from diverse sets of realistic visual inputs; and 3) adapt an existing deep neural network for infant categorization to identify categories in the infant view and make generalizations. The findings and computational model will provide insight into the visual experiences relevant for learning the categories that words refer to, which is essential for improving interventions for children struggling with language development. The award will facilitate the principal investigator's transition to becoming an independent investigator at the forefront of cognitive development, vision science, and machine learning.