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[Neuroscience] Call for Papers :: NIPS 2011 Workshop on Interpretable Decoding of Higher Cognitive States from Neural Data

Kai-min Kevin Chang via neur-sci%40net.bio.net (by kaimin.chang from gmail.com)
Thu Sep 1 10:33:56 EST 2011


Dear Colleagues,

We apologize if you received this message more than once. Please feel free
to forward this message to colleagues that may be interested in our
workshop.

Sincerely,

Organizing Committee for the NIPS 2011 Workshop on Interpretable Decoding of
Higher Cognitive States from Neural Data

**************************************************************

Call for Papers :: NIPS 2011 Workshop on Interpretable Decoding of Higher
Cognitive States from Neural Data Dec 16 or 17, 2011, Granada, Spain
Overview Over recent years, machine learning methods have become a crucial
analytical tool in cognitive neuroscience (see reviews by Formisano et al.,
2008; Pereira et al., 2009). Decoding techniques have dramatically increased
the sensitivity of experiments, and so also the subtlety of cognitive
questions that can be asked. At the same time the mental phenomena being
studied are moving beyond lower-level perceptual and motor processes which
are directly grounded in external measurable realities. Decoding higher
cognition and interpreting the learned behaviour of the classifiers used
pose unique challenges, as these psychological states are complex,
fast-changing and often ill-defined. Contemporary machine learning methods
deal well with the small numbers of cases, and high numbers of co-linear
dimensions typical of neural data, and are generally optimized to maximize
classification performance, rather than to enable meaningful interpretation
of the features they learn from. And indeed recent work has succeeded to
decode psychological phenomena including visual object recognition (e.g.
Kriegeskorte et al., 2008; Connolly et al., 2011), perceptual interpretation
of sounds (Staeren et al., 2009), lexical semantics (Mitchell et al., 2008;
Simanova et al., 2010; Devereux et al., 2010; Murphy et al., 2011), decision
making during game playing (Xiang et al., 2009) and the process of mental
arithmetic (Anderson et al., 2008). But for the cognitive scientists who use
these methods, the primary question is often not "how much" but rather "how"
and "why" the patterns of neural activity identified by a machine learning
algorithm encode particular cognitive processes. The aim of this workshop is
therefore to 1) discuss the achievements and problems of the decoding of
high-level cognitive states, and 2) explore the use of machine learning
methodologies and other computational models that enable such cognitive
interpretation of neural recordings of different modalities. Advances in
this field require close collaboration between machine learning experts,
neuroscientists and cognitive scientists. Thus, this workshop is highly
interdisciplinary and will aim to attract submissions also from outside the
existing NIPS community. By stimulating discussions among experts in the
different fields, the workshop seeks to generate novel insights and new
directions for research. Topics of interest The field requires techniques
that are capable of taking advantage of spatially distributed patterns in
the brain, that are separated in space but coordinated in their activity.
Methods should also be sensitive to the fine-grained temporal patterns of
multiple processes - which may proceed in a serial fashion, overlapping or
in parallel with each-other, or in multiple passes with bidirectional
information flows. Different recording modalities have distinctive
advantages: fMRI provides very fine millimetre-level localisation in the
brain but poor temporal resolution, while EEG and MEG have millisecond
temporal resolution at the cost of spatial resolution. Ideally machine
learning methods would be able to meaningfully combine complementary
information from these different neuroimaging techniques (see e.g. De
Martino et al., 2010). Moreover, as the processes underlying higher
cognition are so complex, methods should be able to disentangle even tightly
linked and confounded subprocesses. Finally, general use algorithms that
could induce latent dimensions from neural data, and so reveal the "hidden"
psychological states, would be a dramatic advance on current
hypothesis-driven analytical paradigms. Originality of approach is
encouraged and submissions on any related methodological approach are
welcomed, such as: - Interpreting spatial and temporal location of selected
features and their weights - Discovering "hidden" or "latent" cognitive
representations - Disentangling confounded processes and representations -
Comparing or combining data from recording modalities (e.g. fMRI, EEG,
structural MRI, DTI, MEG, NIRS, EcOG, single cell recordings) - Fuzzy and
partial classifications - Unaligned or incommensurate feature spaces and
data representation As noted above, the complexity of higher cognition poses
challenges. To take language comprehension as an example, speech is received
at 3-4 words; acoustic, semantic and syntactic processing can occur in
parallel; and the form of underlying representations (sentence structures,
conceptual descriptions) remains controversial. We welcome submissions
dealing with any high-level cognitive functions that exhibit similar
complexity, for instance: - Knowledge representation and concepts - Language
and communication - Understanding visual and auditory experience - Memory
and learning - Reasoning and problem solving - Decision making and executive
control Submissions Authors are invited to submit full papers on original,
unpublished work in the topic area of this workshop via the NIPS 2011
submission site at https://cmt.research.microsoft.com/NIPS2011/Default.aspx.
Submissions should be formatted using the NIPS 2011 stylefiles, with blind
