This $599,651 Project Grant award from the National Science Foundation (NSF) Division of Behavioral and Cognitive Sciences supports a research study titled "Naming Names: How, and How Early, Does Object Naming Influence Infants' Fundamental Object Representations?" at Northwestern University. The study aims to discover if 7-month-old infants can link how objects are named to how they represent those objects, as well as examine if infants' early object representations support their...
This $111,878 National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant award to New York University aims to develop a hybrid, vision-centric framework that integrates intuitive and deliberate visual processing to create more robust visual intelligence. The 5-year project, commencing on February 15, 2025, will explore techniques like visual self-supervised learning, language guidance, and generative modeling to advance parametric knowledge and...
This $213,467 federal Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports research to understand how infants begin learning language by observing their interactions with caregivers. The 3-year project combines observational studies, where infants wear eye-tracking devices while playing with parents, and computational modeling to create a large annotated database of child-directed speech. This research aims to...
This Project Grant award, valued at $283,832.00, was provided by the National Science Foundation (NSF) under the Social, Behavioral, and Economic Sciences (CFDA 47.075) program. The award will fund research by Stony Brook University to investigate how the human brain generates stable visual perception during naturalistic attention tasks, even as the eyes are constantly moving. The project will test recent AI-powered vision-language models to determine if they can replicate the human brain's...
This $1,999,112 project grant from the National Science Foundation's Engineering Directorate (NSF ENG) will fund research at The Johns Hopkins University to develop new artificial intelligence techniques inspired by neuroscience models of visual attention. Specifically, the grant aims to translate models of visual attention in mammalian brains into new deep learning algorithms that can greatly reduce the number of variables updated during machine learning. If successful, these brain-inspired...
This $215,458 federal Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, supports research to study the representation and processing of visual information in the primate visual cortex. The key goals are to: Use deep learning models and multi-neuronal recordings to systematically characterize the nonlinear tuning functions and single-cell invariances of neurons in macaque area V4, in terms of...
This Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, aims to develop algorithms and computational techniques that align deep neural networks (DNNs) with human visual perception and behavior. The $1,090,678 award to Brown University will fund research to characterize human visual strategies, identify the computational principles underlying human object recognition, and translate these insights...
This $580,000 Project Grant from the National Science Foundation Division of Information and Intelligent Systems will fund research to develop more biologically realistic computational models of the primate visual system. Awarded under the Computer and Information Science and Engineering program (CFDA 47.070), the grant supports work at the University of California, San Diego from September 15, 2022 to August 31, 2025. The research aims to incorporate key features of human vision missing from...
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 Science Foundation's Social, Behavioral, and Economic Sciences (CFDA 47.075) program provides $179,857 to the University of Maryland, College Park to conduct research on the development of non-local syntactic dependency acquisition in infants. The project aims to examine when and how infants represent abstract grammatical dependencies that can occur at a distance, which is a core property of human language syntax. The research will use behavioral...