@book{Landini-MRI-book-2,
editor = "Luigi Landini and Vincenzo Positano and Maria Filomena Santarelli",
booktitle = "Advanced Image Processing in Magnetic Resonance Imaging",
year = "2005",
isbn = "0824725425",
publisher = "Dekker",
series = "Signal Processing and Communications",
}
Keyword(s): EEG,
fMRI,
categorization.
| Neuroscientific methods have become an increasingly important influence on the study of cognitive processing. In this chapter we look at how the study of patient populations in addition to neuroimaging techniques have been used to address basic questions about category knowledge. How does the brain represent category knowledge? What information is acquired during category learning? Why do people parse action streams into discrete events? In this chapter we look at how neuroscience has shaped the way we ask and answer questions about category learning and representation. There may not be agreement about the answers, but neuroscience has helped to make the questions interesting. |
@InBook{HH05a,
author = {Hanson, C. and Hanson, S. J.},
editor = {Cohen, H. & Lefebvre, C.},
title = {Handbook on Categorization in Cognitive Science},
chapter = {Knowing this from that: Brain response to objects and events},
keywords = {EEG, fMRI, categorization},
publisher = {Elsevier},
year = {2005},
note = {In Press},
url = {http://www.elsevier.com/wps/find/bookdescription.cws_home/705263/description#description},
PDF = {http://www.rumba.rutgers.edu/pubs/hanson_hanson_catchapt.pdf},
abstract = {Neuroscientific methods have become an increasingly important influence on the study of cognitive processing. In this chapter we look at how the study of patient populations in addition to neuroimaging techniques have been used to address basic questions about category knowledge. How does the brain represent category knowledge? What information is acquired during category learning? Why do people parse action streams into discrete events? In this chapter we look at how neuroscience has shaped the way we ask and answer questions about category learning and representation. There may not be agreement about the answers, but neuroscience has helped to make the questions interesting.}
} Keyword(s): EEG,
MEG,
fMRI,
MRI,
multimodal analysis,
fusion.
| This chapter provides a comprehensive survey of the motivations, assumptions and pitfalls associated with combining signals such as fMRI with EEG or MEG. Our initial focus in the chapter concerns mathematical approaches for solving the localization problem in EEG and MEG. Next we document the most recent and promising ways in which these signals can be combined with fMRI. Specifically, we look at correlative analysis, decomposition techniques, equivalent dipole fitting, distributed sources modeling, beamforming, and Bayesian methods. Due to difficulties in assessing ground truth of a combined signal in any realistic experiment---a difficulty further confounded by lack of accurate biophysical models of BOLD signal---we are cautious to be optimistic about multimodal integration. Nonetheless, as we highlight and explore the technical and methodological difficulties of fusing heterogeneous signals, it seems likely that correct fusion of multimodal data will allow previously inaccessible spatiotemporal structures to be visualized and formalized and thus eventually become a useful tool in brain imaging research. |
@inCollection{HHP05,
crossref = "Landini-MRI-book-2",
author = "Yaroslav O. Halchenko and Stephen Jos{\'e} Hanson and Barak A. Pearlmutter",
title = "Multimodal Integration: {fMRI}, {MRI}, {EEG}, and {MEG}",
pages = "223--265",
url = "http://www.rumba.rutgers.edu/",
urldate = "2005-01-10",
chapter = 8,
keywords = {EEG, MEG, fMRI, MRI, multimodal analysis, fusion},
abstract = {This chapter provides a comprehensive survey of the motivations, assumptions and pitfalls associated with combining signals such as fMRI with EEG or MEG. Our initial focus in the chapter concerns mathematical approaches for solving the localization problem in EEG and MEG. Next we document the most recent and promising ways in which these signals can be combined with fMRI. Specifically, we look at correlative analysis, decomposition techniques, equivalent dipole fitting, distributed sources modeling, beamforming, and Bayesian methods. Due to difficulties in assessing ground truth of a combined signal in any realistic experiment---a difficulty further confounded by lack of accurate biophysical models of BOLD signal---we are cautious to be optimistic about multimodal integration. Nonetheless, as we highlight and explore the technical and methodological difficulties of fusing heterogeneous signals, it seems likely that correct fusion of multimodal data will allow previously inaccessible spatiotemporal structures to be visualized and formalized and thus eventually become a useful tool in brain imaging research.},
PDF = {http://www.onerussian.com/Sci/fusion/fusion-chapter/HHP05.pdf},
urldate = {2005-10-07},
}@Article{Mat05a,
author = {Matsuka, T.},
title = {Simple, individually unique, and context dependent learning methods for models of human category learning},
journal = {Behavior Research Methods},
year = 2005
} Keyword(s): biological motion,
emotion.
@InProceedings{CMHS05,
author = {Chouchourelou, A. and Matsuka, T. and Hanson, C. and Shiffrar, M.},
title = {The perception of emotional bodies},
keywords = {biological motion, emotion},
year = {2005},
address = {New York City, NY},
booktitle = {Annual Meeting of the Cognitive Neuroscience Society},
note = {},
PDF = {http://www.rumba.rutgers.edu/pubs/CNS05talkfinal.pdf}
} @InProceedings{HH05b,
author = {Hanson, C. and Hanson, S. J.},
title = {Neural Correlates of Explicit and Implicit Processing},
booktitle = {Annual Meeting of the Organization for Human Brain Mapping},
year = 2005,
address = {Toronto, Canada},
PDF = {http://www.rumba.rutgers.edu/pubs/hbm2005_impexp_poster.pdf}
}@InProceedings{YMHH05,
author = {Yamauchi, T. and Matsuka, T. and Hanson, C. and Hanson, S. J.},
title = {Neural Correlates of Classification and Inference of Categories},
booktitle = {Human Brain Mapping},
year = 2005,
address = {Tronto}
}@Misc{Mat05b,
author = {Matsuka, T.},
title = {Modeling human learning as context dependent knowledge utility optimization},
year = 2005,
note = {under review}
}@Misc{Mat05c,
author = {Matsuka, T.},
title = {On the selective attention mechanism of prototype models of categorization},
year = 2005,
note = {under review}
} @Misc{MY05,
author = {Matsuka, T. and Yamauchi, T. and Hanson, C. and Hanson, S. J.},
title = {Representing Categorical Knowledge: An fMRI Study},
year = 2005,
note = {under review},
PDF = {http://www.rumba.rutgers.edu/pubs/cogsci05_fMRI.pdf}
}@Misc{TGC05,
author = {Tatsuoka, K. and Guerrero, A. and Corter, J. E. and Yamada, T. and Matsuka, T. and Tatsuoka, C.},
title = {International Comparions of mathematical thinking skills in the TIMSS-R},
year = 2005,
note = {under review}
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