Xing Tian

Assistant Professor of Neural and Cognitive Sciences at NYU Shanghai
Speech is the most fundamental and natural way that enables us to communicate every day. We need to constantly receive and produce sounds to effectively exchange information, which demands an effective interface between perception and production. Using electrophysiological (MEG/EEG/ECoG) and neuroimaging (fMRI) techniques with behavioral and computational approaches, we aim to investigate the function of motor sensory interaction, which is at the core of the speech perception-production control loop, as well as its relation to language, learning, memory, mental imagery, and other higher order cognitive functions. More generally, I use speech and language as a model to investigate neural computations underlying human cognition. Please refer to Research for more scientific details.

Research

Broadly speaking, my research is in the field of cognitive neuroscience, in which the major task is to investigate the neural bases of (human) cognition. It includes at least two major directions ofresearch – neural representation and neural computation. Neural representation is how information is represented in our brain; neural computation is how the neural representation is formed and manipulated. I believe there are generic kinds of neural computation, called canonical neural computation, which can be applied to information processes indifferent modalities and cognitive domains. For my specific research interest I use speech and language as a model to investigate canonical neural computation underlying human cognition. Two kinds of canonical neural computation is of particular interest:

I am also interested in cross-disciplinary research, such as between neuroscience and computer science. By collaborating with faculty in computer science (ZhengZhang and Xipeng Qiu), we are aiming to build a bridge between artificial intelligence (AI) and human intelligence in the areas of speech and language. For example, we investigate the commonality between natural language processing and neural bases of language processing, especially in the aspect of semantics. Moreover, we would like to use recurrent neural network models to test neuroscience theories and models, such as lateralization and motor-to-sensory transformation.The purpose of this endeavor is to advance both fields – borrow methods andmodels from computer science to understand our brain better, while lending neuroscience findings, and models to make AI smarter and more human-like.

People

Principal Investigator

Research Associates

Postdoctoral fellows

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Undergraduates/Interns

Alumni

Collaborators

Publications

In Press

Tian, X., Ding, N., Teng, X., Bai F., &Poeppel, D. (in press). Imagined speech influences perceived loudness of sound. Nature Human Behavior.

2017

Teng, X., Tian, X., Rowland, J.,&Poeppel, D. (2017). Concurrent temporal channels for auditory processing: Oscillatory neural entrainment reveals segregation of function at different scales. PLoS biology, 15(11), e2000812.

Teng, X., Tian, X., Doelling, K., &Poeppel, D. (2017). Theta band oscillations reflect more than entrainment: behavioral and neural evidence demonstrates an active chunking process. European Journal of Neuroscience.

2016

2015

2013

2012

2011

2010

Tian, X., & Poeppel, D. (2010). Mental imagery of speech andmovement implicates the dynamics of internal forward models. Front. Psychology, 1:166. doi: 10.3389/fpsyg.2010.00166

Tian, X., & Huber, D.E. (2010). Testing an associative account of semantic satiation. Cognitive Psychology,60, 267-290.

2008

Teaching

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Guest Lecturer

Open Positions

Resources

TopoToolbox is an open-source software for topographic analysis on the event-related electrophysiological (EEG/MEG) data based on the method proposed by Tian and Huber (2008; 2011). TopoToolbox provides a tool for researchers to directly derive robust measures of response pattern (topographic) similarity and psychological meaningful response magnitude using electromagnetic signals in sensor space. These measures are useful for testing psychological theories without anatomical descriptions. Three functions are provided in this toolbox:

  1. Angle test: testing topographic similarity between experimental conditions

  2. Projection test: normalizing individual difference against a template to measure response magnitude

  3. Angle dynamics test: assessing pattern similarity over time. This toolbox is developed by Dr. Xing Tian, Dr. David Poeppel and Dr. David E. Huber. It requires MATLAB (The Mathworks, Inc.) environments and supports various of standard data format imported from EEGLAB as well as user defined dataset. Please cite the following references if you use the TopoToolbox for publications or public releases:

    Tian, X., & Huber, D. (2008). Measures of spatial similarity and response magnitude in MEG and scalp EEG. Brain Topography, 20(3), 131-141.

    Tian, X., Poeppel, D., & Huber, D.E. (2011). TopoToolbox: Using sensor topography to calculate psychologically meaningful measures from event-related EEG/MEG. Computational Intelligence and Neuroscience, 2011. doi:10.1155/2011/674605

Should you have any questions or comments regarding this toolbox, please contact me at xing.tian@nyu.edu

Contact

Prospective students, postdoctoral researchers and collaborators are welcomed to contact us directly.

Xing Tian (田兴) E-mail: xing.tian@nyu.edu