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Language acquisition
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Neuroemergentism, (NM) is a novel framework which has sought to consider language development as involving the organization and reorganization of cognition and its underlying neural substrate. Work to support this framework comes from studies of language and cognitive development. In this talk, I will focus on two separate levels, the sensorimotor plasticity needed to adjust to new input and the cognitive flexibility needed to select between these competing sources of information. This talk will discuss both these levels with regard to the neurocognitive adaptations seen in bilinguals. This will include structural brain differences in monolinguals and bilinguals that vary in the age of second language acquisition. In the second part, of the talk work that has focused on the cognitive flexibility will be presented. This will focus on the adaptations of the basal ganglia and frontostriatal tracts as a gating mechanism crucial for selecting the correct motor response. This includes newer work which links genes associated with dopamine to cognitive and language flexibility in bilinguals. The ways in which sensorimotor plasticity and cognitive flexibility represent accurate but incomplete conceptualizations of the competitive processes involved in language and cognitive processing will be discussed. The talk will conclude with potential future directions using an NM framework.
Even date: 15/03/2024
Speaker: Prof. Arturo E. HERNANDEZ (University of Houston)
Hosted by: Faculty of Humanities
- Subjects:
- Language and Languages
- Keywords:
- Language acquisition Code switching (Linguistics) Bilingualism Psycholinguistics
- Resource Type:
- Video
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Video
Psychology, Computer Science and Neuroscience have a history of shared questions and inter-related advances. Recently, new technology has enabled those fields to move from “toy” small-scale approaches to the study of language learning from raw sensory input and to do so at a large scale that constitutes daily life. The three primary goals of my research are 1) to quantify the statistical regularities in the real world, 2) to examine the underlying computational mechanisms operated on the statistical data, and 3) to apply the findings from basic science to real-world applications. In this talk, I will present several projects in my research lab to show that the advances in human learning and machine learning fields place us at the tipping point for powerful and consequential new insights into mechanisms of (and algorithms for) learning.
Event Date: 28/06/2023
Speaker: Prof. Chen YU (University of Texas at Austin)
Hosted by: Faculty of Humanities
- Subjects:
- Language and Languages
- Keywords:
- Machine learning Language acquisition Computational linguistics
- Resource Type:
- Video