Professor | Hao Wang | Asia Growth Partners

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Name	Hao Wang
Language ability	English, Chinese
Province	Jiangsu

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Name

Hao Wang

Language ability

English, Chinese

Province

Jiangsu

University/Lab Affiliation and Title	Nanjing Normal University
Faculty	Psychology
Academic Background & Achievements	2012 - Bachelor's Degree: Wesleyan University 2016 - Ph.D.: University of Southern California Postdoctoral Training: University of Pennsylvania Member: Society for the Cognitive Science Member: Society for Language Development
Work Experience	2018-2022 - University of Nevada - Assistant Professor 2022-Present - Nanjing Normal University - Professor

University/Lab Affiliation and Title

Nanjing Normal University

Faculty

Psychology

Academic Background & Achievements

2012 - Bachelor's Degree: Wesleyan University
2016 - Ph.D.: University of Southern California
Postdoctoral Training: University of Pennsylvania
Member: Society for the Cognitive Science
Member: Society for Language Development

Work Experience

2018-2022 - University of Nevada - Assistant Professor
2022-Present - Nanjing Normal University - Professor

Areas of Focus	Language acquisition in infants and adults Statistical learning in language
Publications	Perceptual intake explains variability in statistical word segmentation, Wang, F. H., Luo, M., & Wang, S., 2023 Statistical word segmentation succeeds given the minimal amount of exposure, Wang, F. H., Luo, M., & Wang, S., 2023 Four‐and six‐year‐old children track a single meaning with both familiar and unfamiliar referents when the referent is clear: More evidence for propose‐but‐verify, Wang, F. H., Wang, F. H., Luo, M., & Li, N., 2023 Linguistic Priming and Learning Adjacent and Non-Adjacent Dependencies in Serial Reaction Time Tasks, Wang, F. H., & Kaiser, E., 2022 Being Suspicious of Suspicious Coincidences: The Case of Learning Subordinate Word Meanings, Wang, F. H. & Trueswell, J. C., 2022 Explicit and implicit memory representations in cross-situational word learning, Wang, F. H., 2020 Top-down grouping affects adjacent dependency learning, Wang, F. H., Zevin, J. D., Trueswell, J. C., & Mintz, T. H., 2020 Spotting Dalmatians: Children’s ability to discover subordinate-level word meanings cross-situationally, Wang, F. H., & Trueswell, J. C., 2019 Successfully learning non-adjacent dependencies in a continuous artificial language stream, Wang, F. H., Zevin, J., & Mintz, T. H., 2019 Statistical Learning of Unfamiliar Sounds as Trajectories Through a Perceptual Similarity Space, Wang, F. H., Hutton, E. A., & Zevin, J. D., 2019 The role of reference in cross-situational word learning, Wang, F. H., & Mintz, T. H., 2018 Learning non-adjacent dependencies embedded in sentences of an artificial language: When learning breaks down, Wang, F. H., & Mintz, T. H., 2018 Top-down structure influences learning of nonadjacent dependencies in an artificial language, Wang, F. H., Zevin, J. D., & Mintz, T. H., 2017 Language acquisition is model-based rather than model-free, Wang, F. H. & Mintz, T. H., 2016 Word categorization from distributional information: Frames confer more than the sum of their (Bigram) parts, Mintz, T. H., Wang, F. H., & Li, J., 2014

Areas of Focus

Language acquisition in infants and adults
Statistical learning in language

Publications

Perceptual intake explains variability in statistical word segmentation, Wang, F. H., Luo, M., & Wang, S., 2023
Statistical word segmentation succeeds given the minimal amount of exposure, Wang, F. H., Luo, M., & Wang, S., 2023
Four‐and six‐year‐old children track a single meaning with both familiar and unfamiliar referents when the referent is clear: More evidence for propose‐but‐verify, Wang, F. H., Wang, F. H., Luo, M., & Li, N., 2023
Linguistic Priming and Learning Adjacent and Non-Adjacent Dependencies in Serial Reaction Time Tasks, Wang, F. H., & Kaiser, E., 2022
Being Suspicious of Suspicious Coincidences: The Case of Learning Subordinate Word Meanings, Wang, F. H. & Trueswell, J. C., 2022
Explicit and implicit memory representations in cross-situational word learning, Wang, F. H., 2020
Top-down grouping affects adjacent dependency learning, Wang, F. H., Zevin, J. D., Trueswell, J. C., & Mintz, T. H., 2020
Spotting Dalmatians: Children’s ability to discover subordinate-level word meanings cross-situationally, Wang, F. H., & Trueswell, J. C., 2019
Successfully learning non-adjacent dependencies in a continuous artificial language stream, Wang, F. H., Zevin, J., & Mintz, T. H., 2019
Statistical Learning of Unfamiliar Sounds as Trajectories Through a Perceptual Similarity Space, Wang, F. H., Hutton, E. A., & Zevin, J. D., 2019
The role of reference in cross-situational word learning, Wang, F. H., & Mintz, T. H., 2018
Learning non-adjacent dependencies embedded in sentences of an artificial language: When learning breaks down, Wang, F. H., & Mintz, T. H., 2018
Top-down structure influences learning of nonadjacent dependencies in an artificial language, Wang, F. H., Zevin, J. D., & Mintz, T. H., 2017
Language acquisition is model-based rather than model-free, Wang, F. H. & Mintz, T. H., 2016
Word categorization from distributional information: Frames confer more than the sum of their (Bigram) parts, Mintz, T. H., Wang, F. H., & Li, J., 2014

Language Acquisition Infants Adults Statistical Learning Behavioral Experiments Eye Tracking Eeg Computational Modeling Psycholinguistics Cognitive Neuroscience

Yaqiao Lu - Tianjin Normal University

Xiaofei Dong - Zhejiang Normal University

Yixuan Ku - Sun Yat-Sen University

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