<html><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><h3 dir="ltr" xmlns="http://www.w3.org/1999/xhtml" id="sites-page-title-header" style="" align="left"><div dir="ltr"><div><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">Interpretable Decoding of Higher Cognitive States from Neural Data</span></font></div><div><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;"><br></span></font></div><div><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">NIPS 2011 Workshop, Dec 16 or 17, 2011, Granada, Spain</span></font></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;"><br></span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">(Please feel free to distribute the CFP to all the interested persons and groups.)</span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;"><br></span></font></span></div><div><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">Overview</span></font></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;"><br></span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">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.</span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;"><br></span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">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; Siminova 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.</span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;"><br></span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">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.</span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;"><br></span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;"><div><b>Topics of interest</b></div><div><br></div><div>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:</div><div><br></div><div>- Interpreting spatial and temporal location of selected features and their weights</div><div>- Discovering "hidden" or "latent" cognitive representations</div><div>- Disentangling confounded processes and representations</div><div>- Comparing
or combining data from recording modalities (e.g. fMRI, EEG, structural
MRI, DTI, MEG, NIRS, EcOG, single cell recordings)</div><div>- Fuzzy and partial classifications</div><div>- Unaligned or incommensurate feature spaces and data representation</div><div><br></div><div>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:</div><div><br></div><div>- Knowledge representation and concepts</div><div>- Language and communication</div><div>- Understanding visual and auditory experience</div><div>- Memory and learning</div><div>- Reasoning and problem solving</div><div>- Decision making and executive control</div><div><br></div></span></font></span></div><div><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">Submissions</span></font></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;"><br></span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">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
<a href="https://cmt.research.microsoft.com/NIPS2011/Default.aspx">https://cmt.research.microsoft.com/NIPS2011/Default.aspx</a>. 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
<a href="http://nips.cc/PaperInformation/AuthorSubmissionInstructions">http://nips.cc/PaperInformation/AuthorSubmissionInstructions</a>. The
stylefiles are available at <a href="http://nips.cc/PaperInformation/StyleFiles">http://nips.cc/PaperInformation/StyleFiles</a>.
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.</span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;"><br></span></font></span></div><div><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">Important Dates</span></font></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;"><br></span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">- Aug 30, 2011: Call for papers</span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">- Sep 23, 2011: Deadline for submission of workshop papers</span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">- Oct 15, 2011: Notification of acceptance</span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">- Oct 31, 2011: Camera-ready papers due</span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">- Dec 16 or 17, 2011: Workshop date</span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;"><br></span></font></span></div><div><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">Links</span></font></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;"><br></span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">- NIPS 2011 website: <a href="http://nips.cc/Conferences/2011/">http://nips.cc/Conferences/2011/</a></span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">- Workshop website: <a href="https://sites.google.com/site/decodehighcogstate">https://sites.google.com/site/decodehighcogstate</a></span></font></span></div><div><span class="Apple-style-span" style="font-weight: normal;"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px;">- Call for Papers: <a href="https://sites.google.com/site/decodehighcogstate/cfp/">https://sites.google.com/site/decodehighcogstate/cfp/</a></span></font></span></div><div><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px; font-weight: normal;"><br></span></font></div><div><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px; font-weight: normal;">Kind regards,</span></font></div><div><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px; font-weight: normal;">The Workshop Organizers,</span></font></div><div><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-size: 12px; font-weight: normal;">Kai-min Kevin Chang, Anna Korhonen, Irina Simanova</span></font><span class="Apple-style-span" style="font-weight: normal; font-size: 12px; ">, Brian Murphy</span><span class="Apple-style-span" style="font-weight: normal; font-size: 12px; "> </span></div></div></h3>
<div id="sites-canvas-main" class="sites-canvas-main">
<div id="sites-canvas-main-content"></div></div></body></html>