基本信息
浏览量:138
职业迁徙
个人简介
My research uses fMRI and other technologies to uncover the structure of human thought. The fMRI studies track the brain activity that occurs during a wide range of cognitive and social thought, such as language comprehension, visual thinking, problem-solving, working memory, social judgment, and multi-tasking.
One current interest is in identifying the neural basis of concept representations using fMRI in the new area of neurosemantics. In collaboration with colleagues in the School of Computer Science, we have developed experimental paradigms and machine-learning techniques (multi-voxel pattern analysis) that are being applied to the study of lexical, perceptual, and social concepts (identifying the neural signature of that object and the components of the signature). We can identify the thought of a concrete object, social interaction, and digit, and we are moving on to propositions. This is leading us to a specification of how simple thoughts are neurally coded.
A second research area examines how scientific concepts are learned. As we listen to a lecture or read a textbook, neural representations of the new knowledge are being established. We hope to specify the processes by which new concepts come to be neurally represented.
Another important area of our research is in understanding the brain functioning in autism and relating it to the social and cognitive impairments that sometimes arise in the disorder. Our work has led to a new perspective, expressed as the underconnectivity theory of autism. This work uses fMRI and high angular resolution diffusion imaging (HARDI) to relate several levels of analysis: anatomical connectivity, informational (functional) connectivity, and behavioral performance. We are also starting to apply our neurosemantics (machine learning) methods to concept representations in autism.
The findings are being used to continuously develop a comprehensive theory of how brain function is related to thought, often expressed in terms of the 4CAPS computational theory.
Areas of Expertise
Cognitive Neuroscience
One current interest is in identifying the neural basis of concept representations using fMRI in the new area of neurosemantics. In collaboration with colleagues in the School of Computer Science, we have developed experimental paradigms and machine-learning techniques (multi-voxel pattern analysis) that are being applied to the study of lexical, perceptual, and social concepts (identifying the neural signature of that object and the components of the signature). We can identify the thought of a concrete object, social interaction, and digit, and we are moving on to propositions. This is leading us to a specification of how simple thoughts are neurally coded.
A second research area examines how scientific concepts are learned. As we listen to a lecture or read a textbook, neural representations of the new knowledge are being established. We hope to specify the processes by which new concepts come to be neurally represented.
Another important area of our research is in understanding the brain functioning in autism and relating it to the social and cognitive impairments that sometimes arise in the disorder. Our work has led to a new perspective, expressed as the underconnectivity theory of autism. This work uses fMRI and high angular resolution diffusion imaging (HARDI) to relate several levels of analysis: anatomical connectivity, informational (functional) connectivity, and behavioral performance. We are also starting to apply our neurosemantics (machine learning) methods to concept representations in autism.
The findings are being used to continuously develop a comprehensive theory of how brain function is related to thought, often expressed in terms of the 4CAPS computational theory.
Areas of Expertise
Cognitive Neuroscience
研究兴趣
论文共 297 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
npj Science of Learningno. 1 (2024): 1-12
Nature Human Behaviourno. 4 (2021): 433-435
The Cambridge Handbook of Intelligence and Cognitive Neurosciencepp.448-468, (2021)
npj Science of Learningno. 1 (2021): 1-1
加载更多
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn