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:

• Transformation:

  Variables or representation change over time and/or across brain functional regions. The focus is motor-to-sensory transformation and its functionality on(speech and language) behavior, which includes its putative computational function in the following research topics:

  • Speech production and control: The high computational demand of speech (e.g., producing 4-8 syllable per second) needs an efficient way to provide control. I have proposed a motor-to-sensory transformation in a sequential manner for speech control (fig. 1), and a series of studies have been carried out to test the proposed model. Many aspects and questions still need to be addressed. For example, whatare the computational roles of motor-to-sensory transformation in speech control? And what are the neural mechanisms and implementation?
  • Perception: One theory that links motor and perception is the motor theory of speech perception and language comprehension. Numerous studies have observed activity in motorregions while listening to speech or reading words (verbs) and sentences. Butthe function of motor activation during perception is still debated. Canmotor-to-sensory transformation offer a mechanistic account for these data and explain why motor regions are engaged during perception? Ultimately, can motor-to-sensory transformation address the invariance problem in speechperception?
  • Learning: Second language (L2) and bilingualism are our focus. Learning, especially for speech essentially creates a perception-action mapping – which requires the reproduction of sound patterns with certain meanings. So, do L2 learningrecruit motor-to-sensory transformation? How do the representational changes between languages engage the motor-to-sensory transformation in bilinguals?
  • Memory, mental imagery,and other higher order cognitive function: Bothmotor-to-sensory transformation and memory retrieval can induce internal neuralrepresentation without external stimulation (fig. 2). How do they differ? Whatare their functional specificity, especially in speech and language? Moreover, how do the internally induced neural representation link to our subjective feeling of imagination?
  • For special populations: Can motor-to-sensory transformation be the underlying mechanism for the positive symptoms in special populations, such as those with speech and language deficits (e.g., stuttering, dyslexia), and those with mental and neural disorders (e.g., auditory hallucination in schizophrenia, also see point 6 below)?
  • Agency and self-monitoring: How can we have body ownership (knowing our body is ours and under our control, incontrast to cases like rubber hand illusion and phantom limb sensation), and identify incoming stimulation as generated by ourselves (e.g. speech feedback is produced by me, in contrast to cases like auditory hallucination)? Can motor-sensory transformation in combination with perception provide us agency andself-monitoring function? (See section II. Integration for more details.)

• Integration:

  Combining information across time, locations, or modalities, usually creates new representations. My research focus in this direction is on temporal integration(mostly with my collaborators) and multi-modal integration:

  • Temporal integration (collaborating with Nai Ding, Xiangbin Teng, and David Poeppel): Similar kinds of information are summarize over time (e.g., sounds, written words, and their following linguistic processes). One theory proposed that neural oscillations may serve as an integrative function during speech perception (may come with a figure like Jinbiao’s reading experiment – Yi3 Yi4 Dai4 Lao2, in Nai’s formatwith additional acoustic waveforms). How do the neural oscillations mediate the integrative process, as well as speech perception and language comprehension? (How do the proposed neural oscillations relate to memory 2013 curr bio and 2015 sci report?) [integration and process of before and after] e.g.(retrieval) integration and comparison, as well as later evaluation process(N400) ?

  • Multi-modal integration: Informationfrom different sources are combined together. The common kind of multi-modalintegration is multisensory integration (e.g., visual-auditory integration – looking at a person’s face while listening to speech). I am particularly interested in how motor and other cognitive functions integrate, and thefunction of such integration. This multi-modal integration differs from thecommon multisensory integration in that it does not require information coming from external sources. Instead, the information can be induced internally, suchas via motor-to-sensory transformation. For example,

    ​ 1) how does the motor-to-sensory transformation integrate with external feedback for speech control? What would the computation be when they are (temporally, spectrally, or spatially) inconsistent?

    ​ 2) How are the senses of agency and function of self-monitoring obtained by integrating motor-to-sensory transformation andsensory feedback?

    ​ 3) Can motor-to-sensory transformation integrate with other information to create new (episodic and semantic) memory?

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.

Publications

Teng, X., Tian, X., & Poeppel, D.(2016). Testing multi-scale processing in the auditory system. Scientific Reports, 6, 34390. doi:10.1038/srep34390

Ding, N., Melloni, L., Tian, X., &Poeppel, D. (2016). Rule-based and word-level statistics-based processing of language: insights from neuroscience. Language,Cognition and Neuroscience, 1-6. doi: 10.1080/23273798.2016.1215477

Tian, X., Zarate, J.M., Poeppel, D.(2016). Mental imagery of speech implicates two mechanisms of perceptual reactivation. Cortex. 77, 1-12. doi: 10.1016/j.cortex.2016.01.002

Ding, N., Melloni, L., Zhang, H., Tian,X., Poeppel, D. (2016). Cortical tracking of hierarchical linguistic structure in connected speech. Nature Neuroscience.19(1), 158-164.

Zarate, J.M., Tian, X., Woods, K.J.P.,& Poeppel, D. (2015). Multiple levels of linguistic and paralinguistic features contribute to voice recognition. Scientific Reports. 5: 11475. doi: 10.1038/srep11475 (*equal contribution)

Tian, X., & Poeppel, D. (2015). Dynamics of self-monitoring and error detection in speech production: evidence from mental imagery MEG. Journal of Cognitive Neuroscience. 27(2), 352-364.

Tian, X., & Poeppel, D. (2013). The effect of imagination on stimulation: the functional specificity of efference copies in speech processing. Journal of Cognitive Neuroscience. 25(7), 1020-1036.

Luo, H., Tian, X., Song, K., Zhou, K., & Poeppel, D. (2013). Neural response phase tracks how listeners learn new acoustic representations. Current Biology. 23(11), 968–974.(*equal contribution)

Tian, X., & Huber, D.E. (2013). Playing ‘Duck Duck Goose’ with neurons: Change detection through connectivity reduction. Psychological Science.24(6), 819–827.

Tian, X., & Poeppel, D. (2012). Mental imagery of speech: linking motor and sensory systems through internals imulation. Front. Hum. Neurosci. 6:314. doi: 10.3389/fnhum.2012.00314

Wu, J., Duan, H., Tian, X., Wang, P.,& Zhang, K. (2012). The effects of visual imagery on face identification: an ERP study. Front. Hum. Neurosci. 6:305. doi: 10.3389/fnhum.2012.00305

Davelaar, E.J., Tian, X., Weidemann, C.T., & Huber, D.E. (2011). A habituation account of change detection insame/different judgments. Cognitive,Affective and Behavioral Neuroscience, 11,608-626.

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

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.

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

Huber, D. E., Tian, X., Curran, T., O’Reilly, C, & Woroch, B. (2008). The dynamics of integration and separation: ERP, MEG, and neural network studies of immediate repetition effects. Journal of Experimental Psychology: Human Perception and Performance, 34,1389-1416.

Open Positions

• Postdoctoral Fellows

  Multiple postdoctoral positions are available to investigate the neural mechanisms of speech production and perception, with a focus of sensorimotor integration and its contribution to representation and computation in speech, language, memory and other higher order cognition. Information on applying (http://shanghai.nyu.edu/about/work/fellowships). Job inquires can be sent to xing.tian@nyu.edu

• Prospective Graduate Students

  Students interested in cognitive neuroscience have two ways to join our lab, either via NYU Neuroscience Doctoral Program Shanghai Track or ECNU Brain and Cognitive Science Graduate Program Track. Detailed information can be found here (http://neuro.shanghai.nyu.edu/graduate). Prospective students are encouraged to contact me via xing.tian@nyu.edu

• Undergraduate Research Opportunities

  Our lab is open to motivated undergraduates interested in conducting independent research on human cognitive functions, including speech, language, memory and other higher order cognitive function. Please send your CV, description of your research interests, and a copy of unofficial transcripts to 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

• Pudong Campus: 1555 Century Avenue, Room 1138│ 世纪大道1555号1138室 Shanghai, China 200122 Office: +86 (21) 20595201

• Puxi ECNU Campus: 3663 Zhongshan Road North, Geographybuilding, Room 140 | 华东师范大学中山北路3663号地理楼140室 Shanghai, China 200062 Office: +86(21)20595996