Connectionists: Call for abstracts: NIPS workshop on New Directions on Decoding Mental States from fMRI Data

[fpereira@cs.cmu.edu]

with our apologies if you receive multiple copies of this
through various mailing lists:

-------------------------------------------------------
New Directions on Decoding Mental States from fMRI Data
(http://www.cs.cmu.edu/~fmri/workshop)
-------------------------------------------------------

to be held at NIPS 06 in Whistler, Canada, December 8 or 9

Important dates:
- 2 page abstract submission deadline: November 2
- notification of acceptance: early November
- workshop date: December 8 or 9

Program Committee:
- John-Dylan Haynes (MPI for Human Cognitive and Brain Sciences,Leipzig)
- Francisco Pereira (Carnegie Mellon University)
- Tom Mitchell (Carnegie Mellon University)

Overview:

In the past five years machine learning classifiers have met great
interest in the field of cognitive neuroscience for the purpose of
decoding mental states given observed fMRI data. This work has received
considerable attention because it is seen as a way to overcome
limitations of more conventional fMRI analysis methods. Whereas conventional
fMRI research is focused on spatially localizing
cognitive modules, decoding-based research allows for the first time the study
of the neural encoding of specific mental contents in the human brain.

The recent progress has also raised a number of fundamental questions about
the practice of using classifiers for decoding, the interpretation
of results and their implications for theories of cognitive neuroscience.
This workshop has the following goals:

(1) To give an overview of decoding mental states from fMRI
(2) To present cutting edge research and to address the fundamental practical
challenges.
(3) To provide a venue for discussion of the broader questions that may result
in an agenda for the field.

Scope:

We aim to have several cognitive neuroscience researchers give overview
talks and introduce members of the NIPS community to the field and challenges
from their perspective.

We will also leave ample space for submitted presentations and discussion.
At a high level, we are interested in how decoding can help model-building
in cognitive neuroscience and, ultimately, help develop theories of neural
representation that explain the decoding-identified structure in the fMRI
data. At a more technical level, we are considering specific issues such as:
- Can classifiers be used as a confirmatory scientific tool for existing
theories or hypotheses?
- What are the characteristics of fMRI datasets that affect current machine
learning wisdom?
- Is it feasible to use nonlinear classifiers or do linear ones suffice?
- Is regularization useful and, if so, which form is more appropriate?
- Are there feature selection strategies that work in general, or does
  success depend entirely on the activation structure of each study?
- How should having an hypothesis about the structure of activation
  influence the choices above? What other prior information can be used?
- How can decoding be done under dynamic conditions?
- Is it feasible to decode multiple superimposed mental states (in space or
time)?
- How should one perform inference using data:
    - acquired under different contextual conditions
    - from multiple subjects
    - from multiple studies
- Are there low-dimensional representations of  fMRI data that are better for
decoding?
- What activation structures can classifiers learn other than location of
activation?
- How should one attach statistical significance to decoding results or
activation  structure identified?

We especially believe fMRI decoding to be of high interest also to machine
learning experts. Given the number and type of specific open questions, this
is more than just another application domain and thus there is a requirement
for machine learning researchers to come up with new methods and creative
applications. This workshop is also designed to facilitate their entry into
this field and put them in contact with cognitive neuroscientists receptive
to their computational expertise and creativity.

Submissions:

Here we invite proposals for presentations addressing any of the questions
above or other related issues. We welcome presentations of completed work or
work-in-progress, as well as papers discussing potential research directions
and surveys of recent developments.

If you would like to present at the workshop, please send an abstract at most 2
pages long (NIPS format (http://leon.bottou.com/nips), excluding citations, PDF
preferred) to fpereira at cs.cmu.edu as soon as possible, and no later than
November 2, 2006.

We will select presentations and have a final program posted by early November.