review and not exceeding 8 pages plus an extra page for references. Author
and submission information can be found at
http://nips.cc/PaperInformation/AuthorSubmissionInstructions. The stylefiles
are available at http://nips.cc/PaperInformation/StyleFiles. Each submission
will be reviewed at least by two members of the programme committee.
Accepted papers will be published in the workshop proceedings. Dual
submissions to the main NIPS 2011 conference and this workshop are allowed;
if you submit to the main session, indicate this when you submit to the
workshop. If your paper is accepted for the main session, you should
withdraw your paper from the workshop upon notification by the main session.
Important Dates - Aug 30, 2011: Call for papers - Sep 23, 2011: Deadline for
submission of workshop papers - Oct 15, 2011: Notification of acceptance -
Oct 31, 2011: Camera-ready papers due - Dec 16 or 17, 2011: Workshop date
Organisers The organizing committee are researchers who are all directly
involved in machine learning of higher cognitive states, and have previous
experience running similarly themed interdisciplinary workshops, including
the NAACL Workshop on Computational Neurolinguistics (2010), ICCS Symposium
on Neural Decoding of Higher Cognitive States (2010), the CAOS Special
Session on Computational Approaches to the Neuroscience of Concepts (2010).
- Kai-min Kevin Chang, Language Technologies Institute & Centre for
Cognitive Brain Imaging, Carnegie Mellon University - Anna Korhonen,
Computer Laboratory & Research Centre for English and Applied Linguistics,
University of Cambridge - Brian Murphy, Computation, Language and
Interaction Group, Centre for Mind/Brain Sciences, University of Trento -
Irina Simanova, Max Planck Institute for Psycholinguistics & Donders
Institute for Brain, Cognition and Behaviour, Nijmegen Invited speakers -
Elia Formisano, Universiteit Maastricht, Netherlands - Francisco Pereira,
Princeton University, USA (provisional) Programme committee The preliminary
programme comittee listing is given below, and includes leading researchers
in a range of fields covering machine learning, neuroscience and wider
cognitive sciences: - John Anderson, Carnegie Mellon University, USA - Yi
Chen, Max-Planck Institute for Human Cognitive and Brain Sciences Leipzig,
Germany - Mark Cohen, University of California Los Angeles, USA - Kevyn
Collins-Thompson, Microsoft Research, USA - Andy Connolly, Dartmouth
College, USA - Jack Gallant, University of California Berkeley, USA - Marcel
van Gerven, Radboud University Nijmegen, Netherlands - Michael Hanke,
Dartmouth College, USA - Jim Haxby, Dartmouth College, USA & University of
Trento, Italy - Tom Heskes, Radboud University Nijmegen, Netherlands - Mark
Johnson, Macquarie University, Australia - Marius Peelen, University of
Trento, Italy - Francisco Pereira, Princeton University, USA - Russ
Poldrack, University of Texas Austin, USA - Dean Pomerleau, Intel Labs
Pittsburgh, USA - Diego Sona, Fondazione Bruno Kessler, Italy References -
Anderson, J. R., Carter, C. S., Fincham, J. M., Qin,. Y., Ravizza, S. M.,
and Rosenberg-Lee, M. (2008). Using fMRI to Test Models of Complex
Cognition. Cognitive Science, 32, 1323-1348. - Connolly, A. C., Guntupalli,
J. S., Gors, J., Hanke, M., Halchenko, Y. O., Wu, Y., Abdi, H. and Haxby, J.
V. (Submitted). Representation of biological classes in the human brain. -
De Martino F., Valente G., de Borst A. W., Esposito F., Roebroeck A., Goebel
R., Formisano E. (2010). Multimodal imaging: an evaluation of univariate and
multivariate methods for simultaneous EEG/fMRI. Magn Reson Imaging. 28(8),
1104-12. - Devereux, B., Kelly, C., and Korhonen, A. (2010). Using fMRI
Activation to Conceptual Stimuli to Evaluate Methods for Extracting
Conceptual Representations from Corpora. Proceedings of the NAACL-HLT
Workshop on Computational Neurolinguistics. - Formisano E., De Martino F.,
Valente G. (2008). Multivariate analysis of fMRI time series: classification
and regression of brain responses using machine learning. Magn Reson
Imaging, 26(7), 921-34. - Kriegeskorte, N., Mur, M., Ruff, D., Kiani, R.,
Bodurka, J., Esteky, H., Tanaka, K., and Bandettini, P. (2008). Matching
categorical object representations in inferior temporal cortex of man and
monkey. Neuron, 60(6), 1126-1141. - Mitchell, T. M., Shinkareva, S. V.,
Carlson, A., Chang, K. M., Malave, V. L., Mason, R. A., and Just, M. A.
(2008). Predicting Human Brain Activity Associated with the Meanings of
Nouns. Science, 320, 1191-1195. - Murphy, B., Poesio, M., Bovolo, F.,
Bruzzone, L., Dalponte, M., and Lakany, H. (2011). EEG decoding of semantic
category reveals distributed representations for single concepts. Brain and
Language, 117, 12-22. - Pereira F., Mitchell T., Botvinick M. (2009).
Machine learning classifiers and fMRI: a tutorial overview. Neuroimage. 45(1
Suppl) S199-209. - Simanova, I., Van Gerven, M., Oostenveld, R., and
Hagoort, P. (2010). Identifying object categories from event-related EEG:
Toward decoding of conceptual representations. Plos One, 512, E14465. -
Staeren N., Renvall H., De Martino F., Goebel R., Formisano E. (2009). Sound
categories are represented as distributed patterns in the human auditory
cortex. Curr Biol, 19(6), 498-502. - Xiang, J. and Chen, J. and Zhou, H. and
Qin, Y. and Li, K. and Zhong, N. 2009: Using SVM to predict high-level
cognition from fMRI data: a case study of 4* 4 Sudoku solving. Brain
Informatics, 171-181. Links - NIPS 2011 website:
http://nips.cc/Conferences/2011/ - Workshop website:
https://sites.google.com/site/decodehighcogstate - Call for Papers:
https://sites.google.com/site/decodehighcogstate/cfp/ (Please feel free to
distribute the CFP to all the interested persons and groups.)

-- 
Kai-min Kevin Chang
http://www.cs.cmu.edu/~kkchang/


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